Behavior-Based Malware Detection in Enterprise Networks Using Artificial Intelligence

Malware attacks have always been regarded as a concern, but today, we should be far more worried about them gaining ground, especially on enterprise networks. Although signature-based techniques have witnessed much success in identifying malware, they can only partially encompass these changing threats. This calls for novel malware detection techniques to identify and neutralize such attacks accurately. In the study, we present a behavior-oriented AI-based malware detection system to identify and prevent malware attacks in enterprise networks. We use artificial intelligence algorithms that analyze the actions of network traffic, applications and devices to identify and classify malicious activity. Large datasets of previous malware behaviors are used to train the AI algorithms to determine new threats, even if the malware has never been seen before. In addition, it can also learn and update the algorithm completely by discovering the latest malware behavior. Our behavior-based detection allows our system to see zero-day attacks and malware variants that standard signature-based detection systems have not seen yet. This significantly improves the security of enterprise networks while minimizing data breaches and financial losses. Our behavior-based malware detection system provides a tailored protection capability against the emerging threat landscape to secure enterprise networks, which is a delegated preventive plan against emergent threats instead of reactive measures. It harnesses the power of AI and adapts to new data to offer a comprehensive layer of protection against malware.