Malware poses a severe threat to cyber security. Attackers use malware to achieve their
malicious purposes, such as unauthorized access, stealing confidential data, blackmailing …

Randomized smoothing is a leading approach for constructing classifiers that are certifiably
robust against adversarial examples. Existing work on randomized smoothing has focused …

Malware family classification is a significant issue with public safety and research
implications that has been hindered by the high cost of expert labels. The vast majority of …

Deep learning (DL) has proven to be effective in detecting sophisticated malware that is
constantly evolving. Even though deep learning has alleviated the feature engineering …

Adversarial binaries are executable files that have been altered without loss of function by
an AI agent in order to deceive malware detection systems. Progress in this emergent vein of …

Malware detection is an interesting and valuable domain to work in because it has
significant real-world impact and unique machine-learning challenges. We investigate …

Backdoor attacks targeting the deep neural network are flourishing recently and are more
stealthy than existing adversarial attacks. A deep understanding of the backdoor attacks …

In recent years, self-attention has become the dominant paradigm for sequence modeling in
a variety of domains. However, in domains with very long sequence lengths the $\mathcal …

Machine learning-based (ML) malware detectors have been shown to be susceptible to
adversarial malware examples. Given the vulnerability of deep learning detectors to small …

Windows malware is predominantly available in cyberspace and is a prime target for
deliberate adversarial evasion attacks. Although researchers have investigated the …