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Cylance brings AI and machine learning to anti-virus protection

#artificialintelligence

Right now, nearly 1 billion malware programs are out there, accounting for more than 7 billion attacks last year. Considering that overwhelming volume of new threats, users need to be confident that their anti-virus software is up to the task. Unfortunately, the problem with many of the old-school legacy anti-virus systems is that they aren't. Cylance Smart Antivirus takes a different approach to protecting computers from viruses, malware, and other online threats. Their new world order weaponizes artificial intelligence and machine learning to safeguard your computer and wipe out potential threats before they infect your systems.


Open-source software: How many bugs are hidden there on purpose?

ZDNet

Microsoft-owned GitHub, the world's largest platform for open-source software, has found that 17% of all vulnerabilities in software were planted for malicious purposes. GitHub reported that almost a fifth of all software bugs were intentionally placed in code by malicious actors in its 2020 Octoverse report, released yesterday. Proprietary software makers over the years have been regularly criticized for'security through obscurity' or not making source code available for review by experts outside the company. Open source, on the other hand, is seen as a more transparent manner of development because, in theory, it can be vetted by anyone. But the reality is that it's often not vetted due to a lack of funding and human resource constraints.


AI and Cybersecurity: What's the deal

#artificialintelligence

Artificial intelligence is probably the future of security software regarding how many processes it can improve and how little resources it requires. Positively, it will be integrated into the advanced antivirus programs and take on more and more features. Although not all the antiviruses have AI integrated, it is still essential to protect personal gear and information from intruders and hacker attacks. If you need to find a porter antivirus, read professional and common user reviews. This way, you'll be able to see how good is AVG antivirus, Avast, or any other one, before AI can handle all the security processes. So, for starters, artificial intelligence can be classified into two types.


Artificial Intelligence for Smarter Cybersecurity

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Organizations continue to embrace the Internet of Things (IoT), the cloud, and mobile technology. This has influenced considerable changes in the threat landscape and created more vulnerability points. Cybercriminals are leveraging these new vulnerability points to develop and launch sophisticated, high-volume, multi-dimensional attacks. Such attacks mean that data is at risk, and organizations must analyze potentially malicious files. Using artificial intelligence software, organizations can process large volumes of threat data and adequately prevent and respond to breaches and hacks.


A Large-Scale Database for Graph Representation Learning

arXiv.org Artificial Intelligence

With the rapid emergence of graph representation learning, the construction of new large-scale datasets are necessary to distinguish model capabilities and accurately assess the strengths and weaknesses of each technique. By carefully analyzing existing graph databases, we identify 3 critical components important for advancing the field of graph representation learning: (1) large graphs, (2) many graphs, and (3) class diversity. To date, no single graph database offers all of these desired properties. We introduce MalNet, the largest public graph database ever constructed, representing a large-scale ontology of software function call graphs. MalNet contains over 1.2 million graphs, averaging over 17k nodes and 39k edges per graph, across a hierarchy of 47 types and 696 families. Compared to the popular REDDIT-12K database, MalNet offers 105x more graphs, 44x larger graphs on average, and 63x the classes. We provide a detailed analysis of MalNet, discussing its properties and provenance. The unprecedented scale and diversity of MalNet offers exciting opportunities to advance the frontiers of graph representation learning---enabling new discoveries and research into imbalanced classification, explainability and the impact of class hardness. The database is publically available at www.mal-net.org.


How artificial intelligence is changing cyber security

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Having contact with someone who has a cold increases the chances that you might pick up the bug yourself. In much the same way, businesses adding more connectivity into their system increases the opportunities for cyber criminals to introduce viruses into the system. Here, Sophie Hand, UK Country Manager at EU Automation, explains the role Artificial Intelligence (AI) can play in combatting cyber crime. Even with the most effective preventative measures in place, cyber criminals try to get around them. It is unlikely that we will ever completely eradicate cyber threats because hackers are intelligent and tenacious, always searching for new ways to breach a company's defences.


Avast warns of Minecraft skin, mod apps fleecing 'millions' of Android users

ZDNet

Active Minecraft modding apps on Google Play are fleecing subscribers through hefty payment models, researchers have warned. Malicious mobile apps can come in many forms. Some iOS or Android apps may have Trojan code embedded and waiting to steal your online credentials; others are considered spyware as they can monitor calls, message logs, GPS data, and online activity; whereas nuisanceware plagues users with pop-up ads designed to generate fraudulent revenue for operators. Fleeceware can be classified under the same umbrella. While not necessarily dangerous, fleeceware apps can still deprive unwitting users of their hard-earned cash by providing poor goods or services through extortionate, automatic subscriptions. Gaming is a popular arena for fleeceware as add-on skins, wallpapers, virtual items, and mods may be highly sought by dedicated users.


How Security Systems are Implementing AI and ML for Threat Detection

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A recent study showed that over 90% of security operating centres are now implementing or considering the use of AI and machine learning to detect and defend against digital threats. What is the traditional method for threat detection, what has AI and ML allowed, and how is the hardware world reacting to threats? Since their introduction, computers have played a key role in modern life, providing services such as internet access, online banking, message exchange, and remote work. However, the transmission of sensitive information along with the processing capabilities of any single computer has also resulted in the development of malware by cybercriminals. These programs fall under several categories, including viruses, trojans, and worms, all of which perform different tasks. Of these, their exact function can be separated further; some malware works to destroy a system while others may steal sensitive information.


Three Things to Consider in Emerging AI and ML Cybersecurity Landscape

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Cyber threats continue to escalate in both sophistication and volume. Traditional approaches to threat detection, however, are no longer sufficient to ensure protection. Correspondingly, machine learning (ML) has proven highly effective at identifying and warding off cyber attacks. Machine learning's power is the result of three factors: data, compute power and algorithms. Due to its very nature, the cyber field produces substantial amounts of data.


Data Mining vs. Machine Learning: What's The Difference?

#artificialintelligence

Data mining isn't a new invention that came with the digital age. The concept has been around for over a century but came into greater public focus in the 1930s. According to Hacker Bits, one of the first modern moments of data mining occurred in 1936, when Alan Turing introduced the idea of a universal machine that could perform computations similar to those of modern-day computers. Forbes also reported on Turing's development of the "Turing Test" in 1950 to determine if a computer has real intelligence or not. To pass his test, a computer needed to fool a human into believing it was also human. Just two years later, Arthur Samuel created The Samuel Checkers-playing Program that appears to be the world's first self-learning program.