A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization

Existing Android malware detection approaches use a variety of features such as security sensitive APIs, system calls, control-flow structures and information flows in conjunction with Machine Learning classifiers to achieve accurate detection. Each of these feature sets provides a unique semantic perspective (or view) of apps' behaviours with inherent strengths and limitations. Meaning, some views are more amenable to detect certain attacks but may not be suitable to characterise several other attacks. Most of the existing malware detection approaches use only one (or a selected few) of the aforementioned feature sets which prevent them from detecting a vast majority of attacks. Addressing this limitation, we propose MKLDroid, a unified framework that systematically integrates multiple views of apps for performing comprehensive malware detection and malicious code localisation. The rationale is that, while a malware app can disguise itself in some views, disguising in every view while maintaining malicious intent will be much harder. MKLDroid uses a graph kernel to capture structural and contextual information from apps' dependency graphs and identify malice code patterns in each view. Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted combination of the views which yields the best detection accuracy. Besides multi-view learning, MKLDroid's unique and salient trait is its ability to locate fine-grained malice code portions in dependency graphs (e.g., methods/classes). Through our large-scale experiments on several datasets (incl. wild apps), we demonstrate that MKLDroid outperforms three state-of-the-art techniques consistently, in terms of accuracy while maintaining comparable efficiency. In our malicious code localisation experiments on a dataset of repackaged malware, MKLDroid was able to identify all the malice classes with 94% average recall

Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

Machine learning based solutions have been successfully employed for automatic detection of malware in Android applications. However, machine learning models are known to lack robustness against inputs crafted by an adversary. So far, the adversarial examples can only deceive Android malware detectors that rely on syntactic features, and the perturbations can only be implemented by simply modifying Android manifest. While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective. In this paper, we introduce a new highly-effective attack that generates adversarial examples of Android malware and evades being detected by the current models. To this end, we propose a method of applying optimal perturbations onto Android APK using a substitute model. Based on the transferability concept, the perturbations that successfully deceive the substitute model are likely to deceive the original models as well. We develop an automated tool to generate the adversarial examples without human intervention to apply the attacks. In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning based detectors that rely on semantic features such as control-flow-graph. The perturbations can also be implemented directly onto APK's Dalvik bytecode rather than Android manifest to evade from recent detectors. We evaluated the proposed manipulation methods for adversarial examples by using the same datasets that Drebin and MaMadroid (5879 malware samples) used. Our results show that, the malware detection rates decreased from 96% to 1% in MaMaDroid, and from 97% to 1% in Drebin, with just a small distortion generated by our adversarial examples manipulation method.Comment: 15 pages, 11 figure

Enhancing cloud security through the integration of deep learning and data mining techniques: A comprehensive review

Cloud computing is crucial in all areas of data storage and online service delivery. It adds various benefits to the conventional storage and sharing system, such as simple access, on-demand storage, scalability, and cost savings. The employment of its rapidly expanding technologies may give several benefits in protecting the Internet of Things (IoT) and physical cyber systems (CPS) from various cyber threats, with IoT and CPS providing facilities for people in their everyday lives. Because malware (malware) is on the rise and there is no well-known strategy for malware detection, leveraging the cloud environment to identify malware might be a viable way forward. To avoid detection, a new kind of malware employs complex jamming and packing methods. Because of this, it is very hard to identify sophisticated malware using typical detection methods. The article presents a detailed assessment of cloud-based malware detection technologies, as well as insight into understanding the cloud's use in protecting the Internet of Things and critical infrastructure from intrusions. This study examines the benefits and drawbacks of cloud environments in malware detection, as well as presents a methodology for detecting cloud-based malware using deep learning and data extraction and highlights new research on the issues of propagating existing malware. Finally, similarities and variations across detection approaches will be exposed, as well as detection technique flaws. The findings of this work may be utilized to highlight the current issue being tackled in malware research in the future

Obfuscation-resilient Android Malware Analysis Based on Contrastive Learning

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware analysis can be achieved through contrastive learning. We take the Android malware classification as an example to demonstrate our analysis. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and benign apps (or other types of malware). Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps are obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two widely used datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining 98.2% true positive rate on classifying 8,112 obfuscated malware samples