Although malicious software (malware) has been around since the early days of computers,
the sophistication and innovation of malware has increased over the years. In particular, the …

In this survey, we review the key developments in the field of malware detection using AI and
analyze core challenges. We systematically survey state-of-the-art methods across five …

Machine learning techniques are widely used in addition to signatures and heuristics to
increase the detection rate of anti-malware software, as they automate the creation of …

Malware is one of the prevalent security threats. Sandboxes and, more generally,
instrumented environments play a crucial role in dynamically analyzing malware samples …

Automated dynamic malware analysis systems are important in combating the proliferation
of modern malware. Unfortunately, malware can often easily detect and evade these …

The amount of time in which a sample is executed is one of the key parameters of a malware
analysis sandbox. Setting the threshold too high hinders the scalability and reduces the …

Complex malware samples feature measures to impede automatic and manual analyses,
making their investigation cumbersome. While automatic characterization of malware …

Dynamic binary instrumentation (DBI) techniques allow for monitoring and possibly altering
the execution of a running program up to the instruction level granularity. The ease of use …

Binary packing, a widely-used program obfuscation style, compresses or encrypts the
original program and then recovers it at runtime. Packed malware samples are pervasive …

Code virtualization is a highly sophisticated obfuscation technique adopted by malware
authors to stay under the radar. However, the increasing complexity of code virtualization …