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Deep learning has emerged as a powerful approach for malware detection, demonstrating impressive accuracy across various data representations. However, these models face critical limitations in real-world, non-stationary environments where…

Machine learning (ML) based approach is considered as one of the most promising techniques for Android malware detection and has achieved high accuracy by leveraging commonly-used features. In practice, most of the ML classifications only…

Since Android has become a popular software platform for mobile devices recently; they offer almost the same functionality as personal computers. Malwares have also become a big concern. As the number of new Android applications tends to be…

Training pipelines for machine learning (ML) based malware classification often rely on crowdsourced threat feeds, exposing a natural attack injection point. In this paper, we study the susceptibility of feature-based ML malware classifiers…

This paper presents a demo of our Security Toolbox to detect novel malware in Android apps. This Toolbox is developed through our recent research project funded by the DARPA Automated Program Analysis for Cybersecurity (APAC) project. The…

Ransomware constitutes a significant threat to the Android operating system. It can either lock or encrypt the target devices, and victims are forced to pay ransoms to restore their data. Hence, the prompt detection of such attacks has a…

Famous for its superior performance, deep learning (DL) has been popularly used within many applications, which also at the same time attracts various threats to the models. One primary threat is from adversarial attacks. Researchers have…

It is well-known that Android malware constantly evolves so as to evade detection. This causes the entire malware population to be non-stationary. Contrary to this fact, most of the prior works on Machine Learning based Android malware…

Malware authors have always been at an advantage of being able to adversarially test and augment their malicious code, before deploying the payload, using anti-malware products at their disposal. The anti-malware developers and threat…

Nowadays, Android is the most dominant operating system in the mobile ecosystem, with billions of people using its apps daily. As expected, this trend did not go unnoticed by miscreants, and Android became the favorite platform for…

Concept drift is a significant challenge for malware detection, as the performance of trained machine learning models degrades over time, rendering them impractical. While prior research in malware concept drift adaptation has primarily…

Machine learning classifiers are vulnerable to adversarial examples -- input-specific perturbations that manipulate models' output. Universal Adversarial Perturbations (UAPs), which identify noisy patterns that generalize across the input…

Deep learning-based adversarial malware detectors have yielded promising results in detecting never-before-seen malware executables without relying on expensive dynamic behavior analysis and sandbox. Despite their abilities, these detectors…

The daily amount of Android malicious applications (apps) targeting the app repositories is increasing, and their number is overwhelming the process of fingerprinting. To address this issue, we propose an enhanced Cypider framework, a set…

Adversarial examples add imperceptible alterations to inputs with the objective to induce misclassification in machine learning models. They have been demonstrated to pose significant challenges in domains like image classification, with…

With the growing popularity of Android devices, Android malware is seriously threatening the safety of users. Although such threats can be detected by deep learning as a service (DLaaS), deep neural networks as the weakest part of DLaaS are…

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…

There has been an increased interest in the application of convolutional neural networks for image based malware classification, but the susceptibility of neural networks to adversarial examples allows malicious actors to evade classifiers.…

Due to the continuous improvement of performance and functions, Android remains the most popular operating system on mobile phone today. However, various malicious applications bring great threats to the system. Over the past few years,…

We consider the problem of generating adversarial malware by a cyber-attacker where the attacker's task is to strategically modify certain bytes within existing binary malware files, so that the modified files are able to evade a malware…