Abstract: Detecting cyber threat and malware, particularly zero-day malware is a major challenge for the security community. Signature-based methods of cyber threat and malware detection are unable to detect zero-day malware. In order to detect zero-day malware and cyber threat which may have more severe impacts, a system called Compromised Detection System (CDS) and a method thereof is disclosed. The CDS uses a sophisticated approach and method based on Machine Learning to detect anomalies on the network behavior. By such approach, CDS is able to detect unknown cyber threat and malware (aka zero day)since they will present a deviation from the normal behavior in the network.

Abstract: Detecting cyber threat and malware, particularly zero-day malware is a major challenge for the security community. Signature-based methods of cyber threat and malware detection are unable to detect zero-day malware. In order to detect zero-day malware and cyber threat which may have more severe impacts, a system called Compromised Detection System (CDS) and a method thereof is disclosed. The CDS uses a sophisticated approach and method based on Machine Learning to detect anomalies on the network behaviour. By such approach, CDS is able to detect unknown cyber threat and malware (aka zero day)since they will present a deviation from the normal behaviour in the network.