Malware detection is an integral component of computer security strategies, helping prevent malicious software from infiltrating networks and damaging files, databases or personal information.
Finding malware can be challenging in an ever-evolving digital landscape. One tried-and-tested way of detecting it is using signatures; these compare files against vast cloud-based databases of known profiles of known malicious files to quickly detect new infections.
However, although this approach has proven its efficacy for years, it also comes with risks. False positives may occur as it detects non-malicious code which shares similar attributes to known malware.
An anomaly-based detection technique offers an effective means of spotting malware. This approach leverages machine learning to examine suspicious processes' behavior. A threshold for abnormal activity then creates and flags any processes crossing that threshold as possible malicious.
Wazuh File Integrity Monitoring (FIM) module is an example of such an approach, using hashes from various threat intelligence sources like CDB lists and YARA scans to detect malicious files on monitored endpoints.
Artificial intelligence and machine learning are powerful tools for identifying suspicious behaviors and characteristics, but are vulnerable to manipulation by hackers who feed these tools with specially-crafted artifacts.
Businesses have recognized this reality and are turning towards a comprehensive strategy for protecting networks against advanced malware. It involves employing technologies to continuously detect new infections as well as having an ability to swiftly respond when attacks do happen.
