Malicious IP List – How Machine Learning Improves Malicious Activity Detection
The entire Internet is scanned, attacked, and probed by hackers, bots, and other bad actors twenty-four hours a day. Many organizations and tools create blacklists of IP addresses that have been confirmed to be malicious. These blacklists are then used by firewalls and cybersecurity solutions to block incoming traffic from these suspect sources. However, bad actors are constantly changing their IP addresses. This makes relying on static lists unreliable and can result in false positives.
As a result, Malicious IP List has opted to use machine learning and analytics to detect suspicious behavior and IPs. By using behavioral analysis to identify and detect a variety of attacks, like ransomware, targeted financial cybercrime or even cyber-espionage, models can detect activity that is more indicative of a threat than simple blacklisting.
Guarding Against Malicious IPs: Malicious IP List
Unlike a blacklist which may only contain a few known bad IPs, a model can be trained to understand the physical infrastructure of networks – specifically the Internet Service Providers (ISP) that manage a range of network prefixes. By identifying the ISP and mapping the IPs to it, a model can understand that clusters of high malicious activities are likely tied to a particular ISP. This information is then added to the detection framework, improving results compared to blacklisting alone.
By leveraging the power of consortium real time data we’ve improved the way we detect malicious activity. Using machine learning and big data we’ve built an understanding of the whole Internet to identify the relationships and associations between different IPs. This enables us to understand how different segments of the network are linked together, and detect new types of attacks that can be combined into complex campaigns.
