Adversarial Robustness of Image Based Android Malware Detection Models

Defending malware detection models against evasion based adversarial attacks

The last decade witnessed an exponential rise of Android smartphones and malware attacks on them. Researchers have investigated and proposed many promising malware detection models based on machine learning. However, these malware detection models are susceptible to adversarial attacks which threaten the Android security ecosystem. In this article, we propose to develop malware detection models which are more robust against adversarial attacks. We first designed an i-bit adversarial attack policy which achieved an average fooling rate of 51% with maximum ten modifications across twelve different malware detection models. Later we also propose an adversarial defence mechanism which enhanced the robustness of the malware detection models by reducing the fooling rate to one-third against the same adversarial attack.

Malware detection plays a critical role in securing endpoints such as workstations, servers, cloud instances, and mobile devices. It involves identifying and mitigating malicious activities triggered by various forms of malware, including viruses, trojans, worms, ransomware, adware, and spyware. With the rapid proliferation of sophisticated and evasive malware variants, such as polymorphic and metamorphic malware, traditional signature-based and heuristic approaches are no longer sufficient. These conventional methods often fail to detect previously unseen or obfuscated threats, making the adoption of more intelligent and adaptive systems essential. In response to this challenge, this project explores the application of machine learning techniques to enhance malware detection and classification. Leveraging publicly available malware datasets, various supervised and unsupervised algorithms—such as Decision Trees, Random Forests, Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Naïve Bayes, and Artificial Neural Networks (ANN)—will be employed and evaluated. Additionally, feature extraction and dimensionality reduction techniques such as Principal Component Analysis (PCA) will be applied to improve model performance. The objective is to assess and compare the models using metrics such as accuracy, precision, recall, F1-score, and confusion matrix analysis. This project aims to identify the most effective machine learning model for real-time, scalable, and accurate malware detection, contributing to improved cybersecurity practices and proactive threat mitigation. Keywords: Malware detection, machine learning, enterprise security, information gain, random forest, PE analysis, API calls.

Android system has gained the highest market share due to its openness and portability in the mobile ecosystem. Whereas, users are suffering from serious security issues these years. Many malicious Android applications are released in popular app stores and it’s hard to distinguish them. Most of the current malware detection researches are focus on collecting features as much as possible for better performance instead of mining information inside the simple features. Scaling is an effective means to improve classification results while scaling the features for a large bundle of apps remains a challenging work. In this paper, we propose a malware detection method based on Machine Learning (ML) using permission usage analysis to cope with the rapid increase in the number of Android malware, named Permission Feature Selection (PFS). PFS uses Android permission as a classification feature with high utilization. The method greatly shortens the time cost in detection without reducing the detection accuracy and makes it possible to scan large-scale samples in a short time. Besides, various experiments were designed on real-world datasets to verify the reliability of the method. The results of the evaluation show that the proposed method performed better than other feature scaling methods with 91.2% accuracy and the average time cost reduced to less than 2 s.

Today, in the field of malware detection, the expanding limitations of traditional detection methods and the increasing accuracy of detection methods designed on the basis of artificial intelligence algorithms are driving research findings in this area in favour of the latter. Therefore, we propose a novel malware detection model in this paper. This model combines a grey-scale image representation of malware with an autoencoder network in a deep learning model, analyses the feasibility of the grey-scale image approach of malware based on the reconstruction error of the autoencoder, and uses the dimensionality reduction features of the autoencoder to achieve the classification of malware from benign software. The proposed detection model achieved an accuracy of 96% and a stable F-score of about 96% by using the Android-side dataset we collected, which outperformed some traditional machine learning detection algorithms.

Malware detection from the smartphone has become a challenging issue for academicians and researchers. In this research paper, we applied five distinct machine learning algorithms and three different ensemble methods to develop a model for detecting malware from an Android-based smartphone. In this study, we proposed a framework that helps in selecting the right sets of the feature with an aim to improve the performance of the malware detection models. The proposed malware detection framework is then validated by considering two distinct performance parameters, i.e., accuracy and F-measure as a benchmark to detect malware from real-world apps. We performed an empirical study on thirty different categories of Android apps. The experimental data set consists of 1,94,659 benign apps and 67,538 malware apps that are collected from different promised repositories. Empirical results reveal that the models developed by using the proposed feature selection framework are able to detect more malware-infected apps when compared to all extracted feature sets. Moreover, the malware detection model build by using nonlinear ensemble decision tree forest (NDTF) approach is achieved a detection rate of 98.8%. In addition to that, the proposed malware detection framework is more effective in detecting malware-infected apps as compared to different anti-virus scanners and different frameworks or approaches developed in the literature.

Existing research in malware detection and classification has focused on the higher accuracy of the results. Generally, high accuracy is achieved through a large set of entropy features in most of the existing frameworks. However, detection and classification time (which also includes the feature extraction time) have largely been neglected in past studies. In this paper, we propose a neural network-based model for Windows-based malware detection and classification. We also study the impact of reducing the number of features, feature types, and feature extraction time on the accuracy of malware detection and classification. We compared our model with Gaussian Naive Bayes (gNB), Logistic Regression (LR), K-Nearest Neighbor (KNN), Classification and Regression Trees (CART), Random Forest (RF), and Extreme Gradient Boosting (XGB). Our proposed model achieves the highest accuracy of 92.17% for detection and 98.41% for classification by using only 100 features of entropy. Results demonstrate that by replacing entropy features with the GINI-index features and by adjusting feature sets, we can get 21.93% faster detection and 15.6% faster classification at the cost of less than 3% and 2% reduction in accuracy respectively.

SummaryTo deal with the huge amount of data, minimizing the overhead will play a key role in speedy and efficient malware detection. We propose a machine learning (ML) malware detection model with preprocessing to limit the feature overhead. The portable‐executable (PE) header information that retains meaningful and distinctive information has been considered to classify benign and malware files. The dataset is preprocessed by applying transformation, outlier detection and filling, and smoothing techniques. A maximum relevance minimum redundancy‐based feature selection method is deployed to assign the rank and score to each feature retaining the maximum relevant and minimal redundant information. Based on the obtained rank, many subsets of features have been created and investigated against support vector machine (SVM) and k‐nearest neighbors (k‐NN) with parametric tuning. The proposed ML model integrated with data preprocessing, feature selection, and SVM‐polynomial classifier has superior performance. This model is eliminating 63.8% feature overhead with accuracy above 99.1% for the benchmark datasets. To examine the robustness of the proposed model, new balanced and imbalanced datasets are created using new malware. The test results are encouraging with accuracy and specificity above 96.68%, 97.65%, and 91.57%, respectively. Interestingly, the proposed model is not trained using the newly created dataset.

With computing devices and the Internet being indispensable in people's everyday life, malware has posed serious threats to their security, making its detection of utmost concern. To protect legitimate users from the evolving malware attacks, machine learning-based systems have been successfully deployed and offer unparalleled flexibility in automatic malware detection. In most of these systems, resting on the analysis of different content-based features either statically or dynamically extracted from the file samples, various kinds of classifiers are constructed to detect malware. However, besides content-based features, file-to-file relations, such as file co-existence, can provide valuable information in malware detection and make evasion harder. To better understand the properties of file-to-file relations, we construct the file co-existence graph. Resting on the constructed graph, we investigate the semantic relatedness among files, and leverage graph inference, active learning and graph representation learning for malware detection. Comprehensive experimental results on the real sample collections from Comodo Cloud Security Center demonstrate the effectiveness of our proposed learning paradigms. As machine learning-based detection systems become more widely deployed, the incentive for defeating them increases. Therefore, we go further insight into the arms race between adversarial malware attack and defense, and aim to enhance the security of machine learning-based malware detection systems. In particular, we first explore the adversarial attacks under different scenarios (i.e., different levels of knowledge the attackers might have about the targeted learning system), and define a general attack strategy to thoroughly assess the adversarial behaviors. Then, considering different skills and capabilities of the attackers, we propose the corresponding secure-learning paradigms to counter the adversarial attacks and enhance the security of the learning systems while not compromising the detection accuracy. We conduct a series of comprehensive experimental studies based on the real sample collections from Comodo Cloud Security Center and the promising results demonstrate the effectiveness of our proposed secure-learning models, which can be readily applied to other detection tasks.

In the past decade, the number of malware variants has increased rapidly. Many researchers have proposed to detect malware using intelligent techniques, such as Machine Learning (ML) and Deep Learning (DL), which have high accuracy and precision. These methods, however, suffer from being opaque in the decision-making process. Therefore, we need Artificial Intelligence (AI)-based models to be explainable, interpretable, and transparent to be reliable and trustworthy. In this survey, we reviewed articles related to Explainable AI (XAI) and their application to the significant scope of malware detection. The article encompasses a comprehensive examination of various XAI algorithms employed in malware analysis. Moreover, we have addressed the characteristics, challenges, and requirements in malware analysis that cannot be accommodated by standard XAI methods. We discussed that even though Explainable Malware Detection (EMD) models provide explainability, they make an AI-based model more vulnerable to adversarial attacks. We also propose a framework that assigns a level of explainability to each XAI malware analysis model, based on the security features involved in each method. In summary, the proposed project focuses on combining XAI and malware analysis to apply XAI models for scrutinizing the opaque nature of AI systems and their applications to malware analysis.

In the era of Internet of Things (IoT), Malware has been proliferating exponentially over the past decade. Traditional anti-virus software are ineffective against modern complex Malware. In order to address this challenge, researchers have proposed hardware-assisted Malware detection (HMD) using hardware performance counters (HPCs). The HPCs are used to train a set of machine learning (ML) classifiers, which in turn, are used to distinguish benign programs from Malware. Recently, adversarial attacks have been designed by introducing perturbations in the HPC traces using an adversarial sample predictor to misclassify a program for specific HPCs. These attacks are designed with the basic assumption that the attacker is aware of the HPCs being used to detect Malware. Since modern processors consist of hundreds of HPCs, restricting to only a few of them for Malware detection aids the attacker. In this article, we propose a moving target defense (MTD) for this adversarial attack by designing multiple ML classifiers trained on different sets of HPCs. The MTD randomly selects a classifier; thus, confusing the attacker about the HPCs or the number of classifiers applied. We have developed an analytical model which proves that the probability of an attacker to guess the perfect HPC-classifier combination for MTD is extremely low (in the range of $10^{-1864}$ for a system with 20 HPCs). Our experimental results prove that the proposed defense is able to improve the classification accuracy of HPC traces that have been modified through an adversarial sample generator by up to 31.5%, for a near perfect (99.4%) restoration of the original accuracy.

In recent years, significant advancements have been made in cyber security, particularly through the application of machine learning (ML) methods. ML-based techniques have enhanced system security by effectively distinguishing between malicious and benign objects across various domains, including spam email detection, social media content filtering, intrusion detection systems, and malware detection. This paper focuses on the specific area of ML-based malware detection and its advantages over traditional methods, such as improved accuracy and the ability to generalize to unknown threats. Despite these advancements, ML-based malware detection systems are vulnerable to adversarial attacks, especially in black-box scenarios where the internal workings of the detection model are not accessible. This paper provides a comprehensive review of adversarial attacks on black-box malware detection systems and examines the current defense mechanisms against these attacks. Understanding the challenges and strategies in this field, this review aims to offer insights into enhancing the robustness and security of ML-based malware detection systems.

A multi-tiered feature selection model for android malware detection based on Feature discrimination and Information Gain

Time-interval temporal patterns can beat and explain the malware

Deep convolutional neural networks have achieved great success in recent years. They have been widely used in various applications such as optical and SAR image scene classification, object detection and recognition, semantic segmentation, and change detection. However, deep neural networks rely on large-scale high-quality training data, and can only guarantee good performance when the training and test data are independently sampled from the same distribution. Deep convolutional neural networks are found to be vulnerable to subtle adversarial perturbations. This adversarial vulnerability prevents the deployment of deep neural networks in security-sensitive applications such as medical, surveillance, autonomous driving and military scenarios. This paper first presents a holistic view of security issues for deep convolutional neural network-based image recognition systems. The entire information processing chain is analyzed regarding safety and security risks. In particular, poisoning attacks and evasion attacks on deep convolutional neural networks are analyzed in detail. The root causes of adversarial vulnerabilities of deep recognition models are also discussed. Then, we give a formal definition of adversarial robustness and present a comprehensive review of adversarial attacks, adversarial defense, and adversarial robustness evaluation. Rather than listing existing research, we focus on the threat models for the adversarial attack and defense arms race. We perform a detailed analysis of several representative adversarial attacks on SAR image recognition models and provide an example of adversarial robustness evaluation. Finally, several open questions are discussed regarding recent research progress from our workgroup. This paper can be further used as a reference to develop more robust deep neural network-based image recognition models in dynamic adversarial scenarios.