Detecting Android Malware and Classifying Its Families in Large-scale Datasets

To relieve the burden of security analysts, Android malware detection and its family classification need to be automated. There are many previous works focusing on using machine (or deep) learning technology to tackle these two important issues, but as the number of mobile applications has increased in recent years, developing a scalable and precise solution is a new challenge that needs to be addressed in the security field. Accordingly, in this article, we propose a novel approach that not only enhances the performance of both Android malware and its family classification, but also reduces the running time of the analysis process. Using large-scale datasets obtained from different sources, we demonstrate that our method is able to output a high F-measure of 99.71% with a low FPR of 0.37%. Meanwhile, the computation time for processing a 300K dataset is reduced to nearly 3.3 hours. In addition, in classification evaluation, we demonstrate that the F-measure, precision, and recall are 97.5%, 96.55%, 98.64%, respectively, when classifying 28 malware families. Finally, we compare our method with previous studies in both detection and classification evaluation. We observe that our method produces better performance in terms of its effectiveness and efficiency.

Experimental Evaluation of Malware Family Classification Methods from Sequential Information of TLS-Encrypted Traffic

In parallel with the rapid adoption of transport layer security (TLS), malware has utilized the encrypted communication channel provided by TLS to hinder detection from network traffic. To this end, recent research efforts are directed toward malware detection and malware family classification for TLS-encrypted traffic. However, amongst their feature sets, the proposals to utilize the sequential information of each TLS session has not been properly evaluated, especially in the context of malware family classification. In this context, we propose a systematic framework to evaluate the state-of-the-art malware family classification methods for TLS-encrypted traffic in a controlled environment and discuss the advantages and limitations of the methods comprehensively. In particular, our experimental results for the 10 representations and classifier combinations show that the graph-based representation for the sequential information achieves better performance regardless of the evaluated classification algorithms. With our framework and findings, researchers can design better machine learning based classifiers.

MALWARE DETECTION SYSTEM BASED ON DEEP LEARNING TECHNIQUE

In this paper, we propose a malware classification and detection framework using transfer learning based on existing Deep Learning models that have been pre-trained on massive image datasets, we applied a deep Convolutional Neural Network (CNN) based on Xception model to perform malware image classification. The Xception model is a recently developed special CNN architecture that is more powerful with less overfitting problems than the current popular CNN models such as VGG16, The experimental results on a Malimg Dataset which is comprising 9,821 samples from 26 different families ,Malware samples are represented as byteplot grayscale images and a deep neural network is trained freezing the convolutional layers of Xception model adapting the last layer to malware family classification , The performance of our approach was compared with other methods including KNN, SVM, VGG16 etc. , the Xception model can effectively be used to classify and detect malware families and achieve the highest validation accuracy than all other approaches including VGG16 model which are using image-based malware, our approach does not require any features engineering, making it more effective to adapt to any future evolution in malware, and very much less time consuming than the champion’s solution.

Machine-Learning-Based Android Malware Family Classification Using Built-In and Custom Permissions

Malware family classification is grouping malware samples that have the same or similar characteristics into the same family. It plays a crucial role in understanding notable malicious patterns and recovering from malware infections. Although many machine learning approaches have been devised for this problem, there are still several open questions including, “Which features, classifiers, and evaluation metrics are better for malware familial classification”? In this paper, we propose a machine learning approach to Android malware family classification using built-in and custom permissions. Each Android app must declare proper permissions to access restricted resources or to perform restricted actions. Permission declaration is an efficient and obfuscation-resilient feature for malware analysis. We developed a malware family classification technique using permissions and conducted extensive experiments with several classifiers on a well-known dataset, DREBIN. We then evaluated the classifiers in terms of four metrics: macrolevel F1-score, accuracy, balanced accuracy (BAC), and the Matthews correlation coefficient (MCC). BAC and the MCC are known to be appropriate for evaluating imbalanced data classification. Our experimental results showed that: (i) custom permissions had a positive impact on classification performance; (ii) even when the same classifier and the same feature information were used, there was a difference up to 3.67% between accuracy and BAC; (iii) LightGBM and AdaBoost performed better than other classifiers we considered.

A Hybrid Analysis-Based Approach to Android Malware Family Classification

With the popularity of Android, malware detection and family classification have also become a research focus. Many excellent methods have been proposed by previous authors, but static and dynamic analyses inevitably require complex processes. A hybrid analysis method for detecting Android malware and classifying malware families is presented in this paper, and is partially optimized for multiple-feature data. For static analysis, we use permissions and intent as static features and use three feature selection methods to form a subset of three candidate features. Compared with various models, including k-nearest neighbors and random forest, random forest is the best, with a detection rate of 95.04%, while the chi-square test is the best feature selection method. After using feature selection to explore the critical static features contained in this dataset, we analyzed a subset of important features to gain more insight into the malware. In a dynamic analysis based on network traffic, unlike those that focus on a one-way flow of traffic and work on HTTP protocols and transport layer protocols, we focused on sessions and retained all protocol layers.he Res7LSTM model is then used to further classify the malicious and partially benign samples detected in the static detection. The experimental results show that our approach can not only work with fewer static features and guarantee sufficient accuracy, but also improve the detection rate of Android malware family classification from 71.48% in previous work to 99% when cutting the traffic in terms of the sessions and protocols of all layers.

AdvAndMal: Adversarial Training for Android Malware Detection and Family Classification

In recent years, Android malware has continued to evolve against detection technologies, becoming more concealed and harmful, making it difficult for existing models to resist adversarial sample attacks. At the current stage, the detection result is no longer the only criterion for evaluating the pros and cons of the model with its algorithms, it is also vital to take the model’s defensive ability against adversarial samples into consideration. In this study, we propose a general framework named AdvAndMal, which consists of a two-layer network for adversarial training to generate adversarial samples and improve the effectiveness of the classifiers in Android malware detection and family classification. The adversarial sample generation layer is composed of a conditional generative adversarial network called pix2pix, which can generate malware variants to extend the classifiers’ training set, and the malware classification layer is trained by RGB image visualized from the sequence of system calls. To evaluate the adversarial training effect of the framework, we propose the robustness coefficient, a symmetric interval i = [−1, 1], and conduct controlled experiments on the dataset to measure the robustness of the overall framework for the adversarial training. Experimental results on 12 families with the largest number of samples in the Drebin dataset show that the accuracy of the overall framework is increased from 0.976 to 0.989, and its robustness coefficient is increased from 0.857 to 0.917, which proves the effectiveness of the adversarial training method.