Role of AI and ML in Advanced Cyber Threat Detection

Introduction

As cyber threats are becoming increasingly advanced these days and the cyber threat landscape for organizations is increasing enormously, there is a need to leverage advanced technologies like artificial intelligence and machine learning to detect such advanced cyber threats. Latest day malware and other malicious software can enter the target networks and move laterally inside the network without getting detected by the traditional signature-based anti-malware and intrusion detection tools. Moreover, malware these days can use advanced techniques to randomly change the trivial indicators of compromise and leave a large number of noise log traces, making it almost impossible to detect. The best way to counter such unknown malware activities inside your network is to detect any abnormal behavior inside the network. This can be achieved using artificial intelligence and machine learning techniques.

Advantages of AI & ML in Threat Detection

Traditional threat detection systems identify cyber threats based on their signatures. If the signature of the malicious file or activity matches the signature in the tool’s threat database, then only the tool can identify a threat. This approach does not work effectively with modern-day threats. These days, threats can easily bypass traditional threat detection systems and infect the target networks. In addition, the traditional signature-based threat detection tools fail to detect zero-day attacks and unknown threats in real time. Moreover, the threat database in traditional tools has to be regularly updated to detect the latest threats.

Artificial Intelligence and Machine Learning algorithms are complex mathematical models. These algorithms recognize patterns and make logical inferences based on statistics and probability. AI and ML include complex algorithms and techniques including clustering, neural networks, and deep learning methods which can effectively detect anomalies or abnormal behavior in any large networks or computer systems.

AI & ML can help in:

• Detecting threats in real-time
• Detecting unknown threats and zero-day attacks
• Detecting threats based on the behavior and pattern
• Prioritizing cyber threats relevant to your organization

ThreatResponder’s Machine Learning Engine For Advanced Cyber Threat Detection

Cyber security threats and ransomware attacks are increasing at a tremendous pace. It is extremely difficult for cyber security analysts and incident responders to investigate and detect cyber security threats using conventional tools and techniques. NetSecurity’s ThreatResponder, with its cloud-based machine learning threat detection engine and its diverse capabilities, can help your team detect the most advanced cyber threats, including APTs, zero-day attacks, and ransomware attacks. It can also help automate incident response actions across millions of endpoints, making it easy, fast, and hassle-free.

Want to try our ThreatResponder, cutting-edge Endpoint Detection & Response (EDR), and ThreatResponder FORENSICS, the Swiss knife for forensic investigators in action? Click on the below button to request a free demo of our NetSecurity’s ThreatResponder platform.

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