Security News

Adversarial machine learning is a technique aimed at deceiving the ML model by providing specially crafted input to fool the AV into classifying the malicious input as a benign file and evade detection. There is great impetus to expand the knowledge that we have not just on the machine learning models that we use, but the adversarial attacks made against them.

"Despite thousands of cybersecurity products, data breaches are at an all-time high," writes Bishop in his sponsored VentureBeat article To protect people, we need a different type of machine learning. It has the ability to look at historical data and calculate important features by aggregating all of the relevant data points which are then passed to the machine learning model.

Why does machine learning matter? Machine learning systems are able to quickly apply knowledge and training from large data sets to excel at facial recognition, speech recognition, object recognition, translation, and many other tasks. What machine learning tools are available? Businesses like IBM, Amazon, Microsoft, Google, and others offer tools for machine learning.

Calligo launched the only Machine Learning Service to simultaneously address the key obstacles to SME and enterprise adoption of machine learning: cost, data quality, complexity, security, accuracy and data privacy. "Too many businesses have been hesitant to take advantage of machine learning because of the cost and lack of internal expertise required to interpret and use data -and especially to do so safely."

Abnormal is disrupting the market by using AI to reinvent email security. The company is using the new funding to double the size of its machine learning and data science teams to further extend Abnormal's lead as the most effective AI threat detection engine for enterprise email security.

Threat Stack announced ThreatML, its new machine learning engine that enhances security observability for the Threat Stack Cloud Security Platform, Threat Stack Oversight, and Threat Stack Insight with anomaly detection. The Threat Stack Cloud Security Platform collects, normalizes, and analyzes over 60 billion events per day from customer cloud infrastructure and applications.

A report published last year has noted that most attacks against artificial intelligence systems are focused on manipulating them, but that new attacks using machine learning are within attackers' capabilities. Microsoft now says that attacks on machine learning systems are on the uptick and MITRE notes that, in the last three years, "Major companies such as Google, Amazon, Microsoft, and Tesla, have had their ML systems tricked, evaded, or misled." At the same time, most businesses don't have the right tools in place to secure their ML systems and are looking for guidance.

Microsoft, in collaboration with MITRE, IBM, NVIDIA, and Bosch, has released a new open framework that aims to help security analysts detect, respond to, and remediate adversarial attacks against machine learning systems. Just as artificial intelligence and ML are being deployed in a wide variety of novel applications, threat actors can not only abuse the technology to power their malware but can also leverage it to fool machine learning models with poisoned datasets, thereby causing beneficial systems to make incorrect decisions, and pose a threat to stability and safety of AI applications.

Microsoft and MITRE, in collaboration with a dozen other organizations, have developed a framework designed to help identify, respond to, and remediate attacks targeting machine learning systems. The Adversarial ML Threat Matrix, which Microsoft has released in collaboration with MITRE, IBM, NVIDIA, Airbus, Bosch, Deep Instinct, Two Six Labs, Cardiff University, the University of Toronto, PricewaterhouseCoopers, the Software Engineering Institute at Carnegie Mellon University, and the Berryville Institute of Machine Learning, is an industry-focused open framework that aims to address this issue.

DEF CON is perhaps the ultimate "Come one/come all" hackers' convention, now in its 28th year, and it famously takes place in Las Vegas each year in a fascinating juxtaposition with Black Hat USA, a corporate cybersecurity event. The DEF CON Villages are breakout zones at the event where where likeminded researchers gather to attend talks and discussions in research fields all the way from Aerospace, Application Security and AI to Social Engineering, Voting Machines and Wireless.