51 papers with code • 1 benchmarks • 1 datasets
Malware Detection is a significant part of endpoint security including workstations, servers, cloud instances, and mobile devices. Malware Detection is used to detect and identify malicious activities caused by malware. With the increase in the variety of malware activities on CMS based websites such as malicious malware redirects on WordPress site (Aka, WordPress Malware Redirect Hack) where the site redirects to spam, being the most widespread, the need for automatic detection and classifier amplifies as well. The signature-based Malware Detection system is commonly used for existing malware that has a signature but it is not suitable for unknown malware or zero-day malware
With the rapid proliferation and increased sophistication of malicious software (malware), detection methods no longer rely only on manually generated signatures but have also incorporated more general approaches like machine learning detection.
First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs.
Sparse adversarial perturbations received much less attention in the literature compared to $l_2$- and $l_\infty$-attacks.
A generative network is trained to minimize the generated adversarial examples' malicious probabilities predicted by the substitute detector.
Recent work has shown that adversarial Windows malware samples - referred to as adversarial EXEmples in this paper - can bypass machine learning-based detection relying on static code analysis by perturbing relatively few input bytes.
In the experiments conducted on the novel test data set the accuracy was observed as 89. 23% for the integrated feature set which is 15% improvement on accuracy achieved with raw-feature set alone.
This motivates us to investigate which kind of robustness the ensemble defense or effectiveness the ensemble attack can achieve, particularly when they combat with each other.