Traditional methods to detect malware and cyber security threats are failing; How artificial intelligence (AI) is used to detect network anomalies and cyber-attacks.
Behavioral Anomaly Recognition
In today’s corporate environments, we are seeing a steady increase in the sophistication of attacks on the customer’s data. Sophisticated worm viruses, ransomware, crypto-jacking, unsecure IoT, and shadow IT are the most prevalent we are seeing. All these attack vectors share a common behavior which can be detected on a network but is often missed in the confusion of log clutter. Utilizing an Autonomous response is there when you can’t be – artificial intelligence that knows what to do and when in order to stop a cyber-threat in its tracks. Having a tool that can detect network behavior to baseline what is normal and what is an upward trend or anomaly is becoming more prevalent in the security in-depth architectures.
Network Behavior Anomaly Detection
Network behavior anomaly detection (NBAD) is the real-time monitoring of a network for any unusual activity, trends, or events utilizing Artificial Intelligence (AI) and Machine Learning (ML). The tools can detect threats and stop suspicious activities in situations where traditional security software is ineffective. NBAD systems sweep the entire network for threat actors. When it detects network behavior that appears out of the ordinary – for example, excessive traffic usage during non-peak hours – it alerts the network team and prompts them to investigate it.
Today, most of the fruitful research and advancements have come from the sub-discipline of AI called machine learning (ML), which focus on teaching machines to learn by applying algorithms to data. Artificial intelligence (AI) is a system that has been taught or learned how to carry out specific tasks without being explicitly programmed how to do so. Often, the terms AI and ML are used interchangeably.
Artificial intelligence and machine learning present a significant opportunity for the cyber security industry. Today, new machine learning methods can vastly improve the accuracy of threat detection and enhance network visibility thanks to the greater amount of computational analysis they can handle. They are also heralding in a new era of autonomous response, where a machine system is sufficiently intelligent to understand how and when to fight back against in-progress threats.
The application of artificial intelligence to the cyber defense challenge has marked a fundamental shift in our ability to protect critical data systems and digital infrastructures. For strained security teams, it offers the possibility to keep pace with an ever-evolving threat landscape. By constantly observing your network behavior, you can be assured that you aren’t just preventing security-related catastrophes, but you’ve also locked down your network’s overall security.
–Rick Beaupre, Advanced Security Solutions Architect
Interested in starting an assessment? Aqueduct offers Threat and Vulnerability Assessments to help identify host-based vulnerabilities, network risks and threats, and provide exposure analytics. To get started, submit an inquiry through our contact us form or contact your Aqueduct Account Manager.
Still interested in learning more about our security portfolio? View our solutions page here.