Deep learning (DL)-based malware detectors have shown promise in swiftly detecting unseen malware without expensive dynamic malware behavior analysis. These detectors have been shown to be susceptible to adversarial malware variants generated from meticulously modifying known malware to mislead detectors into recognizing them as benign. Being able to automatically generate optimized functional adversarial malware variants by defenders is crucial to effective cyber defense and staying ahead of the adversary. Current adversarial malware example generation methods often assume threat models with any of the following four restrictions: (1) requiring access to insider knowledge about malware detectors, (2) an unlimited size of adversarial modifications, (3) an unlimited number of queries to malware detector, and (4) relying on dynamic analysis of malware behavior in a sandbox. Drawing on Actor-Critic Reinforcement Learning (RL), we propose a novel closed-box binary manipulation method for adversarial malware optimization, named Actor-Critic with Contextualized Action Representations (AC-CAR), to generate malware variants without these restrictions. AC-CAR leverages two novel components, a contextualized policy and a neural language model-based RL-augmented top-$k$sampling method. Unlike current methods, AC-CAR can utilize tens of thousands of actions to augment malware executables for evading DL-based malware detectors. AC-CAR yields an approximately 2-fold performance increase over the current methods on average, while decreasing the payload size to 20 times smaller than leading methods. We show that using the malware variants generated by AC-CAR in an adversarial re-training procedure improves malware detector’ robustness against adversarial variants by 29.65% on average.

Adversarial Malware Generation (AMG), the generation of adversarial malware variants to strengthen Deep Learning (DL)-based malware detectors has emerged as a crucial tool in the development of proactive cyberdefense. However, the majority of extant works offer subtle perturbations or additions to executable files and do not explore full-file obfuscation. In this study, we show that an open-source encryption tool coupled with a Reinforcement Learning (RL) framework can successfully obfuscate malware to evade state-of-the-art malware detection engines and outperform techniques that use advanced modification methods. Our results show that the proposed method improves the evasion rate from 27%-49% compared to widely-used state-of-the-art reinforcement learning-based methods.

Deep Learning (DL)-based malware detectors are increasingly adopted for early detection of malicious behavior in cybersecurity. However, their sensitivity to adversarial malware variants has raised immense security concerns. Generating such adversarial variants by the defender is crucial to improving the resistance of DL-based malware detectors against them. This necessity has given rise to an emerging stream of machine learning research, Adversarial Malware example Generation (AMG), which aims to generate evasive adversarial malware variants that preserve the malicious functionality of a given malware. Within AMG research, black-box method has gained more attention than white-box methods. However, most black-box AMG methods require numerous interactions with the malware detectors to generate adversarial malware examples. Given that most malware detectors enforce a query limit, this could result in generating non-realistic adversarial examples that are likely to be detected in practice due to lack of stealth. In this study, we show that a novel DL-based causal language model enables single-shot evasion (i.e., with only one query to malware detector) by treating the content of the malware executable as a byte sequence and training a Generative Pre-Trained Transformer (GPT). Our proposed method, MalGPT, significantly outperformed the leading benchmark methods on a real-world malware dataset obtained from VirusTotal, achieving over 24.51% evasion rate. MalGPT enables cybersecurity researchers to develop advanced defense capabilities by emulating large-scale realistic AMG.

Personally identifiable information (PII) has become a major target of cyber-attacks, causing severe losses to data breach victims. To protect data breach victims, researchers focus on collecting exposed PII to assess privacy risk and identify at-risk individuals. However, existing studies mostly rely on exposed PII collected from either the dark web or the surface web. Due to the wide exposure of PII on both the dark web and surface web, collecting from only the dark web or the surface web could result in an underestimation of privacy risk. Despite its research and practical value, jointly collecting PII from both sources is a non-trivial task. In this paper, we summarize our effort to systematically identify, collect, and monitor a total of 1,212,004,819 exposed PII records across both the dark web and surface web. Our effort resulted in 5.8 million stolen SSNs, 845,000 stolen credit/debit cards, and 1.2 billion stolen account credentials. From the surface web, we identified and collected over 1.3 million PII records of the victims whose PII is exposed on the dark web. To the best of our knowledge, this is the largest academic collection of exposed PII, which, if properly anonymized, enables various privacy research inquiries, including assessing privacy risk and identifying at-risk populations.