Scalable analysis for malware and vulnerability detection in binaries - Chandramohan, Mahinthan (2018)
In recent years, malware (malicious software) has greatly evolved and has become very sophisticated. The evolution and the volume of malware make it difficult to analyze them in an effective and scalable manner. It is reported that, on average, more than 100,000 fresh malware binaries emerge each day. Despite the common use and widespread availability of various anti-malware tools, proliferation of malware poses a major security threat to computer systems. Identification of malware variants significantly improves the detection rate and reduces the size of malware signature databases. Thus, it has become crucial for security researchers to analyze and cluster malware samples based on their malicious behavior. Though manual analysis of malware may provide a better understanding of its malicious behavior, it is practically impossible due to the exponential growth in the number of new malware each day. As a result, security researchers are constantly playing the catch-up game with the malware authors.