Category Archives: Uncategorized

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Protocol of Telephony which is newer to Diameter Just As Vulnerable As SS7

Category : Uncategorized

Protocol of Telephony which is newer to Diameter Just As Vulnerable As SS7

Protocol is the special set of rules that end points in a telecommunication connection use when they communicate. Protocols specify interactions between the communicating entities.

Security researchers say the Diameter protocol used with today’s 4G (LTE) telephony and data transfer standard is vulnerable to the same types of vulnerabilities as the older SS7 standard used with older telephony standards such as 3G, 2G, and earlier.

Both Diameter and SS7 (Signaling System No. 7) have the same role in a telephony network. Their purpose is to serve as an authentication and authorization system inside a network and between different telephony networks (providers).

The difference between these two is that while SS7 did not use any type of encryption for its authentication procedures, leading to the easy forgery of authentication and authorization messages, Diameter supports TLS/DTLS (for TCP or SCTP, respectively) or IPsec.

 

Protocol

 

4G operators often misconfigure Diameter

According to research published last month by Positive Technologies detailing Diameter’s use among mobile networks across the globe, the protocol’s features are rarely used.

The incorrect use of Diameter leads to the presence of several vulnerabilities in 4G networks that resemble the ones found in older networks that use SS7.

Researchers say that the Diameter misconfigurations they’ve spotted inside 4G networks are in many cases unique per each network but they usually repeat themselves to have them organized in five classes of attacks: (1) subscriber information disclosure, (2) network information disclosure, (3) subscriber traffic interception, (4) fraud, and (5) denial of service.

 

4G operators

 

Subscriber and network information disclosure

Subscriber and network information disclosure, allow an attacker to gather operational information about the user’s device, subscriber profile, and information about the mobile network in general.

Such vulnerabilities can reveal the user’s IMSI identifier, device addresses, network configuration, or even his geographical location —helping an attacker track users of interest as they move about.

 

Subscriber

 

 

Subscriber traffic interception

The third vulnerability, subscriber traffic interception, is only theoretically possible because both SMS and call transmission often establish channels with previous-generation protocols that do not use the Diameter protocol for authentication.

Nevertheless, Positive Technologies researchers warn that if the attacker is set on SMS and call interception, he can at any time downgrade a Diameter-capable 4G connection to a previous-generation connection and use flaws in SS7 and other protocols to carry out his attack.

 

traffic

 

 

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Ransomware Nozelesn Reportedly Using Spam to Target Poland

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Ransomware Nozelesn Reportedly Using Spam to Target Poland

Ransomware is a form of malicious software (or malware) that, once it’s taken over your computer, threatens you with harm, usually by denying you access to your data. The attacker demands a ransom from the victim, promising — not always truthfully — to restore access to the data upon payment.

The motive for ransomware attacks is nearly always monetary, and unlike other types of attacks, the victim is usually notified that an exploit has occurred and is given instructions for how to recover from the attack.

A distribution campaign for a new ransomware called Nozelesn is currently underway that is targeting Poland. This campaign started July 1st and already have reported from victims in our forums and numerous cases have been spotted on ID Ransomware.

A researcher at CERT Polska, the Computer Emergency Response Team for Poland, has also stated that they believe the ransomware is being distributed through a spam campaign pretending to be a DHL invoice.

 

Ransomware

 

 

How ransomware works

There are a number of vectors ransomware can take to access a computer. One of the most common delivery systems is phishing spam — attachments that come to the victim in an email, masquerading as a file they should trust. Once they’re downloaded and opened, they can take over the victim’s computer, especially if they have built-in social engineering tools that trick users into allowing administrative access.

 

Phising Spam

 

What happens when you are infected with the Nozelesn Ransomware

Sample of Nozelesn Ransomware has not been found yet. The information of Nozelesn Ransomware based on the reports by victims who have posted in Bleeping Computer forum.

the ransomware will encrypt a user’s files and append the .nozelesn extension to the encrypted file’s name.

The ransomware will also create ransom notes on the computer named HOW_FIX_NOZELESN_FILES.htm. This ransom note contains isntructions on how to login to a TOR payment server at lyasuvlsarvrlyxz.onion to receive instructions. It also contains a unique personal code that the victim will be need in order to login to the server.

 

encrypt

 

The Nozelesn decryption cabinet

The TOR Payment server for this ransomware is called the “Nozelesn decryption cabinet” and is located at the lyasuvlsarvrlyxz.onion address. When you first visit the site you will be required to enter the personal code from your ransom note and a captcha answer into the login screen.

Once logged in you will see payment instructions that contain the amount of bitcoins to send and the address to send them to in order pay the ransom.  Currently the ransom payment amount is set to .10 bitcoins or approximately $660 USD.

It is not known if paying the ransom will result in getting a decryption key and it is strongly advised that you do not pay the ransom. Instead try and restore from backups or Shadow Volume Copies if they are available.

Once a sample is found, it will be analyzed to determine if a victim’s files can be decrypted for free. Once again, if you need help with this ransomware, please post in our Nozelesn Ransomware Support & Help Topic.

 

TOR

 

How to protect yourself from the Nozelesn Ransomware

To protect yourself from ransomware in general, it is important that you use good computing habits and security software. The most important step is to always have a reliable and tested backup of your data that can be restored in the case of an emergency, such as a ransomware attack.

A good security software solution that incorporates behavioral detections to combat ransomware and not just use signature detections or heuristics is important as well.

For example, Emsisoft Anti-Malware and Malwarebytes Anti-Malware both contain behavioral detection that can prevent many, if not most, ransomware infections from encrypting a computer.

 

Security

 

Follow the Points for security habits which are the most important in many cases:

  • Do not open attachments if you do not know who sent them.
  • Do not open attachments until you confirm that the person actually sent you them,
  • Scan attachments with tools like VirusTotal.
  • Do not connect Remote Desktop Services directly to the Internet. Instead, make sure they can only be accessed by logging into a VPN first.
  • Make sure all Windows updates are installed as soon as they come out! Also make sure you update all programs, especially Java, Flash, and Adobe Reader. Older programs contain security vulnerabilities that are commonly exploited by malware distributors. Therefore it is important to keep them updated.
  • Make sure you use have some sort of security software installed that uses behavioral detections or white list technology. White listing can be a pain to train, but if your willing to stock with it, could have the biggest payoffs.
  • Use hard passwords and never reuse the same password at multiple sites.

 

VPN

 

 

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Vulnerability of Host Header Injection reported by ICSS Student Rabsun Sarkar

Category : Uncategorized

HTTP header injection

HTTP header injection is a general class of web application security vulnerability which occurs when Hypertext Transfer Protocol (HTTP) headers are dynamically generated based on user input. Header injection in HTTP responses can allow for HTTP response splitting, Session fixation via the Set-Cookie header, cross-site scripting (XSS), and malicious redirect attacks via the location header. HTTP header injection is a relatively new area for web-based attacks, and has primarily been pioneered by Amit Klein in his work on request/response smuggling/splitting.

Most of the common web servers are configured in the form of the same server to host many web applications with the same IP address this type of configuration is the reason for the Host Header issues. Here we are going to deal with the host header injection attack in various forms, its impact and mitigation.

 

HTTP

 

Host Header Issues:

An attacker can manually divert the code to produce the hacker desired output simply by editing the host header. Most probably web servers are configured to pass the host header to the first virtual host in the list without proper reorganisation, So It is possible to send the HTTP requests with arbitrary host headers to the first virtual host. In that case, if we specify an invalid Host means the web server process it and pass the invalid host header to the first virtual host in the list.

An attacker can modify the host name by giving a fake web page or a vulnerable website and deliver it to the user and fraud the users.

 

host header

 

How Attackers Utilize Host Header Attack

The exploitation is based on the logic of the web application. If the application does not use the user input value, then there is no risk. But the host header attack is considered as a serious issue at the time of resetting our password. When we are resetting our forgotten password, or we change our password for our privacy, the web application generates a link dynamically there it uses the host header provided in the request. In this scenario, the hacker uses this header for their evil cause. The hackers use some social engineering and phishing attacks for getting the link. So, the developer should realise the importance of the host header attack.

 

hacker

 

 

Reasons leading to Host Header Injection

Any approach in the field of web application if not implemented properly can make room for several vulnerabilities. Same goes with the implementation of the Host header. If the application relies on the value of the Host header for writing links without HTML-encoding, importing scripts, deciding the location to redirect to or even generate password resets links with its value without proper filtering, validation and sanitization then it can lead to several vulnerabilities like Cache Poisoning, Cross Site Scripting etc.

 

HTML

 

 

Conclusion

Many application developers did not realise that the HTTP host header is accessible and controlled by all user. In an application security perspective, the input given by the user is always deceivable, and it is unsafe to trust. So, a web developer should consider host header issues as a dangerous thing not to and neglect it.

 

developers

 

 

HSD Responsible Disclosure: Rabsun Sarkar Reported a Vulnerability in the Security Talent Website

HSD has a Responsible Disclosure Policy for its IT systems. Recently, Rabsun Sarkar (Certified Ethical Hacker from India) reported a vulnerability in the Security Talent website, showing the importance of having such a policy. It concerns Host Header Injection, which could cause the web application to behave in unexpected ways.

Our website developer Maaike Media quickly took action and solved the issue. We are very grateful for their expertise and professional response.

The HSD Responsible Disclosure Policy is based on the Guideline Responsible Disclosure published by the NCSC and was introduced after the HSD Café on Ethical Hacking and Responsible Disclosure during the Cyber Security Week 2015.

 

NCSC

 

 

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Brave Browser Adds Support for Private Browsing With Tor Integration

Category : Uncategorized

Brave Browser

Brave is a free and open-source pay-to-surf web browser based on the Chromium web browser and its Blink engine (with the exception of its iOS version, a Firefox for iOS fork using the WebKit engine), announced by the co-founder of the Mozilla project and creator of JavaScript, Brendan Eich.

 

Brave

 

Brave Browser Adds Support for Private Browsing With Tor Integration

Brave, a lesser known but perfectly fine browser, launched a new version today that features a private browsing mode that automatically starts inside a Tor session.

The Brave browser is infamous for its privacy-first features, and the new “Private Tabs with Tor” feature, as it’s labeled in the interface (screenshot above), fits right in with the rest of the package.

 

Browser

 

Tor integration improves Brave’s privacy-focused features

The Brave team says the new “Private Tabs with Tor” feature will be helpful for users who are looking for additional protection that goes beyond the local PC.

Private browsing sessions were invented to wipe data from the browser after a browsing session is closed. But this type of browsing is not opaque to ISPs and the websites a user accesses, which can log traffic originating from the user.

Brave’s Tor-integrated private browsing sessions anonymize the user’s IP address by passing the browser traffic through the Tor network.

ISPs and websites can’t pinpoint the origin of the traffic on the user, similarly to how they can’t pinpoint the origin of any Tor traffic.

As a thank you for integrating the Tor technology inside Brave, the Brave team also announced it would be contributing back to the Tor Project by running a couple of Tor relay servers and help keep the Tor network up and running.

 

Features

 

Brave not yet stable, but a solid product nevertheless

Brave hasn’t reached a stable 1.0 version yet and is still under development, but the browser is highly regarded in privacy circles.

The reasons are that Brave’s default configuration blocks ads, tracking scripts, and cryptocurrency mining scripts.

Furthermore, Brave’s normal private browsing session, even before of today’s addition of Tor support, was pretty privacy-focused as well.

Brave private browsing tabs do not save users’ browsing history or cookies, and they also use DuckDuckGo, a privacy-first and no-user-tracking search engine, as the browser’s default search provider.

 

Product

 

 

 

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A Study of the Indian BFSI Sector Based on Classification, Text Mining & Sentiment Analysis of Customer Feedback Using Python – By ICSS Student – Pijush Mandal

Category : Uncategorized

Sentiment Analysis

Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mining, deriving the opinion or attitude of a speaker. A common use case for this technology is to discover how people feel about a particular topic.

With the recent advances in deep learning, the ability of algorithms to analyse text has improved considerably. Creative use of advanced artificial intelligence techniques can be an effective tool for doing in-depth research.

These basic concepts when used in combination, become a very important tool for analyzing millions of brand conversations with human level accuracy.

 

Sentiment Analysis

 

A Study of the Indian BFSI Sector Based on Classification, Text Mining & Sentiment Analysis of Customer Feedback Using Python

 

Abstract

In the era of social media, use of social networking data to study customers’ attitude towards an organization, services or events has become an increasingly dominant trend in business strategic management research. Sentiment analysis, which is also called opinion mining, is a field of study that aims at extracting opinion and sentiment from natural language processing using computational methods. With the growth of Internet, numerous business websites have been deployed to allow online review and commenting the services in forms of either business forums or social networks. Mining opinion automatically using the reviews from such online platforms is not only useful for customers to seek for advice but also necessary for business to understand their customers and to improve their services. This paper presets the design and implementation of a system to group, summarize and analyze sentiment of various customer feedbacks. Our framework solves the problem of feedback overload, congestion, and difficulties in prioritizing valuable feedback for an organization; here we perform text mining, sentiment analysis and classification on our dataset from various websites. Virtual accuracy is achieved which shows the efficiency and reliability of the project for future implementation.

 

Social Media

 

 

Introduction

Understanding what customers think about business products or services has always been one of the most important issues in business strategic management, particularly in business decision-making program. The beliefs or perceptions of reality and the choices one makes somehow conditioned upon the way the others act. This is not true only for individuals but also for organizations. While consumers’ hunger for and rely on online advice or recommendations of products and services, business demand for utilities that can transform customers’ thoughts and conversations into customer insights or those for social media monitoring, reputation management and voice of customer programs. Traditionally individuals usually ask for opinion from friends and family members, while business rely on the surveys, focus groups, opinion polls, feedback collector and consultants. In the modern age of Big data, while millions of consumer reviews and discussions flood the internet daily basis, while individuals feel overwhelmed with information, it is as well impossible for business to keep that up manually. Thus there is a clear need of computational methods for automatically analyzing feedback.

 

In this paper we propose an effective method for managing feedback information, reducing overloads by method of grouping based on users’ activities, analyzing sentiment and providing summarization of the feedback. Our technique allows classifier and summarizer to extract information from feedback message and build a model from extraction of most frequent and common word in the message in ordered to group message into activities. Several approaches therefore have been proposed for the classification and sentiment analysis.

 

Customers

 

 

Impact of Sentiment Analysis

An organization has to have a complete understanding of their customer’s opinion and needs on their products or services they offer, but they face the challenge of dealing of unstructured text form sources of customer’s opinions and needs. Consumer’s products and services sentiments are now not only just a source of customers’ reviews and references but a source for customer services, business intelligence, and product brand reputation management.

Some of challenges and needs make organizations want to answer fundamental problems in the voice of the customer are:

  • Are the customers satisfied with services, product and support?
  • What do the customer like?
  • What customer thinks of products and services offered by competitors?
  • What influences the market and how opinions propagate?

 

These challenges include handling noise and linking with structured data. Business intelligence involves the use of technologies and methodologies for the collection, integration and analysis of the opinion as well as sentiment relevant in formation in a business for the purpose of better decision making in business.

As far the benefits form application of sentiment analysis in various contemporary company is concerned it is worth noticing application of a company or a brand with the analysis of reviews of customer product and services, provision of analytical perspectives financial investor who ant to discover and respond the market opinion its application in politics where marketing campaigns are interested in tacking sentiment expressed by voters associated with the candidates.

Like ways sentiment analysis can be used multiple areas in business like economics, finance and marketing. In economics allows responding to the question of how supervised learning methods can be used to learn the association between polarity of financial news and key financial indicator. For marketing domain, by judging the sentiment of the consumer it is very easy to place a share of heart of a new product on consumers mind.

 

Business intelligence

 

 

Related Work

Previously one of the most common existing methods to manually archive feedback into various folder with a view of reducing the number of information objects a user must process at any given time. But this is an insufficient solution as a folder names are not necessarily a true reflection of their content and their creation and maintenance can impose a significant burden on the user.

 

There are several examples of feedback analysis tool available such as:

  1. Feedier: It collects actionable feedback, Engage and value to organizations’ customers.
  2. Receptive: It easily collect, measure, and understand feedback form customers, internal team, and prospects. It is a specialist product for B2B and SAAS organization.
  3. Zonka Feedback: A comprehensive Feedback Management system with customizable surveys, instant alerts, real time report and more.
  4. Informizely: it quickly gathers customer insights with in-site surveys and polls.
  5. ai: It makes customers feedback analysis very easy.

 

Previously methods for sentiment analysis are mostly based on manually defined rules. With recent development of deep learning techniques, neural network based approaches becomes the mainstream. On the basis many researchers apply linguistic knowledge for better performance in sentiment analysis.

 

  1. Traditional Sentiment analysis: Many methods for sentiment analysis focus on feature engineering. The carefully designed features are then fed to machine learning methods in a supervised learning setting. Performance of sentiment classification therefore heavily depends on the choice of feature representation of text. In terms of features different kinds of representations have been used in sentiment analysis, including bag of words representation, word co-occurrences, and syntactic contexts. Despite its effectiveness feature engineering is labor intensive and is unable to extract and organize the discriminative information from data.

 

  1. Sentiment Analysis by Neural Network: The proposal of a simple and effective approach to learn distributive representation of word and phrase, neural network based models have shown their great success in mane natural language processing (NLP) tasks. Many models have been applied to classification, sentiment analysis and extract information. Neural network model improves coherences by exploiting the distribution of word co-occurrences through the use of neural word embedding. The extracted short and coherent pieces of text alone are sufficient for prediction, classification and can be used to explain the prediction and classification.

 

  1. Linguistic Knowledge: Linguistic knowledge has been carefully incorporated into models to realize the best potentials in terms of prediction accuracy. Classical linguistic knowledge or sentiment resources include sentiment lexicons, negators and intensifiers. Sentiment lexicons are valuables for rule based or lexicon based models, there are also studies for automatic construction of sentiment form social data or from multiple languages.

 

Previously extracting information form a feedback is done by manually but now a days it can be done through various online text mining tools like ‘Ranks.nl’ , ‘Vivisimo/Clusty’ , ‘Wordle’ etc. and various commercial text mining software like ‘ActivePoint’, ‘Aiaioo Labs’ , AKIN Desktop HyperSearch’ etc.

 

Traditional Sentiment analysis

 

Classification can be done through various classifiers like:

  1. RIPPER Text classification: RIPPER classification algorithm is often used in automatic email filtering process; its architecture is based on rule-based framework. It has the ability to automatic generate rules for selecting keywords instead of manual selection and it is fast able to deal with large set of attributes.

Focus Key: RIPPER Text classification

 

  1. Nearest Neighbour Classification: This approach is explored in a study based featured selection using mutual information. It is very simple numeric based algorithm which simply treats the feature vector as a vector inn-dimensional space and find the nearest matching vector in terms of distance. Boone found that nearest neighbour is particularly effective when only examples of each folder are presented to the algorithm.

 

Sentiment Analysis by Neural Network

 

Our Solutions

The statistical algorithms are able to fill gaps in the rule based methods but at the cost of more processing time. But one area where research is lacking in application is Natural Language Processing (NLP) for insignificant feature selection. While being tedious to apply but offers the potentials to classify more effectively on unclassified feedback as information extraction using text classification provides not only relative weights between attribute words but also helps in finding attribute.

 

Proposed algorithm utilizes NLP and probabilistic technique for feedback classification-association, recognition and prediction of new data class and sentiment.

We are using various classification technique for better results, those techniques are:

 

  1. Naïve Bayes Classification: Naïve Bayes is an algorithm based on statistical analysis with decisions and rule being made using numeric data. It processes a feedback to match words chosen at random from total words present in each folder. The words chance of being matched is proportional to the probability of finding the word in all the classes. Bayes classifier is then used in the next step to determine likelihood that the feedback being considered belongs to the right class or not.

 

  1. Support Vector Machine classification: Support Vector machine (SVM) is a supervised machine-learning algorithm, which can be used for both classification and association. In this classification algorithm each data item plotted in n-decimal space with the value of each feature being particular coordinates then classification can be find by finding the hyper plane that differentiate the classes very well.

 

Information extractions are done form unstructured or semi structured documents. Named-Entity-Recognition (NER) also known as entity identification and entity extraction is very suitable for extracting information form a data. By using NER a data can be easily classified into previously defined categories like ‘Name of Person’, ‘Organization Name’, ‘Date and Place’, ‘Expressions of Time’, ‘Monetary Value’, ‘Category of Transaction’ and more.

 

Naïve Bayes Classification

 

Workflow

Internet plays a vital role in this work as dataset is collected from various websites (like ‘www.bankbazzar.com’, ‘www.glassdoor.co.in’, ’www.mouthshut.com’, and ‘www.indeed.co.in’), which contains feedbacks for the BFSI sector popularly used in India. Customer feedback and reviews refers to the statement given by various customers who have used these services so far. Referring to the words and star ratings used by them the feedbacks are classified into various services and then in carried towards the next step. Applying Naïve Bayes and Support vector machine classifier are used, after this by using supervised learning approach new feedback star rating can be predicted by the sentimental analysis, feedback can be classified according to their classes, and information can be extracted from the feedback. Scores are calculated and compare between the methods for better results and accuracy. Then by using various NER models for extracting information from the feedback as previously defined classes.

For classification and sentiment analysis each feedback is calculated using both Naïve Bayes classifier-Naïve Bayes sentiment analysis and SVM classifier-SVM sentiment analysis, based on which a comparative study is made leading to choose a better algorithm out of two. The steps are as follows the feedbacks recorded imported from the dataset and separated for every data class, Sentimental analysis and Classification algorithm are applied, positive negative and neutral feedbacks are calculated and classes are divides, scores are calculated using both methods. Comparisons are preferred and accuracy is judged accordingly.

 

For extracting information form a data by using Stanford NER data can be extracted. Stanford NER is a java implementation of Named Entity Recognizer. NER levels all the words in texts, which is text, name of tings such as person name company name etc. Stanford NER are used for defining 3 model which is 3 class model (Location, Person, Organization), 4 class model (Location, Person Organization, Misc.), 7 class model (Location, Person Organization, Money, Percent, Date, Time).

 

Internet

 

Methodology

Feedbacks for the BFSI sector popularly used in India are collected from various websites (like ‘www.bankbazzar.com’, ‘www.glassdoor.co.in’, ’www.mouthshut.com’, and ‘www.indeed.co.in’), which contains feedbacks. Positive feedbacks are for good customer services, beneficial product or service, nice environment and well management. Negative feedbacks are for bad customer services, product or service are not good as the expected level of the customer, bad circumstances and management are not good in those sector to the customer as their expected level or the standard level in those BFSI sector. Average feedbacks are for the average services, product and management.

 

Methodology

 

Exploration

For visualize the data a little more by plotting some graphs with the Seaborn library. Seaborn’s FacetGrid allows creating grid of histogram places side by side, by using FacetGrid we can see if there is any relationship between the variables.

 

 

Overview of Python

Python is a general purpose, dynamic, high level and interpreted programming language. It supports Object Oriented programming approach to develop applications. It is relatively simple, so it’s easy to learn since it requires a unique syntax that focuses on readability. Developers can read and translate Python code much easier than other languages. In turn, this reduces the cost of program maintenance and development because it allows teams to work collaboratively without significant language and experience barriers.

Unlike other languages Python is dynamically typed that is why we don’t need to declare data types of the variables (for example, if we write a=10 it will automatically assign an integer value to the variable ‘a’). Like most languages, Python has a number of basic types including integers, floats, booleans, and strings. These data types behave in ways that are familiar from other programming languages.

Python can also be used to process text, display numbers or images, save data, etc. So, to for executing the Natural Language Processing we used python as the scripting language. Basic statements of python which are frequently used-

 

The if  statement is used to  check  a condition and if the condition is

true, we run a block of statements (called the if-block), else we process another block of statements (called the else-block). Nested if or elif

can also be used for multiple conditions.

 

  • The for statement iterates over the members of a sequence in order, executing the block each time. In contrast to for statement while loop is used when a condition needs to be checked each iteration, or to repeat a block of code.

 

  • The try statement is sets exception handling blocks in the code. The keyword try and except are used to catch exceptions, when an error occurs within the try block, Python looks for a matching except block to handle it.

 

  • The def statement is used to define a function or method.

 

  • The import statement is used to import modules whose functions can be used in current program.

 

  • The print statement is used to send output to the standard output unit of your computer system. But in python 3 it has become a function.

 

Python

 

 

Important Libraries of Python used in Project

One of Python’s greatest assets is its extensive set of libraries. Libraries are sets of routines and functions that are written in a given language. A robust set of libraries can make it easier for developers to perform complex tasks without rewriting many lines of code. These are the basic libraries that transform Python from a general purpose programming language into a powerful and robust tool for data analysis and visualization. Libraries which are used in my project are-

 

  1. NumPy is the foundational library for scientific computing in Python, and many of the libraries on this list use NumPy arrays as their basic inputs and outputs. In short, NumPy introduces objects for multidimensional arrays and matrices, as well as routines that allow

developers to perform advanced mathematical and statistical functions on those arrays with as little code as possible.

  1. Pandas adds data structures and tools that are designed for practical data analysis in finance, statistics, social sciences, and engineering. Pandas works well with incomplete, messy, and unlabeled data (i.e., the kind of data you’re likely to encounter in the real world), and provides tools for shaping, merging, reshaping, and slicing datasets.
  • SciPy builds on NumPy by adding a collection of algorithms and high-level commands for manipulating and visualizing data. This package includes functions for computing integrals numerically, solving differential equations, optimization, and more.
  1. NLTK, the name of this suite of libraries stands for Natural Language Toolkit and, it is a set of libraries designed for Natural Language Processing (NLP). NLTK’s basic functions allow you to tag text, identify named entities, and display parse trees, which are like sentence diagrams that reveal parts of speech and dependencies. From there, you can do more complicated things like sentiment analysis and automatic summarization.

 

Statements

 

 

Conclusions

On the era of modern age as the online interaction has bridged physical distance and allowed companies to pursue profit and expand their business as well as reputation all over the world, keeping touch in with their customers has simultaneously more and more important for business. To have their finger on the pulse of the customer, business must have access to reliable feedback and able to analyze it properly.

Sentiment analysis, classification and extracting information is yet a challenging problem, and gains the interests of many researchers from different disciplines, its application are practical, promising and various in many industries including BFSI sector.

 

Era

 

 

 

Project Done by ICSS Student – Pijush Mandal (PDF)

 

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WPA3 New Wi-Fi Standard Released

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WPA3 New Wi-Fi Standard Released

WPA stands for Wi-Fi Protected Access, and is a security technology for Wi-Fi networks. It was developed in response to the weaknesses of WEP (Wired Equivalent Privacy), and therefore improves on WEP’s authentication and encryption features.

Wi-Fi Alliance, the organization that manages Wi-Fi technologies, announced the official release of WPA3.

WPA3 is the latest version of Wi-Fi Protected Access (WPA), a user authentication technology for Wi-Fi connections.

News that the Wi-Fi Alliance was working on WPA3 leaked online in January. The organization started working on WPA3 after a security researcher revealed KRACK, a vulnerability in the WPA2 WiFi protocol that made it somewhat trivial for an attacker to gain access to WiFi transmissions protected by WPA2.

WPA3 is currently optional for all newly produced devices, but it will become the de-facto Wi-Fi authentication standard for all Wi-Fi capable devices in the coming years.

 

WPA

 

 

WPA3-Personal and WPA3-Enterprise

Like WPA1 and WPA2 before it, there are two WPA3 “security modes” —WPA3-Personal and WPA3-Enterprise. The main difference between these two security modes is in the authentication stage.

WPA3 uses the Simultaneous Authentication of Equals (SAE) algorithm, which replaces Pre-shared Key (PSK) in WPA2-Personal, while WPA3-Enterprise uses a more complex set of features that replace IEEE 802.1X from WPA2-Enterprise.

The WPA3-Enterprise security mode is recommended for devices used on enterprise, governments, and financial networks.

As for WPA3-Personal, this is the standard that most of us will be interacting with on a regular basis once we replace older devices.

Here are some key features provided by the new protocol:

1.) Protection Against Brute-Force Attacks

WPA3 provides enhanced protection against offline brute-force dictionary attacks, making it harder for hackers to crack your WiFi password—even if you choose less complex passwords—by using commonly used passwords over and over again.

2.) WPA3 Forward Secrecy

WPA3 leverages SAE (Simultaneous Authentication of Equals) handshake to offer forward secrecy, a security feature that prevents attackers from decrypting old captured traffic even if they ever learn the password of a network.

 

WPA3

 

 

WPA3 is resistant to dictionary attacks

The Wi-Fi Alliance says that WPA3’s SAE is resistant to offline dictionary attacks where an attacker tries to guess a Wi-Fi network’s password by trying various passwords in a quick succession.

Security experts who’ve analyzed the standard say WPA3 will block authentication requests after several failed attempts, hence limiting the impact of such brute-force attacks.

Furthermore, WPA3’s SAE also implements a cryptography method known as forward secrecy. This is a feature of key-exchange authentication protocols where session keys are independent on their own and will not be compromised even if the private key of the server is compromised.

 

attacks

 

 

Wi-Fi Easy Connect for WPA2 and WPA3

A separate Wi-Fi feature also announced with WPA3 is a technology called Wi-Fi Easy Connect. This feature is aimed at smart (Internet of Things) devices that don’t have a screen where a user can configure its Wi-Fi network settings.

For example, a user will be able to use his phone or tablet to configure the WiFi WPA3 options of another device that doesn’t have a screen, such as tiny IoT equipment like smart locks, smart light bulbs, and others.

 

Wi-Fi

 

 

Wi-Fi Enhanced Open

Earlier this month, the Wi-Fi Alliance also announced Wi-Fi Enhanced Open, another proprietary technology that is meant to be deployed on “open Wi-Fi networks” such as those in airports, malls, bars, or internet cafes.

The technology works by using an algorithm known as Opportunistic Wireless Encryption (OWE) to encrypt each connection between a WiFi user and the router/access point with its own custom encryption key.

This per-user encryption prevents local attackers from snooping on other users’ traffic, even if the network doesn’t require a password to join.

Following the disclosure of the KRACK vulnerability, the Wi-Fi Alliance has reacted admirably and has released technologies meant to boost everyone’s security. Now, all that remains is that device vendors incorporate them in new products at their earliest convenience.

 

Enhanced

 

 

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EFF Launches Encryption Initiative for Email Domains Named STARTTLS Everywhere

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EFF Launches Encryption Initiative for Email Domains Named STARTTLS Everywhere

EFF (Electronic Frontier Foundation) announced a new project named STARTTLS Everywhere that aims to provide guidance to server administrators on how to set up a proper email server that runs STARTTLS the correct way.

STARTTLS Everywhere is eerily similar to Let’s Encrypt, another pro-encryption initiative the EFF launched together with Mozilla and Cisco two years ago.

But this initiative aims to bring encrypted communications to email servers, instead of web servers (Let’s Encrypt’s purpose).

 

EFF

 

 

What’s STARTTLS

STARTTLS is an addition to SMTP, which allows one email server to say to the other, “I want to deliver this email to you over an encrypted communications channel.” The recipient email server can then say “Sure! Let’s negotiate an encrypted communications channel.” The two servers then set up the channel and the email is delivered securely, so that anybody listening in on their traffic only sees encrypted data. In other words, network observers gobbling up worldwide information from Internet backbone access points (like the NSA or other governments) won’t be able to see the contents of messages while they’re in transit, and will need to use more targeted, low-volume methods.

STARTTLS works by allowing two email servers that want to send/receive an email to exchange certificates and set up an encrypted communications channel between the two. Once the encrypted channel is secured, the sending server transmits the email in an encrypted form, which is then decrypted on arrival.

 

STARTTLS

 

 

STARTTLS already deployed on 89% of all email servers

STARTTLS is not new by any stretch of the imagination. The SMTP standard extension was approved in 1999, and according to Google’s latest Email Transparency Report, it’s already deployed on 89% of all email servers currently online.

But despite its huge reach, EFF experts say STARTTLS is often misconfigured.

Anyone can interpose himself between two email servers and use an invalid certificate to pose as the recipient or sender, as most email servers fail to verify the provided certificate’s authenticity.

Furthermore, due to a lapse in STARTTLS’ design, STARTTLS-encrypted email communication channels can be downgraded to sending the email message in cleartext, instead of an encrypted form.

This “feature” was designed for situations where one server does not support STARTTLS, but during the past few years, security researchers and privacy advocates have often spotted ISPs in various countries intentionally downgrading STARTTLS to cleartext for various purposes that range from state-wide surveillance to user tracking and advertising.

 

SMTP

 

 

STARTTLS Everywhere is like Let’s Encrypt, but for email

The EFF says this is where its latest project, STARTTLS Everywhere, will be able to help.

“STARTTLS Everywhere provides software that a sysadmin can run on an email server to automatically get a valid certificate from Let’s Encrypt,” the EFF says. “This software can also configure their email server software so that it uses STARTTLS, and presents the valid certificate to other email servers.”

“Finally, STARTTLS Everywhere includes a ‘preload list’ of email servers that have promised to support STARTTLS, which can help detect downgrade attacks. The net result: more secure email, and less mass surveillance.”

 

Encrypt

 

 

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HMRC – The UK’s tax agency Recorded the Voices of 5.1 Million Brits

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HMRC – The UK’s tax agency Recorded the Voices of 5.1 Million Brits

HMRC is a non-ministerial department of the UK Government responsible for the collection of taxes, the payment of some forms of state support and the administration of other regulatory regimes including the national minimum wage.

HMRC was formed by the merger of the Inland Revenue and Her Majesty’s Customs and Excise, which took effect on 18 April 2005.[4] The department’s logo is the St Edward’s Crown enclosed within a circle.

HMRC (Her Majesty’s Revenue and Customs)— The UK’s tax agency has collected the voice records of over 5.1 million Brits, a UK-based privacy and civil liberties group has discovered.

The HMRC collected these voice records via a new service it launched in January 2017. Called Voice ID, the service allows UK citizens to authenticate when calling HMRC call centers via their voice.

 

HMRC

 

HMRC misled users into providing a voice sample

When it launched, the HMRC website claimed users would be able to opt out of using this feature and continue to authenticate and prove their identity via the usual methods.

But an investigation by privacy group Big Brother Watch has discovered that there’s no opt-out option when calling the HMRC support line, and all callers were forced to record a voice track to use with the Voice ID service.

The only way to avoid creating a voice track was by saying “no” three times during the voice track creation process, something the privacy group’s investigators discovered on their own.

Unfortunately, the Voice ID system didn’t record this option, and it would pester the caller for a voice sample every time they called back.

 

voice sample

 

Privacy group: HMRC broke the law

Big Brother Watch members argue that the HMRC broke user rights by not providing a simple way of opting out.

Furthermore, after a very lengthy and complicated process, users can only opt out from using voice recognition for the authentication process, but users can’t have their voice patterns removed from HMRC’s database.

The privacy group argues that HMRC is in clear violation of GDPR (an EU user privacy directive that’s been enacted in the UK) by not prompting Brits for active consent and by not giving them an easy method of revoking consent and having their personal biometric data removed.

 

Big Brother

 

ICO is investigating

Big Brother Watch officials are now urging users to file a complaint with the HMRC and file another complaint about the HMRC with the UK’s Information Commissioner’s Office (ICO), the UK’s national data protection authority.

The privacy group says it already notified ICO officials on its own, and the latter started an official investigation into HMRC’s practices.

 

ICO

 

 

 

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Noriben: Portable, Simple, Malware Analysis Sandbox

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Noriben

Noriben   is a Python-based script that works in conjunction with Sysinternals Procmon to automatically collect, analyze, and report on runtime indicators of malware. In a nutshell, it allows you to run your malware, hit a keypress, and get a simple text report of the sample’s activities.

Noriben allows you to not only run malware similar to a sandbox but to also log system-wide events while you manually run malware in ways particular to make it run. For example, it can listen as you run malware that requires varying command line options, or user interaction. Or, to watch the system as you step through malware in a debugger.

Noriben solely requires Sysinternals procmon.exe (or procmon64.exe) to function. It requires no pre-filtering (although it might tremendously assist) because it incorporates quite a few white record gadgets to scale back undesirable noise from system exercise.

Noriben

 

Cool Features of Noriben:

If  you will have a folder of YARA signature information, you’ll be able to specify it with the –yara choice. Every new file create shall be scanned towards these signatures with the outcomes displayed within the output outcomes.

If you will have a VirusTotal API, place it right into a file named “virustotal.api” (or embed immediately within the script) to auto-submit MD5 file hashes to VT to get the variety of viral outcomes.

You can add lists of MD5s to auto-ignore (resembling your entire system information). Use md5deep and throw them right into a textual content file, use –hash to learn them.

You can automate the script for sandbox-utilization. Using -t to automate execution time, and –cmd “pathexe” to specify a malware file, you’ll be able to routinely run malware, copy the outcomes off, after which revert to run a brand new pattern.

YARA

 

Bypassing Anti-Sandboxing

One   common instance to use Noriben is with malware that is VM and Sandbox aware. Throwing the sample into any existing sandbox will most likely result in a report with no artifacts as the malware didn’t run. Some applications look for manual user activity, such as mouse movement and clicking. Other malware may infect the WinHTTP stack and only trigger when a web browser is used. By just launching Noriben in the background, all of the system behavior is logged as the analyst manually controls the system to give the impression of a normal user. Once the file has been detonated, the results can be reviewed as a standard sandbox report.

sandbox

 

Command Line-Based Applications

 

In   rarer cases are malware samples that require command line options in order to run. Launching these executables within a sandbox would immediately fail as the malware does not have the arguments to operate. However, an analyst manually controlling the malware while Noriben is running can quickly gather all system artifacts from various command line options.

command

 

General Attack Artifacts

Even   more interesting, Noriben has been used by pentesters to determine what system artifacts exist when launching an attack against a system or service. By monitoring files created or registry entries modified, a security analyst can determine all artifacts that result from running an attack, a PowerShell command, or a Javascript-based web page.

Javascript

 

Perfect for Malware Analysis on the Road

It’s    commonly a scenario where an analyst may have a proper sandbox environment in a home lab but on the road has only a laptop. In working with various Sales Engineers and Support individuals from security companies, there were many times where they needed an immediate malware answer out of their hotel room. Noriben was designed to be used with little effort, little setup, and little maintenance. Even if you don’t have a dedicated malware VM, any Windows VM will do! Even <a snapshot copy of> your corporate environment!

VM

 

 

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Retailer Debenhams's Flowers website hacked

Retailer Debenhams’s Flowers website hacked

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Retailer Debenhams’s Flowers website hacked

Retailer Debenhams’s Flowers website hacked. The news came that Retailer Debenhams’s Flowers website was hacked and up to 26,000 customers, personal data has been compromised.

Department store chain Debenhams has issued a statement on the data breach, revealing payment details, names and addresses were potentially taken , which targeted Ecomnova, a third party e-commerce company that owns and operates the flower and gifting website. Debenhams said it has contacted customers whose data was accessed. While, customers of  Debenhams.com, which is a separate website, have not been affected.

On  24 February , the company noticed the cyber attack for the first time and then again on  11 April from then Debenhams Flowers website is offline.

Retailer Debenhams's Flowers website hackedDebenhams stated, “Our communication to affected customers includes detailing steps that we have taken and steps that those customers should take”.

In an interview with BBC, a spokeswoman has said that they have sent emails to up to 26,000 customers and informed them about the cyber attack followed up with a letter in the post.

Customers who suspect they’ve been the victim of fraud must immediately contact their bank or credit card provider, as well as Action Fraud, the UK’s national fraud and internet crime reporting centre, on 0300 123 2040 or online.

Debenhams chief executive Sergio Bucher said in a statement “As soon as we were informed that there had been a cyber-attack, we suspended the Debenhams Flowers website and commenced a full investigation”.

“We are very sorry that customers have been affected by this incident and we are doing everything we can to provide advice to affected customers and reduce their risk” , they said.

The Information Commissioner’s Office (ICO) has already been informed about the incident.

 

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