How to Extract the Most Relevant Cyberthreat News From Darknet Articles
Among the many types of cyberthreat news, cyberattacks offer valuable insights into the most recent cutting edge hacking tools, techniques, and malware. As such, cybersecurity practitioners, vendors, and hackers publish articles, blog posts, and forum discussions on these topics. Hence, a thorough understanding of the latest and greatest in cyber security is imperative.
One of the best sources of information for the CTI buff is the darknet, a nefarious cyberspace abounding with hacking tools, malware, and documents. Cybersecurity researchers have a myriad of tools and techniques to extract the most relevant information from darknet articles. Fortunately, these methods are not all that difficult.
The simplest method is to manually sift through articles and forum discussions. This may be done using the aforementioned semi-structured format, or by employing an automated solution such as a machine learning technique. It is also possible to extract the relevant information from social media and news aggregators.
The next step is to combine the relevant information from the various sources. This may involve using clustering algorithms to cluster similar text segments into meaningful clusters. Clustering methods include hierarchical, ainity propagation, k-means, and ainity-propagation-like techniques.
The above process will yield information about the most recent cyberattacks, including information about the number of targets, attack distribution, and attack mitigation guidelines. This information is useful in developing cyberthreat alerts, which can be created based on user-defined rules. Moreover, it is also possible to use the information to build better tools for defense against malicious cyberattacks.