Static malware analysis is well-suited to endpoint anti-virus systems as it can be conducted quickly by examining the features of an executable piece of code and matching it to previously observed malicious code. However, static code analysis can be vulnerable to code obfuscation techniques. Behavioural data collected during file execution is more difficult to obfuscate, but takes a relatively long time to capture -typically up to 5 minutes, meaning the malicious payload has likely already been delivered by the time it is detected.In this paper we investigate the possibility of predicting whether or not an executable is malicious based on a short snapshot of behavioural data. We find that an ensemble of recurrent neural networks are able to predict whether an executable is malicious or benign within the first 5 seconds of execution with 94% accuracy. This is the first time general types of malicious file have been predicted to be malicious during execution rather than using a complete activity log file post-execution, and enables cyber security endpoint protection to be advanced to use behavioural data for blocking malicious payloads rather than detecting them post-execution and having to repair the damage.
It is possible to forecast the volume of CVEs released within a time frame with a given prediction interval. For example, the number of CVEs published between now and 365 days from now can be predicted a year in advance within 8% of the actual value. Different predictive algorithms perform well at different lookahead values other than 365 days, such as monthly, quarterly, and half year. It is also possible to estimate the proportions of that total volume belonging to specific vendors, software, CVSS scores, or vulnerability types. Some vendors and products can be predicted with accuracy, others with too much uncertainty to be practically useful. This paper documents which ones are amenable to being forecasted. Strategic patch management should become much easier with these tools, and further uncertainty reductions can be built from the methodologies in this paper.
Perimeter-based detection is no longer sufficient for mitigating the threat posed by malicious software. This is evident as antivirus (AV) products are replaced by endpoint detection and response (EDR) products, the latter allowing visibility into live machine activity rather than relying on the AV to filter out malicious artefacts. This paper argues that detecting malware in real-time on an endpoint necessitates an automated response due to the rapid and destructive nature of some malware. The proposed model uses statistical filtering on top of a machine learning dynamic behavioural malware detection model in order to detect individual malicious processes on the fly and kill those which are deemed malicious. In an experiment to measure the tangible impact of this system, we find that fast-acting ransomware is prevented from corrupting 92% of files with a false positive rate of 14%. Whilst the false-positive rate currently remains too high to adopt this approach as-is, these initial results demonstrate the need for a detection model that is able to act within seconds of the malware execution beginning; a timescale that has not been addressed by previous work.
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