Android malware detection through generative adversarial networks

Abstract

Mobile and cell devices have empowered end users to tweak their cell phones more than ever and introduce applications just as we used to with personal computers. Android likewise portrays an uprise in mobile devices and personal digital assistants. It is an open‐source versatile platform fueling incalculable hardware units, tablets, televisions, auto amusement frameworks, digital boxes, and so forth. In a generally shorter life cycle, Android also has additionally experienced a mammoth development in application malware. In this context, a toweringly large measure of strategies has been proposed in theory for the examination and detection of these harmful applications for the Android platform. These strategies attempt to both statically reverse engineer the application and elicit meaningful information as features manually or dynamically endeavor to quantify the runtime behavior of the application to identify malevolence. The overgrowing nature of Android malware has enormously debilitated the support of protective measures, which leaves the platforms such as Android feeble for novel and mysterious malware. Machine learning is being utilized for malware diagnosis in mobile phones as a common practice and in Android distinctively. It is important to specify here that these systems, however, utilize and adapt the learning‐based techniques, yet the overhead of hand‐created features limits ease of use of such methods in reality by an end user. As a solution to this issue, we mean to make utilization of deep learning–based algorithms as the fundamental arrangement for malware examination on Android. Deep learning turns up as another way of research that has bid the scientific community in the fields of vision, speech, and natural language processing. Of late, models set up on deep convolution networks outmatched techniques utilizing handmade descriptive features at various undertakings. Likewise, our proposed technique to cater malware detection is by design a deep learning model making use of generative adversarial networks, which is responsible to detect the Android malware via famous two‐player game theory for a rock‐paper‐scissor problem. We have used three state‐of‐the‐art datasets and augmented a large‐scale dataset of opcodes extracted from the Android Package Kit bytecode and used in our experiments. Our technique achieves F1 score of 99% with a receiver operating characteristic of 99% on the bytecode dataset. This proves the usefulness of our technique and that it can generally be adopted in real life.

A Novel Malware Classification Method Based on Crucial Behavior

“…To overcome the shortcomings of traditional detection models, we also need to explore some state-of-the-art modes, i.e., Amin et al [30], Amin et al [31], and D'Angelo et al [32]. We will explore deep learning-based methods to improve the detection rate of malware.…”

“…Amin et al [30,31] explored bidirectional long shortterm memory for building an antimalware system to detect static opcodes of malware. In addition, they designed a deep learning model of generative adversarial networks to detect Android malware.…”

“…e value [27] API call sequence Simple, vulnerable to reorder or irrelevant API calls Lee et al [28] API call sequence Hansen et al [29] API call sequence; arguments; frequency Amin [30,31] Opcode End-to-end learning D'Angelo et al [32] API call sequence-based image Park et al [34] Behavioral graph High dimensional features can bring more calculations Elhadi et al [11] API call graph Nikolopoulos and Polenakis [35] System call dependency graph Fredrikson et al [37] Optimally discriminative specification Simplified representation of behavior graphs Alam et al [40] Control flow graph-based feature Ding et al [41] API dependency graph 4 Mathematical Problems in Engineering 0x0000044c of Handle is used to connect the RegQuer-yValue on line 2. e details of API call graph construction are described in our previous work [43]. It is necessary to extract crucial behaviors from the API call graph for malware classification.…”

A Survey of Android Malware Static Detection Technology Based on Machine Learning

“…Generative Adversarial Network. Amin [89] proposed a Generative Adversarial Network-based model to detect Android malware inspired by the famous two-player game theory for rock-paper-scissor problems. Inside the discriminator and generator, they incorporated LSTM as deep learning architecture to learn the opcode-based binary sequential data on a large and unlabelled dataset.…”

Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation

“…Because of their nature and capacity to alleviate the challenge of imbalanced datasets, GANs have been identified as having high potential in security and adversarial applications. We hence review GAN applications in Intrusion Detection Systems (IDSs) [114,115,116,117], malware detection [118,119], detection of rogue Radio Frequency (RF) transmitters [120], malware adaption/improvement [121,122,123], black-box Application Programming Interfaces (API) attacks [124] and other cybersecurity applications such as password guessing [125] and credit-card fraud detection [126,127,128].…”