To explain how Vectra uses AI to automate the detection of cyber attackers and speed-up incident response, CTO Oliver Tavakoli talks about data science, machine learning techniques, deep learning and more at Infosecurity Europe.
The evolution of the threat environment has already changed the dynamics of attack and defense enough to turn a litany of once radically negative assumptions into routine advice: Consider a breach as inevitable—perimeter protections will fail, and attackers will get in and stay in until their mission is accomplished, which could take months.
This white paper focuses on how to protect data centers from cyber attacks. It looks at the unique architectural and operational challenges of cyber security in the data center, examines real-world techniques and attacks from the wild, and proposes a framework for defending against them.
This white paper examines obstacles that enterprises face in combating cyber attacks, and how artificial intelligence is essential to modern security operations centers. AI can augment SOC teams to make operations more efficient, as well as detect the early signs of attacks in real time before key assets are stolen or damaged.
The Vectra AI approach to threat detection blends human expertise with a broad set of data science and advanced machine learning techniques. This model delivers a continuous cycle of threat intelligence based on cutting-edge research, global and local learning models, deep learning, and neural networks.
Staying ahead of ransomware threats is where organizations want to be because these insidious attacks are not going away. In fact, they are likely to become even more prevalent within organizations. The criminal appetite for juicy payouts and limited risk are just too big to ignore.
Signatures are great at catching large-scale commodity threats. But to stop targeted attacks, you need to jump off the signature hamster wheel and lay in wait where attackers will inevitably show up – inside your network.
Covert communications are key enablers of cyber attacks that allow remote humans to patiently manage and direct their attacks undetected. Attackers choose these vehicles specifically for their ability to evade signatures, malware sandboxes and reputation lists.
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Insider threat cases make up 28% of all cybercrime and more than a third of organizations reported an insider cyber attack in 2013, and 32% of affected organizations said that the damage caused by insider cyber attacks was greater than outsider attacks.
Prevention security at the network perimeter provides one imperfect chance to stop an attack. SOC teams need automated threat detection and prioritized risk reporting that show what attackers are doing and provide multiple opportunities to stop an attack.