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

Protecting Android Devices from Malware Attacks: A State-of-the-Art Report of Concepts, Modern Learning Models and Challenges

Advancements in microelectronics have increased the popularity of mobile devices like cellphones, tablets, e-readers, and PDAs. Android, with its open-source platform, broad device support, customizability, and integration with the Google ecosystem, has become the leading operating system for mobile devices. While Android's openness brings benefits, it has downsides like a lack of official support, fragmentation, complexity, and security risks if not maintained. Malware exploits these vulnerabilities for unauthorized actions and data theft. To enhance device security, static and dynamic analysis techniques can be employed. However, current attackers are becoming increasingly sophisticated, and they are employing packaging, code obfuscation, and encryption techniques to evade detection models. Researchers prefer flexible artificial intelligence methods, particularly deep learning models, for detecting and classifying malware on Android systems. In this survey study, a detailed literature review was conducted to investigate and analyze how deep learning approaches have been applied to malware detection on Android systems. The study also provides an overview of the Android architecture, datasets used for deep learning-based detection, and open issues that will be studied in the future

Detection of Android Malware based on Sequence Alignment of Permissions

Permissions control accesses to critical resources on Android. Any weaknesses from their exploitation can be of great interest to attackers. Investigation about associations of permissions can reveal some patterns against attacks. In this regards, this paper proposes an approach based on sequence alignment between requested permissions to identify similarities between applications. Permission patterns for malicious and normal samples are determined and exploited to evaluate a similarity score. The nature of an application is obtained based on a threshold, judiciously computed. Experiments have been realized with a dataset of 534 malicious samples (300 training and 234 testing) and 534 normal samples (300 training and 234 testing). Our approach has been able to recognize testing samples (either malware or normal) with an accuracy of 79%, an average precision of 76% and an average recall of 75%. This research reveals that sequence alignment can improve malware detection research

Deep Learning Based Malware Detection Tool Development for Android Operating System

In today's world that called technology age, smartphones have become indispensable for users in many areas such as internet usage, social media usage, bank transactions, e-mail, as well as communication. The Android operating system is the most popular operating system that used with a rate of 85.4% in smartphones and tablets. Such a popular and widely used platform has become the target of malware. Malicious software can cause both material and moral damages to users.In this study, malwares that targeting smart phones were detected by using static, dynamic and hybrid analysis methods. In the static analysis, feature extraction was made in 9 different categories. These attributes are categorized under the titles of requested permissions, intents, Android components, Android application calls, used permissions, unused permissions, suspicious Android application calls, system commands, internet addresses. The obtained features were subjected to dimension reduction with principal component analysis and used as input to the deep neural network model. With the established model, 99.38% accuracy rate, 99.36% F1 score, 99.32% precision and 99.39% sensitivity values were obtained in the test data set.In the dynamic analysis part of the study, applications were run on a virtual smartphone, and Android application calls with strategic importance were obtained by hooking. The method called hybrid analysis was applied by combining the dynamically obtained features with the static features belonging to the same applications. With the established model, 96.94% accuracy rate, 96.78% F1 score, 96.99% precision and 96.59% sensitivity values were obtained in the test data set.</p

Multi-level analysis of Malware using Machine Learning

Multi-level analysis of Malware using Machine Learnin