The answer, much like the outputs derived through machine learning algorithms, is neither black nor white. The promise of machine learning in cybersecurity lies in its ability to detect as-yet-unknown threats, particularly those that may lurk in networks for long periods of time seeking their ultimate goals. Machine learning technology does this by distinguishing atypical from typical behavior, while noting and correlating a great number of simultaneous events and data points. But in order to know what constitutes typical activity on a website, endpoint or network at any given time, the machine learning algorithms must be trained on large volumes of data that have already been properly labelled, identified or categorized with distinguishing features that can be assigned and reassigned relative weights. While this may sound logical, machine learning technology is a darker black box than most.
Hewlett-Packard's Aruba unit has announced the launch of Aruba 360 Secure Fabric, an enterprise security solution to protect businesses from cloud-borne, mobile, and IoT-based threats. On Monday, the enterprise firm said the new offering is a security framework for analytics-based attack detection and response. As part of the Aruba IntroSpect product family, 360 Secure Fabric uses User and Entity Behavioral Analytics (UEBA) to focus on how enterprise players can reduce the risk of insider-driven issues and lapses in security. According to Bitglass, one in three companies admitted to experiencing a data breach caused by an insider between 2015 and 2016, and 74 percent out of 500 companies feel vulnerable to insider threats. "To help organizations address new and unknown threats, the Aruba 360 Secure Fabric offers security and IT teams an integrated way to quickly detect and respond to advanced cyberattacks from pre-authorization to post- authorization across multi-vendor infrastructures, supporting enterprises of all sizes," the company says.
Cybereason said it would provide cybersecurity tools that use big-data algorithms to canvass all the devices on a network in real time for suspicious behavior--such as a thermostat sending communications to a computer it has never interacted with before. Arm, whose chips are in nearly all smartphones, will offer services to manage internet-connected devices and their data. "If you look, you will find vulnerability in every device out there," said Lior Div, CEO of Cybereason and a former cyberoperative at Unit 8200, Israel's equivalent of the U.S. National Security Agency. "Hackers will use whatever they can." The two companies expect the offerings to be available in products by the first half of next year and aim to monitor 1 trillion devices by 2035.
As businesses struggle to combat increasingly sophisticated cybersecurity attacks, the severity of which is exacerbated by both the vanishing IT perimeters in today's mobile and IoT era, coupled with an acute shortage of skilled security professionals, IT security teams need both a new approach and powerful new tools to protect data and other high-value assets. Increasingly, they are looking to artificial intelligence (AI) as a key weapon to win the battle against stealthy threats inside their IT infrastructures, according to a new global research study conducted by the Ponemon Institute on behalf of Aruba, a Hewlett Packard Enterprise company HPE, 1.66% This press release features multimedia. The Ponemon Institute study, entitled "Closing the IT Security Gap with Automation & AI in the Era of IoT," surveyed 4,000 security and IT professionals across the Americas, Europe and Asia to understand what makes security deficiencies so hard to fix, and what types of technologies and processes are needed to stay a step ahead of bad actors within the new threat landscape. The research revealed that in the quest to protect data and other high-value assets, security systems incorporating machine learning and other AI-based technologies are essential for detecting and stopping attacks that target users and IoT devices.
Not even Cersei Lannister's scheming or Sir Jorah's father-like protectiveness could have prevented attackers from breaching HBO's network and stealing 1.5 terabytes of data (including unreleased Game of Thrones episodes). Machine learning, however, may have offered a more sound defense of HBO's virtual fortress. Artificial intelligence (AI) and machine learning (ML) are the topics of much debate, especially within the cybersecurity community. Is machine learning the next big security frontier? Is AI ready to take on machine learning-driven attacks?