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1. Discovering Malicious Signatures in Software from Structural Interactions

   https://doi.org/10.1109/ICASSP48485.2024.10446565  

   Yin, Chenzhong; Zhang, Hantang; Cheng, Mingxi; Xiao, Xiongye; Chen, Xinghe; Ren, Xin; Bogdan, Paul (April 2024, IEEE)

   Ko, Hanseok (Ed.)

   Malware represents a significant security concern in today’s digital landscape, as it can destroy or disable operating systems, steal sensitive user information, and occupy valuable disk space. However, current malware detection methods, such as static-based and dynamic-based approaches, struggle to identify newly developed ("zero-day") malware and are limited by customized virtual machine (VM) environments. To overcome these limitations, we propose a novel malware detection approach that leverages deep learning, mathematical techniques, and network science. Our approach focuses on static and dynamic analysis and utilizes the Low-Level Virtual Machine (LLVM) to profile applications within a complex network. The generated network topologies are input into the GraphSAGE architecture to efficiently distinguish between benign and malicious software applications, with the operation names denoted as node features. Importantly, the GraphSAGE models analyze the network’s topological geometry to make predictions, enabling them to detect state-of-the-art malware and prevent potential damage during execution in a VM. To evaluate our approach, we conduct a study on a dataset comprising source code from 24,376 applications, specifically written in C/C++, sourced directly from widely-recognized malware and various types of benign software. The results show a high detection performance with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 99.85%. Our approach marks a substantial improvement in malware detection, providing a notably more accurate and efficient solution when compared to current state-of-the-art malware detection methods. The code is released at https://github.com/HantangZhang/MGN. 

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2. Metamorphic Detection of Repackaged Malware

   https://doi.org/10.1109/MET52542.2021.00009  

   Singh, Shirish; Kaiser, Gail (June 2021, 6th International Workshop on Metamorphic Testing (MET))

   null (Ed.)

   Machine learning-based malware detection systems are often vulnerable to evasion attacks, in which a malware developer manipulates their malicious software such that it is misclassified as benign. Such software hides some properties of the real class or adopts some properties of a different class by applying small perturbations. A special case of evasive malware hides by repackaging a bonafide benign mobile app to contain malware in addition to the original functionality of the app, thus retaining most of the benign properties of the original app. We present a novel malware detection system based on metamorphic testing principles that can detect such benign-seeming malware apps. We apply metamorphic testing to the feature representation of the mobile app, rather than to the app itself. That is, the source input is the original feature vector for the app and the derived input is that vector with selected features removed. If the app was originally classified benign, and is indeed benign, the output for the source and derived inputs should be the same class, i.e., benign, but if they differ, then the app is exposed as (likely) malware. Malware apps originally classified as malware should retain that classification, since only features prevalent in benign apps are removed. This approach enables the machine learning model to classify repackaged malware with reasonably few false negatives and false positives. Our training pipeline is simpler than many existing ML-based malware detection methods, as the network is trained end-to-end to jointly learn appropriate features and to perform classification. We pre-trained our classifier model on 3 million apps collected from the widely-used AndroZoo dataset. 1 We perform an extensive study on other publicly available datasets to show our approach’s effectiveness in detecting repackaged malware with more than 94% accuracy, 0.98 precision, 0.95 recall, and 0.96 F1 score. 

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3. RoboGuardZ: A Scalable Zero-Shot Framework for Detecting Zero-Day Malware in Robots

   Kaur, Upinder; Celik, Z Berkay; Voyles, Richard M (October 2024, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   The ubiquitous deployment of robots across diverse domains, from industrial automation to personal care, underscores their critical role in modern society. However, this growing dependence has also revealed security vulnerabilities. An attack vector involves the deployment of malicious software (malware) on robots, which can cause harm to robots themselves, users, and even the surrounding environment. Machine learning approaches, particularly supervised ones, have shown promise in malware detection by building intricate models to identify known malicious code patterns. However, these methods are inherently limited in detecting unseen or zero-day malware variants as they require regularly updated massive datasets that might be unavailable to robots. To address this challenge, we introduce ROBOGUARDZ, a novel malware detection framework based on zero-shot learning for robots. This approach allows ROBOGUARDZ to identify unseen malware by establishing relationships between known malicious code and benign behaviors, allowing detection even before the code executes on the robot. To ensure practical deployment in resource-constrained robotic hardware, we employ a unique parallel structured pruning and quantization strategy that compresses the ROBOGUARDZ detection model by 37.4% while maintaining its accuracy. This strategy reduces the size of the model and computational demands, making it suitable for real-world robotic systems. We evaluated ROBOGUARDZ on a recent dataset containing real-world binary executables from multi-sensor autonomous car controllers. The framework was deployed on two popular robot embedded hardware platforms. Our results demonstrate an average detection accuracy of 94.25% and a low false negative rate of 5.8% with a minimal latency of 20 ms, which demonstrates its effectiveness and practicality. 

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4. Robust PDF Malware Detection with Image Visualization and Processing Techniques

   https://doi.org/10.1109/ICDIS.2019.00024  

   Corum, Andrew; Jenkins, Donovan; Zheng, Jun (January 2019, 2019 2nd International Conference on Data Intelligence and Security (ICDIS))

   PDF, as one of most popular document file format, has been frequently utilized as a vector by attackers to covey malware due to its flexible file structure and the ability to embed different kinds of content. In this paper, we propose a new learning-based method to detect PDF malware using image processing and processing techniques. The PDF files are first converted to grayscale images using image visualization techniques. Then various image features representing the distinct visual characteristics of PDF malware and benign PDF files are extracted. Finally, learning algorithms are applied to create the classification models to classify a new PDF file as malicious or benign. The performance of the proposed method was evaluated using Contagio PDF malware dataset. The results show that the proposed method is a viable solution for PDF malware detection. It is also shown that the proposed method is more robust to resist reverse mimicry attacks than the state-of-art learning-based method. 

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5. Malware Detection and Prevention using Artificial Intelligence Techniques

   https://doi.org/10.1109/BigData52589.2021.9671434  

   Hossain Faruk, Md Jobair; Shahriar, Hossain; Valero, Maria; Barsha, Farhat Lamia; Sobhan, Shahriar; Khan, Md Abdullah; Whitman, Michael; Cuzzocrea, Alfredo; Lo, Dan; Rahman, Akond; et al (January 2022, 2021 IEEE International Conference on Big Data (Big Data))

   With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. To maintain stakeholder’s, particularly, end user’s security, protecting the data from fraudulent efforts is one of the most pressing concerns. A set of malicious programming code, scripts, active content, or intrusive software that is designed to destroy intended computer systems and programs or mobile and web applications is referred to as malware. According to a study, naive users are unable to distinguish between malicious and benign applications. Thus, computer systems and mobile applications should be designed to detect malicious activities towards protecting the stakeholders. A number of algorithms are available to detect malware activities by utilizing novel concepts including Artificial Intelligence, Machine Learning, and Deep Learning. In this study, we emphasize Artificial Intelligence (AI) based techniques for detecting and preventing malware activity. We present a detailed review of current malware detection technologies, their shortcomings, and ways to improve efficiency. Our study shows that adopting futuristic approaches for the development of malware detection applications shall provide significant advantages. The comprehension of this synthesis shall help researchers for further research on malware detection and prevention using AI. 

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