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Artificial Intelligence

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     Artifical Intelligence 

 Introduction:

Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science AI research is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving.
The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring “intelligence” are often removed from the definition, a phenomenon known as the AI effect, leading to the quip in Tesler’s Theorem, “AI is whatever hasn’t been done yet.” For instance, optical character recognition is frequently excluded from “artificial intelligence”, having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go),autonomously operating cars, and intelligent routing in content delivery networks and military simulations.

artificial intelligence
History:

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an “AI winter”), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. “robotics” or “machine learning”), the use of particular tools (“logic” or artificial neural networks), or deep philosophical differences.Subfields have also been based on social factors (particular institutions or the work of particular researchers).
The study of mechanical or “formal” reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing’s theory of computation, which suggested that a machine, by shuffling symbols as simple as “0” and “1”, could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the Church–Turing thesis.[26] Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to consider the possibility of building an electronic brain. Turing proposed that “if a human could not distinguish between responses from a machine and a human, the machine could be considered “intelligent”.[27] The first work that is now generally recognized as AI was McCullouch and Pitts’ 1943 formal design for Turing-complete “artificial neurons”
The field of AI research was born at a workshop at Dartmouth College in 1956. Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research. They and their students produced programs that the press described as “astonishing”computers were learning checkers strategies (c. 1954) (and by 1959 were reportedly playing better than the average human), solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English. By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense and laboratories had been established around the world. AI’s founders were optimistic about the future: Herbert Simon predicted, “machines will be capable, within twenty years, of doing any work a man can do”. Marvin Minsky agreed, writing, “within a generation … the problem of creating ‘artificial intelligence’ will substantially be solved”.artificial itelligene

Basics

A typical AI perceives its environment and takes actions that maximize its chance of successfully achieving its goals.An AI’s intended goal function can be simple (“1 if the AI wins a game of Go, 0 otherwise”) or complex (“Do actions mathematically similar to the actions that got you rewards in the past”). Goals can be explicitly defined, or can be induced.
AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute.[b] A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following recipe for optimal play at tic-tac-toe:
If someone has a “threat” (that is, two in a row), take the remaining square. Otherwise,
if a move “forks” to create two threats at once, play that move. Otherwise,
take the center square if it is free. Otherwise,
if your opponent has played in a corner, take the opposite corner. Otherwise,
take an empty corner if one exists. Otherwise,
take any empty square.
Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or “rules of thumb”, that have worked well in the past), or can themselves write other algorithms. Some of the “learners” described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, if given infinite data, time, and memory, learn to approximate any function, including whatever combination of mathematical functions would best describe the entire world. These learners could therefore, in theory, derive all possible knowledge, by considering every possible hypothesis and matching it against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of “combinatorial explosion”, where the amount of time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering broad swaths of possibilities that are unlikely to be fruitful.[58][59] For example, when viewing a map and looking for the shortest driving route from Denver to New York in the East, one can in most cases skip looking at any path through San Francisco or other areas far to the West; thus, an AI wielding an pathfinding algorithm like A* can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered in turn.


Problems

The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.
Reasoning, problem solving

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.
Knowledge representation

Knowledge representation and knowledge engineering are central to classical AI research. Some “expert systems” attempt to gather together explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the “commonsense knowledge” known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects; situations, events, states and time;causes and effects;knowledge about knowledge (what we know about what other people know); and many other, less well researched domains. A representation of “what exists” is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them.

artificial intelligence
Planning

A hierarchical control system is a form of control system in which a set of devices and governing software is arranged in a hierarchy.
Intelligent agents must be able to set goals and achieve them. They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the utility (or “value”) of available choices.artificial intelligence
Learning

Machine learning, a fundamental concept of AI research since the field’s inception is the study of computer algorithms that improve automatically through experience.
Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first. Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.
In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.

artificial  intelligence
Natural language processing

Natural language processing (NLP) gives machines the ability to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering and machine translation. Many current approaches use word co-occurrence frequencies to construct syntactic representations of text. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well. Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications. Beyond semantic NLP, the ultimate goal of “narrative” NLP is to embody a full understanding of commonsense reasoning.

artificial intelligence
Perception

Machine perception is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition,[115] facial recognition, and object recognition.[116] Computer vision is the ability to analyze visual input.
Requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its “object model” to assess that fifty-meter pedestrians do not exist.

artifciial intelligence
Social intelligence

Moravec’s paradox can be extended to many forms of social intelligence. Distributed multi-agent coordination of autonomous vehicles remains a difficult problem. Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent.Similarly, some virtual assistants are programmed to speak conversationally or even to banter humorously; this tends to give naïve users an unrealistic conception of how intelligent existing computer agents actually are.

artificial intelligence
General intelligence

Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author’s argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author’s original intent (social intelligence). A problem like machine translation is considered “AI-complete”, because all of these problems need to be solved simultaneously in order to reach human-level machine performance.
Embodied intelligence

This includes embodied, situated, behavior-based, and nouvelle AI. Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive. Their work revived the non-symbolic viewpoint of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.

artificial intelligence
Intelligent agent paradigm

An intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as firms). The paradigm allows researchers to directly compare or even combine different approaches to isolated problems, by asking which agent is best at maximizing a given “goal function”The paradigm also gives researchers a common language to communicate with other fields—such as decision theory and economics—that also use concepts of abstract agents.

artificial intelligence
Artificial neural networks

Neural networks, or neural nets, were inspired by the architecture of neurons in the human brain. A simple “neuron” N accepts input from multiple other neurons, each of which, when activated (or “fired”), cast a weighted “vote” for or against whether neuron N should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed “fire together, wire together”) is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The net forms “concepts” that are distributed among a subnetwork of shared[j] neurons that tend to fire together; a concept meaning “leg” might be coupled with a subnetwork meaning “foot” that includes the sound for “foot”. Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural nets can learn both continuous functions and, surprisingly, digital logical operations. Neural networks’ early successes included predicting the stock market and (in 1995) a mostly self-driving car. In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related M&A in 2017 was over 25 times as large as in 2015.
Today, neural networks are often trained by the backpropagation algorithm, which had been around since 1970 as the reverse mode of automatic differentiation published by Seppo Linnainmaa,and was introduced to neural networks by Paul Werbos.

artificial  intelligence
Applications

AI is relevant to any intellectual task. Modern artificial intelligence techniques are pervasive and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.artificial intelligence

Healthcare

AI is being applied to the high cost problem of dosage issues—where findings suggested that AI could save $16 billion. In 2016, a ground breaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.
X-ray of a hand, with automatic calculation of bone age by computer software
Artificial intelligence is breaking into the healthcare industry by assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer. There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called “Hanover”.

artificial intelligence
Finance and economics

Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987 when Security Pacific National Bank in US set-up a Fraud Prevention Task force to counter the unauthorised use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services.
Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place. In August 2001, robots beat humans in a simulated financial trading competition.AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.

artificial intelligence
Video games

In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in non-player characters (NPCs). In addition, well-understood AI techniques are routinely used for pathfinding. Some researchers consider NPC AI in games to be a “solved problem” for most production tasks. Games with more atypical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010).artificial intelligence
Audit

For financial statements audit, AI makes continuous audit possible. AI tools could analyze many sets of different information immediately. The potential benefit would be the overall audit risk will be reduced, the level of assurance will be increased and the time duration of audit will be reduced.
Superintelligence

Are there limits to how intelligent machines – or human-machine hybrids – can be? A superintelligence, hyperintelligence, or superhuman intelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. ‘’Superintelligence’’ may also refer to the form or degree of intelligence possessed by such an agent.
Technological singularity

If research into Strong AI produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to recursive self-improvement.[358] The new intelligence could thus increase exponentially and dramatically surpass humans. Science fiction writer Vernor Vinge named this scenario “singularity”.Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization. Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable.
Ray Kurzweil has used Moore’s law (which describes the relentless exponential improvement in digital technology) to calculate that desktop computers will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045.

artificial intelligence
In fiction

The word “robot” itself was coined by Karel Čapek in his 1921 play R.U.R., the title standing for “Rossum’s Universal Robots”
Thought-capable artificial beings appeared as storytelling devices since antiquity,and have been a persistent theme in science fiction.
A common trope in these works began with Mary Shelley’s Frankenstein, where a human creation becomes a threat to its masters. This includes such works as Arthur C. Clarke’s and Stanley Kubrick’s 2001: A Space Odyssey (both 1968), with HAL 9000, the murderous computer in charge of the Discovery One spaceship, as well as The Terminator (1984) and The Matrix (1999). In contrast, the rare loyal robots such as Gort from The Day the Earth Stood Still (1951) and Bishop from Aliens (1986) are less prominent in popular culture.
Isaac Asimov introduce the Three Laws of Robotics in many books and stories, most notably the “Multivac” series about a super-intelligent computer of the same name.

artificial intelligence
Conclution

In short, most neural networks use some form of gradient descent on a hand-created neural topology. However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches. One advantage of neuroevolution is that it may be less prone to get caught in “dead ends”.

artificial intelligence


Monero

Monero Currently in Circulation Has Been Mined Using Malware

Category : Blog

Monero Currently in Circulation Has Been Mined Using Malware

Monero cryptocurrency currently in circulation has been mined using malware, and about 2% of the total daily hashrate comes from devices infected with cryptocurrency-mining malware. These numbers are the results of in-depth research of the coin-mining malware scene by security researchers from Palo Alto Networks.

The report, released June 11, has analyzed 629,126 malware samples that have been detected as part of coin-mining operations. The research didn’t analyze in-browser miners (cryptojackers), but only traditional malware families that infected desktops and servers since June last year, when there was a significant spike in coin-mining operations.

The research team at Palo Alto discovered because malware needs to be built directly into the source code of cryptocurrency mining pool. The malware also requires a Monero address under which it operates and handles any illegal funds generated from mining the cryptocurrency.

 

Monero

 

 

Monero is the most popular cryptocoin

According to researchers, 84% of all malware samples they’ve detected were focused on mining for the Monero cryptocurrency, by far the most popular coin among malware groups.

Because Monero-based coin-mining malware must embed in its source code the mining pool and Monero address through which the malware operates and collects ill-gotten funds, researchers have been able to track most of the money these groups generated on infected devices.

By querying nine mining pools (which allow third-parties to query their payment stats) with the 2,341 Monero addresses researchers found embedded in the 531,6663 malware samples that focused on mining Monero, they were able to determine the amount of funds these groups have made in the past year.

 

coin-mining

 

Malware groups made over $108 million worth of Monero

According to Palo Alto Networks researchers, criminal groups have mined an approximate total of 798,613.33 Monero coins (XMR) using malware on infected devices.

That’s over $108 million in US currency, just from coin-mining operations alone. This sum also represents around 5% of all the Monero currently in circulation —15,962,350 XMR.

Furthermore, since mining pools also reveal a miner’s hash rate —the speed at which a miner completes an operation— researchers were also able to determine the amount of Monero coin-mining botnets have been generating per day.

Researchers say that during the past year, infected devices were responsible for 19,503,823.54 hashes/second, which is roughly 2% of the entire hashing power of the Monero network.

 

Malware

 

 

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Ethereum

Ethereum “Giveaway” Scammers Have Tricked People Out of $4.3 Million

Category : Blog

Ethereum “Giveaway” Scammers Have Tricked People Out of $4.3 Million

Ethereum is a distributed public blockchain network. the Ethereum blockchain focuses on running the programming code of any decentralized application.

In the Ethereum blockchain, instead of mining for bitcoin, miners work to earn Ether, a type of crypto token that fuels the network.

Online crooks promoting fake “giveaways” have tricked people out of 8,148 Ether, currently worth around $4.3 million, according to statistical data compiled in EtherScamDB.

The EtherScamDB website was created by the team behind the MyCrypto wallet service for the purpose of tracking various types of online scams centering around the Ethereum platform and associated cryptocurrencies and assets.

For the past few months, the website has been inventorying various types of Ethereum scams, such as classic phishing sites that imitate legitimate apps and wallets, trust-trading sites that push inaccurate advice or recommendations, but also online giveaways scams that promise to multiply Ether funds if victims transfer crooks a small sum of money.

 

Ethereum

 

Twitter’s “Ether giveaway” scam problem

The latter category has recently become rampant on Twitter, and on a daily basis, the social network’s most popular tweets are often inundated by these “Ether giveaway” scams.

More precisely, this particular trend caught fire with crooks this past February after Bleeping Computer first reported that one particular scammer made $5,000 in one night just by posing as Elon Musk, John McAfee, and a few other celebrities on Twitter.

Soon after our report, scams of these types started to flood Twitter left and right, with crooks registering Twitter accounts with names similar to legitimate ones, and then posting misleading messages, asking users to donate funds to an Ethereum address to receive a multiplied sum as part of a limited offer giveaway.

 

scams

 

EtherScanDB tracks hundreds of fake giveway addresses

Some of these scams and the Ether addresses where crooks have been collecting “donations” for the fake giveaways have been tracked in the EtherScamDB.

According to a recent tweet by John Backus, founder of Bloom and Cognito, two blockchain-powered apps, crooks promoting these giveaway scams have made 8,148 Ether ($4.3 million) just from the Ether funds sent to the 468 Ethereum addresses tracked by the site.

This sum is obviously larger, since the website does not track all giveaway scams, but even so, this small statistics shows how big this problem is today.

 

blockchain

 

Twitter’s been slow to react

Twitter, in particular, has been slow to respond to users reporting ake accounts, sometimes taking days or weeks to suspend obvious clones. Nevertheless, with a limited support staff, and with all the hate speech and terrorist propaganda happening on the platform, it is somewhat understandable why Twitter has been slow to react.

In the meantime, spreading the word about this scam is probably the best way to educate users and remind them to pay attention to the Twitter handle from which these offers are being made.

But while some might think the consensus advice is to tell users to “pay attention to the Twitter handle pushing an Ethereum giveaway,” the actual sensible advice is to “not participate in giveaways” to begin with, since most of these are just plain ol’ scams.

 

Twitter

 

 

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Weight Watchers

Weight Watchers IT Infrastructure Exposed via No-Password Kubernetes Server

Category : Blog

Weight Watchers suffered a small Security Breach

Weight Watchers is the registered trademark of Weight Watchers International, Inc.

Just like many companies before it, weight loss program Weight Watchers suffered a small security breach after security researchers found a crucial server exposed on the Internet that was holding the configuration info for some of the company’s IT infrastructure.

The exposed server was a Kubernetes instance, a type of software for managing large IT networks and easily deploying app containers across multiple servers, usually on a cloud infrastructure.

Dozens of servers containing Weight Watcher’s data were left exposed after the company failed to password protect software used for managing application containers, according to German cybersecurity firm Kromtech.

An Amazon cloud infrastructure used by Weight Watchers was left vulnerable—46 Amazon S3 buckets in total—including logs, passwords, and private encryption keys, Kromtech found.

 

Weight Watchers

 

Weight Watchers ran a no-password Kubernetes instance

Researchers from German cyber-security firm Kromtech discovered that Weight Watchers forgot to set a password for the administration console of one of its Kubernetes instances.

This granted anyone knowing where to look (port 10250) access to this servers, without the need to enter a username and password.

All in all, the Kubernetes instances exposed an administrator’s root credentials, access keys for 102 of their domains, and 31 IAM users including users with administrative credentials and applications with programmatic access.

Weight Watchers added that its internal team and a third-party forensics company investigated the incident and that “each has independently confirmed that there was no indication that any personally identifiable information was exposed,” a spokesperson said.

The exposure was the result of a misconfigured Kubernetes instance, Kromtech said. Kubernates is a tool developed by Google for managing large numbers of applications. Notably, a Kubernetes instance on Telsa’s cloud infrastructure was hacked earlier this year, and then used by the perpetrators to mine cryptocurrency.

 

Kubernetes

 

Unclear what data was exposed

It is unclear if someone else besides the Kromtech team discovered this Kubernetes instance, but an attacker with access to this server would have been able to access a large part of Weight Watchers’ network.

It is also unclear what kind of data (user details?) these servers were storing, as the Kromtech team could not go wandering off inside Weight Watchers’ network without violating a slew of laws.

Diachenko and the Kromtech team said they reported the exposed server to Weight Watchers, who quickly remediated the issue, thanking the researchers.

 

Unclear Data

 

Weight Watchers claims it was a non-production network

“We really appreciate the community working to make us all safer,” a Weight Watchers spokesperson said in its response to Kromtech.

“We have confirmed the issue – a security group for a test cluster in our non-production account was misconfigured during testing. The issue should be resolved and keys should be revoked. We’ve also implemented some safeguards to protect against this issue from recurrence.”

But Kromtech disputes Weight Watchers’ explanation that this was a non-production account. Nonetheless, today, a Weight Watchers spokesperson stood by its initial statement.

“Last week, Weight Watchers received a report from security researchers related to the exposure of credentials in one non-production AWS account,” a company spokesperson told Bleeping Computer via email. “The account was in a testing environment clearly labeled ‘nonprod’ and is used only to test new services and features.”

“To be able to test and innovate securely, we keep test environments completely separate from production environments. Our internal team and a reputable third-party security forensics team have investigated the exposed account key scope and activity, and each has independently confirmed that there was no indication that any personally identifiable information was exposed,” the spokesperson told us.

Weight Watchers is certainly not the first company to have to deal with a leaky or non-protected server. Other companies that suffered a similar fate include Tesla, Honda, Universal, and Bezop, just to name a few.

 

Kromtech

 

 

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CrowdStrike

CrowdStrike customers suffers data breach

Category : Blog

CrowdStrike customers suffers data breach

CrowdStrike is an American cybersecurity technology company based in Sunnyvale, California, and a wholly owned subsidiary of CrowdStrike Holdings, Inc. The company provides endpoint security, threat intelligence, and incident response services to customers in more than 170 countries. The company has been involved in countermeasure efforts to several high-profile cyber-attacks, including the Sony Pictures hack the 2016 Democratic National Committee email leak, and the Democratic National Committee cyber attacks.

When data breaches occur, often, the problem can be traced down to third-parties in a supply chain, or basic, lax security processes in IT environments.

US cyber-security firm CrowdStrike announced a new warranty program for its customers, offering to cover up to $1 million in expenses if a customer protected by its top-tier endpoint protection solution suffers a security breach.

On Tuesday, the cybersecurity firm announced the launch of a warranty worth up to $1 million should customers of its endpoint security software experience a successful data breach caused by exploits, ransomware, zero-day vulnerabilities, and more.

 

CrowdStrike

 

The warranty can be used to cover data breach expenses

CrowdStrike says customers can use the warranty to cover certain breach response fees and expenses incurred by the customer following the breach, such as legal consultation, forensic services, notification expenses, identity theft and credit monitoring, public relations and cyber extortion payments.

The warranty is offered on a “take it or leave it” basis, and CrowdStrike doesn’t plan to allow customers to negotiate its terms and coverage.

The warranty will only cover security breaches during its duration, and pre-existing security incidents are not eligible.

 

warranty

 

The problem of inexistent security software warranties

“Other industries have long offered product warranties to assure customers that the products they purchase will function as advertised,” CrowdStrike said on Tuesday in a canned presser. “This has not been the case in cybersecurity, where customers generally have little recourse when security products fail to protect them.”

The company claims it’s the first to offer such a data breach warranty protection to clients. This may be true for “data breaches,” but not true for other types of security incidents.

 

data breaches

 

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Crypto

Crypto Mining used by Prowli Malware which Infects over 40,000 Machines

Category : Blog

Crypto Mining used by Prowli Malware which Infects over 40,000 Machines

Crypto currency is a type of digital currency that uses cryptography for security and anti-counterfeiting measures. Public and private keys are often used to transfer cryptocurrency between individuals.

Cyber-criminals have managed to assemble a gigantic botnet of over 40,000 infected web servers, modems, and other IoT devices, which they used for cryptocurrency mining, and for redirecting users to malicious sites.

The campaign called Operation Prowli used various techniques like exploits and password brute-forcing to spread malware and take over devices, such as web servers, modems, and Internet-of-Things (IoT) devices. GuardiCore found that the attackers behind Prowli were focused on making money rather than ideology or espionage.

 

Crypto

 

Crooks deploy cryptocurrency miner, backdoor, SSH scanner

Once servers or IoT devices have been compromised, the Prowli group determines if they can be used for heavy crypto currency mining operations.

Those that can are infected with a Monero miner and the r2r2 worm, a malware strain that performs SSH brute-force attacks from the hacked devices, and helps the Prowli botnet expand with new victims.

Furthermore, CMS platforms that are used to run websites receive special treatment, because they are also infected with a backdoor (the WSO Web Shell).

Crook used this web shell to modify the compromised websites to host malicious code that redirects some of the site’s visitors to a traffic distribution system (TDS), which then rents out the hijacked web traffic to other crooks and redirects users to all sorts of malicious sites, such as tech support scams, fake update sites, and more.

 

Crook

 

A money-making machine

The big picture, according to researchers, is that the entire Prowli operation was intentionally designed and optimized to maximize profits for crooks.

During its lifetime Prowli malware infected over 40,000 servers and devices located on the networks of over 9,000 companies, which it then used to their full potential to earn money before their malware was discovered. Prowli operated without discrimination and made victims all over the world, and regardless of the underlying platform.

 

Prowli malware

 

 

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macOS

All New Privacy and Security Features Coming in macOS 10.14 Mojave

Category : Blog

macOS 10.14 Mojave coming with new Privacy and Security Features

macOS is a series of graphical operating systems developed and marketed by Apple Inc. since 2001. It is the primary operating system for Apple’s Mac family of computers. Within the market of desktop, laptop and home computers, and by web usage, it is the second most widely used desktop OS, after Microsoft Windows.

The new operating system will include a multitude of app redesigns, a new dark mode, and desktop versions of multiple iOS apps. One focus of the new OS is security and privacy. Mac users will now be a bit safer online thanks to these three changes coming to Mojave.

Apple CEO Tim Cook said the new features included in Mojave are “inspired by pro users, but designed for everyone,” helping you protect from various security threats.

 

macOS

 

Safari’s Enhanced “Intelligent Tracking Prevention”

It’s no longer shocking that your online privacy is being invaded, and everything you search online is being tracked—thanks to third-party trackers present on the Internet in the form of social media like and sharing buttons that marketers and data brokers use to monitor web users as they browse.

But not anymore. With macOS Mojave, Safari has updated its “Intelligent Tracking Prevention”—a feature that limits the tracking ability of website using various ad-tracking and device fingerprinting techniques.

The all-new enhanced Intelligent Tracking Prevention will now automatically block all third-party trackers, including social media “Like” or “Share” buttons, as well as comment widgets from tracking users without their permission.

 

safari

 

End-to-End Encrypted Group FaceTime (Up to 32 People)

This is a huge security improvement, as at WWDC 2018, Apple has introduced group FaceTime feature that lets groups of 32 or fewer people do video calls at the same time, which have end-to-end encryption just like the already existing one-to-one audio and video calls and group audio calls.

End-to-encryption for group calls with the Facetime app means that there’s no way for Apple or anyone to decrypt the data when it’s in transit between devices.

 

Encrypted

 

macOS Mojave Will Alert When Your Camera & Mic Are Accessed

As we reported several times in past few years, cybercriminals have now been spreading new malware for macOS that targets built-in webcam and microphone to spy on users without detection.

To address this threat, macOS Mojave adds a new feature that monitors access to your macOS webcam/microphone and alerts you with new permission dialogues whenever an app tries to access the camera or microphone.

This new protection has primarily been designed to prevent malicious software from silently turning on these device features in order to spy on its users.

 

malware

 

Excessive Data Access Request User Permissions

macOS Mojave also adds similar permission requirements for apps to access personal data like mail database, message history, file system and backups.

By default, the macOS Mojave will also protect your location information, contacts, photos, Safari data, mail database, message history, iTunes device backups, calendar, reminders, time machine backups, cookies, and more.

 

Data

 

Secure Password Management

It is a long warned users to deploy a good password practice by keeping their passwords strong and unique for every website or service. Now, Apple has made it easier in macOS 10.14 Mojave and iOS 12.

While Safari in macOS has provided password suggestions for years when users are asked to create a login at a site, Apple has improved this feature in a way that Safari now automatically generates strong passwords, enters them into the web browser, and stores them in the iCloud keychain when users create new online accounts.

Previously, third-party password manager apps have done that much of tasks, and now Apple is integrating such functionalities directly into the next major versions of both macOS and iOS.

 

Secure

 

 

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Drupal

Drupal Sites Over 115,000 Still Vulnerable to Drupalgeddon 2

Category : Blog

Drupal Sites Over 115,000 Still Vulnerable to Drupalgeddon 2

Drupal is a free and open source content-management framework written in PHP and distributed under the GNU General Public License. Drupal provides a back-end framework for at least 2.3% of all web sites worldwide – ranging from personal blogs to corporate, political, and government sites. Systems also use Drupal for knowledge management and for business collaboration.

Two months after the Drupal project released a patch for a highly critical security flaw, there are over 115,000 Drupal sites that have failed to install the fix and are now at the mercy of cyber-criminals.

This estimation comes from Troy Mursch, a US-based security researcher, who spent the last few days scanning the Internet for all sites running a version of the Drupal 7.x CMS.

Mursch was able to find over 500,000 of these sites, and he says that he was able to identify 115,070 websites running an outdated Drupal 7.x CMS version, vulnerable to CVE-2018-7600, also known as Drupalgeddon 2.

 

Drupal

 

Drupalgeddon 2

CVE-2018-7600 is a security flaw that came to light in late March 2018 and was considered one of the most severe security flaws to affect the Drupal CMS since the original Drupalgeddon flaw discovered back in 2014.

The vulnerability allows attackers to take over a site just by accessing a malformed URL, no authentication required. Patches were made available for Drupal 6.x, 7.x, and 8.x versions.

Mursch’s scan didn’t look for 6.x and 8.x sites, but the 500,000 sites he managed to identify and scan are believed to be half of all the Drupal sites deployed online today.

 

Drupalgeddon 2

 

Drupal cryptojacking campaigns have expanded

Hackers started exploiting the Drupalgeddon 2 vulnerability only two weeks after patches came out because most hackers didn’t know how to attack the flaw. Exploitation attempts began soon after the publication of public proof-of-concept code.

Since then, the flaw has been used to infect servers with backdoors, coinminers, cryptojackers, and IoT botnet malware. Mursch himself previously discovered a large cryptojacking campaign using the Drupalgeddon 2 flaw to infect sites’ frontend code with an in-browser miner.

The researcher published a Google Docs spreadsheet at the start of May to track the original campaign, but now, the spreadsheet includes data on several different campaigns and thousands more compromised Drupal sites. With 115,000 of Drupal 7.x sites still without the Drupalgeddon 2 patch, these campaigns have loads of cannon fodder at their disposal.

 

Cryptojacking

 

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RSAT

RSAT Will Automatically Be Reinstalled After New Updates in Next Windows 10 Version

Category : Blog

RSAT

(RSAT) Remote Server Administration Tools enables IT administrators to remotely manage roles and features in Windows Server 2012 R2, Windows Server 2012, Windows Server 2008, and  Server 2008 R2 from a computer that is running Windows 10, Windows 8.1, Windows 8, Windows 7, or Windows Vista.

RSAT allows administrators to run snap-ins and tools on a remote computer to manage features, roles and role services.

 

RSAT

 

RSAT Will Automatically Be Reinstalled After New Updates in Next Windows 10 Version

RSAT is a tool that allows administrators to manage Windows Server from a remote computer running Windows 10. For some time, Administrators have been complaining that when you install a new upgrade of Windows 10, the installed Remote Server Administration Tools would be removed. This is because each version of RSAT is tied to a particular version of Windows and thus you need to download and install the correct version for it to work properly.

In Windows Insider Preview build 17682, Microsoft has made the Remote Server Administration Tools (RSAT) an on-demand software feature. What this means is that once you install RSAT in Windows 10, it will be automatically reinstalled when you install a future operating system update.

Once it is installed as a feature, Windows 10 will automatically reinstall it after you upgrade to another version of the operating system.

 

Windows 10

 

Configuring Remote Server Administration Tools (RSAT) Through Optional Features:

In the future, to set up RSAT as an on-demand software feature, admins can go into the “Manage optional features” settings as shown below. To access this screen, just search for “optional features”.

To add the feature click on the “Add a feature” button. Microsoft will then build a catalog of available features, which may take some time, so please be patient.

Once you install an RSAT tool using this method, it will always be reinstalled when Windows 10 is updated in the future.

As this is currently a new feature in the latest Windows Insider build 17682, you will not see it in the current version of Windows. If you wish to test this feature, you can sign up as a Windows Insider and install the latest build.

 

Server Administration Tools

 

 

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OMB

OMB Releases Damning Report on U.S. Govt’s Inability to Counter Cyber Threats

Category : Blog

OMB Releases Damning Report on U.S. Govt’s Inability to Counter Cyber Threats

OMB (Office of Management and Budget) oversees the implementation of the president’s objectives in the areas of policy, budget, management and regulation. To that end, the recent government-wide cybersecurity risk assessment, carried out by the OMB, in coordination with the Department of Homeland Security (DHS), highlights several serious issues that continue to imperil federal cybersecurity and ultimately put the nation at risk.

The risk report examined federal agencies’ ability to, “identify, detect, respond, and if necessary, recover from cyber intrusions, in accordance with Executive Order 13800.

The OMB and DHS found that 71 of 96 agencies have cybersecurity programs that are either at risk or high risk. The OMB and DHS assessed the performance of 96 agencies across 76 metrics and identified the four core actions they deemed necessary to address cybersecurity risks across the Federal enterprise.

 

OMB

 

Increase cybersecurity threat awareness among Federal agencies by implementing the Cyber Threat Framework to prioritize efforts and manage cybersecurity risks

Thirty-eight percent of federal cyber incident reports lacked an identified attack vector, which means that in roughly 4 out of 10 cyber incidents, it was not known who the attacker was. And, in terms of bolstering communication of cyber risks, just 59 percent of agencies reported having processes in place to communicate cyberrisks across their enterprises.

 

Cyber Threat

 

 

Standardize IT and cybersecurity capabilities to control costs and improve asset management

The report acknowledged that, “an agency’s ability to mitigate security vulnerabilities is a direct function of its ability to identify those vulnerabilities across the enterprise. Agency risk assessments show that this issue becomes more complex in federated agencies, where there are not standardized procedures or technology across the organization is lacking.

Phishing was also addressed, as phishing attacks remain one of the most common attack vectors across both government and industry. The report notes that standardizing and consolidating email at the enterprise level is an important element of the strategy to secure users. But, some federal agencies report having several, separately managed email services inside their agencies. One agency listed 62 separately managed email services used by its staff, which would make it virtually impossible to track and inspect inbound and outbound communications across that agency.

 

cybersecurity

 

Consolidate agency Secure Operations Centers (SOCs) to improve incident detection and response capabilities

A measly 27 percent of agencies reported having the ability to detect and investigate attempts to access large volumes of data. The assessment points out that the current situation is untenable, as agencies lack both the visibility into their networks to determine the occurrence of cybersecurity incidents and the ability to minimize the impact of an incident if one is detected.

 

SOC

 

Drive accountability across agencies through improved governance processes, recurring risk assessments, and OMB’s engagements with agency leadership

With only 16 percent of agencies compliant with the government-wide goal of encrypting data at rest, one of the conclusions arrived at in the report is that there is a lack of accountability for managing risks.

In fact, many have voiced concern over the decision to eliminate these roles and have warned that it will lead to a lack of unified focus against cyber threats.

 

Cyber

 

Conclusion

The report concludes by stating that, “at a time when our reliance on technology is becoming greater and the Nation’s digital adversaries are growing more adept, we must ensure that the Federal Government can secure citizens’ information and deliver on their core missions.”

Next on the agenda, for the OMB, is taking the necessary actions to “implement the Cybersecurity Threat Framework, standardize IT capabilities and tools, consolidate or migrate SOC operations, and drive accountability for cybersecurity risk management across the enterprise.” And, the agency will continue to coordinate with its cross-agency partners, including DHS, NIST and GSA, to ensure that agencies are aware of expectations and available resources. The OMB will also work through the Federal CIO and CISO Councils to ensure that the federal government is moving forward towards improved cybersecurity outcomes.

 

report

 

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