Malware detection and prevention API is an umbrella term for a diverse set of intrusive software programs that infiltrate and exploit computer systems, networks, and end-user devices [1]. These malevolent software entities are capable of causing substantial financial loss and reputational damage to organizations and individuals. Malware detection and prevention is a key part of cybersecurity that requires advanced techniques beyond traditional firewalls and basic antivirus software.

Detecting malware relies on an understanding of how malicious software behaves, which is difficult to do with static analysis alone. Traditional signature-based malware detection uses technical indicators such as file hashes, domains, and IP addresses contacted by the executable, or strings within the code to identify potential threats. However, this type of detection can miss zero-day and new malware variants, leading to high false positive rates.

Malware Prevention and Detection with File Scanning APIs

To address the limitations of signature-based detection, behavioral detection utilizes AI to model normal behavior and identify anomalies. This approach provides more accurate and reliable results than traditional heuristic detection, which uses a rules engine to look for unusual activities in a process. However, it can take a long time to build and update this model for each new threat.

To enhance the capabilities of dynamic malware detection, we developed an API to analyze and dissect the behavior of PE files based on their sequence of API calls. The API call sequences from malware and benign PE files, retrieved from the ‘calls’ element in Cuckoo Sandbox reports, are used to identify features that distinguish malware from benign executables. The API call sequence data includes socket descriptors, a variety of network data (such as command-and-control communications and exfiltrated information), and file attributes such as entropy and import table.