Cyberwarfare


Why AI is crucial to cyber security

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This should include social engineering training and the use of AI/machine learning in your environment. Then, add a tool that gives you a holistic view of your entire network in real time that identifies advanced threats, including those stealthy, unconventional, silent attackers. Whereas cyber attackers in years past have struck quickly and loudly as part of a virtual sneak attack, today's cyber criminals are taking it much more slowly and methodically. Threat detection is certainly a main focus of today's AI and machine learning technology push.


Five myths about machine learning in cybersecurity

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It can solve some creative tasks – recognize images, predict the weather, play chess, etc. In the majority of spheres where machine learning is used, the objective is not changing with time, while in the case of malware things are changing constantly and rapidly. Obviously, with rapidly modifying malware, a security solution based on a model without an antivirus database is worthless. However, it doesn't work like that, because the number of malware samples passing through the computer of an average client is much smaller than the amount of malware samples collected by an antivirus lab system.


Artificial Intelligence: Friend or Enemy of Cybersecurity? - Learning Simplify

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This technology provides extended visibility across the entire distributed network and enables integrated security solutions to automatically adapt to changes in network configurations and change needs with a synchronized response against threats. Improving the quality of intelligence against threats is extremely important as IT teams increasingly transfer control to artificial intelligence to perform work that they otherwise should do. These work relationships will really make artificial intelligence and machine learning applications for cyber defense really effective. Because there is still a shortage of talent in cybersecurity, products and services must be developed with greater automation in order to correlate intelligence against threats and thus, determine the level of risk to synchronize a coordinated response automatically.


Machine learning in cybersecurity is coming to IAM systems

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Several experts at the 2017 Cloud Identity Summit this week discussed machine learning in cybersecurity applications for identity management systems, as well the risks and rewards of such applications. And to give an idea of the volume of activity that identity management systems are dealing with today, Simons said his company sees 115.5 million blocked log in attempts and 15.8 million takeover attempts for Microsoft accounts each day. Maass said if the baseline for good behavior is set incorrectly, then the identity management systems will learn incorrectly and make mistakes. Dholakia cited another potential problem for machine learning-powered IAM: Continuous and possibly endless accumulation of data for identity management systems will make machine learning in cybersecurity applications increasingly complex and harder for actual human identity professionals to manage.


Malwarebytes Introduces Enterprise Cloud Platform for Next-Gen Endpoint Protection, Announces Validation as Replacement for Antivirus - Malwarebytes Press Center

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An easily deployed and managed cloud solution with machine learning capabilities gives businesses scalable endpoint protection against today's growing threats." Malwarebytes Endpoint Protection, built on the Malwarebytes platform, is an endpoint security solution featuring layers of detection technologies with a unified endpoint agent. Malwarebytes' machine learning approach, powered by the new Anomaly Detection layer, provides real-time, signature-less detection against new and unknown threats by modeling known trusted files rather than attempting to model historical malware samples. Integrated into the layered approach of detection techniques for both pre- and post-execution, Malwarebytes Endpoint Protection provides the most effective security solution for endpoints.


The Pitfalls of Hunting Cyber Threats with AI - CBR

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In threat trapping, passive technologies identify malware using models of bad behavior like signatures. Unfortunately, developing accurate malware detection products based on good behavior modeling is not easy. But no company has enough human resources to manually evaluate a large number of alerts about possible security threats. When AI applies both bad and good behavior models, it reduces the number of false positives to a manageable amount.


World's Most Powerful Particle Collider Taps AI to Expose Hack Attacks

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CERN's cybersecurity department is training its AI software to learn the difference between normal and dubious behavior on the network, and to then alert staff via phone text, e-mail or computer message of any potential threat. In anticipation of that type of growth the laboratory in 2002 created its Worldwide LHC Computing Grid, which connects computers from more than 170 research facilities across more than 40 countries. Jarno Niemelä, a senior security researcher at F-Secure, a company that designs antivirus and computer security systems, says CERN's use of machine learning to train its network defenses will give the lab much-needed flexibility in protecting its grid, especially when searching for new threats. The first test will be protecting the portion of the grid used by ALICE (A Large Ion Collider Experiment)--a key LHC project to study the collisions of lead nuclei.


Can AI and Machine Learning Help Fill the Cybersecurity Skills Gap?

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Trained human operators are needed for the most difficult tasks, and the advance of AI and machine learning will lead to increased effectiveness. In the short term, I don't think AI can truly fill the cybersecurity skills gap. The advance of AI and machine learning will continue to improve cycles in the cybersecurity domain. It is essential for future cybersecurity workers to quickly learn these crucial skills for the industry's future jobs.


Q&A: How artificial intelligence is changing the nature of cybersecurity

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With the rise of cloud-based apps and the proliferation of mobile devices, information security is becoming a top priority for both the IT department and the C-Suite. Businesses ranging from startups to large corporations are increasingly looking to new technologies, like artificial intelligence (AI) and machine learning, to protect their consumers. For cybersecurity, AI can analyze vast amounts of data and help cybersecurity professionals identify more threats than would be possible if left to do it manually. But the same technology that can improve corporate defences can also be used to attack them.


Majority of Execs Embrace AI, Machine Learning

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Some 57% of executives report trusting automated systems that employ AI and machine learning as much or more than humans to protect their organizations. Two in five (38%) executives indicated that within two years, automated security systems would be the primary resource for managing cybersecurity. This year's survey respondents affirmed that their organizations are actively integrating digital technologies--and that cybersecurity is the number-one driver of their digital transformation. "Today's educated consumer is keenly aware of security--as customer experience is now closely tied with reputation management and data protection.