Cambridge-based Darktrace, backed by one-time Autonomy chief exec Mike Lynch, uses machine learning and AI technology to protect corporate networks against cyber threats through what it markets as an "Enterprise Immune System". Last year's funds were used to drive this growth, but this latest investment is reportedly being put towards Latin America and Asia Pacific, as Darktrace continues to fulfil its global ambitions." In a statement, Darktrace said it now has over 3,000 deployments worldwide, across all industry sectors, including global financial companies, telecommunications providers, media firms, retailers, healthcare providers, government agencies and critical national infrastructure facilities. Darktrace claims that its technology is the only machine learning technology to "detect and fight against in-progress threats in real time".
When artificial intelligence (AI) is discussed today, most people are referring to machine learning algorithms or deep learning systems. The first (non-targeted adversarial attack) involves getting algorithms to confuse a machine learning system so it won't work properly. "It's a brilliant idea to catalyze research into both fooling deep neural networks and designing deep neural networks that cannot be fooled," Jeff Clune, a University of Wyoming assistant professor whose own work involves studying the limits of machine learning systems, told the MIT Technology Review. "Computer security is definitely moving toward machine learning," Google Brain researcher Ian Goodfellow told the MIT Technology Review.
Financial institutions are increasingly deploying Robotic Process Automation (RPA) and other early-stage AI technologies to the front lines, identifying the behavior of trustworthy users and detecting emerging threats. We are beginning to see both offense and defense using automation, machine learning and artificial intelligence (AI) to counter each other's moves. AI-supported visualization is becoming a core element of enterprise cybersecurity strategy, helping cyber defense teams harness and amplify humans' ability to zero in on patterns quickly and pick out anomalies. Financial institutions have historically held the upper hand as they benefit from enterprise-wide investment strategies in artificial intelligence (AI) and machine learning.
More cyberattacks will leverage machine learning to make more autonomous malware, more efficient fuzzing for vulnerabilities, etc. Semi-supervised learning and one-shot learning will reduce the amount of data needed to train several kinds of models and make AI use more widespread. More cyberattacks will leverage machine learning to make more autonomous malware, more efficient fuzzing for vulnerabilities, etc. Semi-supervised learning and one-shot learning will reduce the amount of data needed to train several kinds of models and make AI use more widespread.
UEBA uses machine learning and data science to gain an understanding of how Users (humans) and Entities (machines) within an environment typically behave. Then, by looking for risky, anomalous activity that deviates from normal behaviour, UEBA helps identify cyber threats. BS: All of the biggest data breaches, judged either by number of records breached or the importance of the data stolen, have involved attackers leveraging stolen user credentials to gain access. Businesses need UEBA because their existing threat detection tools are unable to detect hackers that are leveraging stolen, but valid, user credentials.
The artificial intelligence technology is deployed by cybersecurity firms in an effort to keep pace with the evolution of cyberattacks, as machine learning algorithms are able to improve predictability the more it is used. But according to Guy Caspi, CEO of cybersecurity company Deep Instinct, machine learning is no longer enough in an age of unprecedented evolution and volume of cybercrime. Part of that is because machine learning relies on only two or three algorithms; deep learning deploys tens of algorithms, and complex math. But the ongoing evolution of corporate cybercrime means cybersecurity companies may no longer be able to afford relying solely on machine learning.
A new competition heralds what is likely to become the future of cybersecurity and cyberwarfare, with offensive and defensive AI algorithms doing battle. "It's a brilliant idea to catalyze research into both fooling deep neural networks and designing deep neural networks that cannot be fooled," says Jeff Clune, an assistant professor at the University of Wyoming who studies the limits of machine learning. Machine learning, and deep learning in particular, is rapidly becoming an indispensable tool in many industries. "Adversarial machine learning is more difficult to study than conventional machine learning--it's hard to tell if your attack is strong or if your defense is actually weak," says Ian Goodfellow, a researcher at Google Brain, a division of Google dedicated to researching and applying machine learning, who organized the contest.
Many cybersecurity companies are starting to invest or implement AI in their cybersecurity solutions and it is giving their security teams a significant boost, according to a recently released report commissioned by McAfee. Cybercriminals are starting to use these solutions to sift through large amounts of data to "classify victims that have weaker defenses" so they can get the maximum "return on their investment," Steve Grobman, chief technology officer for McAfee, told Bloomberg BNA. Grobman told Bloomberg BNA that AI and machine-learning won't replace cybersecurity teams, rather "it will change the way that cybersecurity professionals will do their jobs." To keep up with the constantly evolving world of privacy and security sign up for the Bloomberg BNA Privacy and Security Update.
The Singapore Government is stepping up investments in artificial intelligence (AI) to better counter cyber threats from hackers who are also increasingly using AI to vary their strategies. A huge part of the security budget will go to the first Government Security Operations Centre, which will feature AI and the analytics smarts to detect cyber threats. NRF will invest up to $150 million over five years in this new initiative, dubbed AI.SG. The cross-government initiative will involve six agencies: NRF, the Smart Nation and Digital Government Office, the Economic Development Board, the Infocomm Media Development Authority, SGInnovate and Integrated Health Information Systems.
Dr. Eli David is one of the leading global experts in the field of computational intelligence, specializing in deep learning (neural networks) and evolutionary computation. He has published more than thirty papers in leading artificial intelligence journals and conferences, mostly focusing on applications of deep learning and genetic algorithms in various real-world domains. For the past ten years, he has been teaching courses on deep learning and evolutionary computation at Bar-Ilan University, in addition to supervising the research of graduate students in these fields. Dr. David received the Best Paper Award in 2008 Genetic and Evolutionary Computation Conference, the Gold Award in the prestigious "Humies" Awards for Human-Competitive Results in 2014, and recently the Best Paper Award in 2016 International Conference on Artificial Neural Networks.