Abstract
5G is about to open Pandora’s box of security threats to the Internet of Things (IoT). Key technologies, such as network function virtualization and edge computing introduced by the 5G network, bring new security threats and risks to the Internet infrastructure. Therefore, higher detection and defense against malware are required. Nowadays, deep learning (DL) is widely used in malware detection. Recently, research has demonstrated that adversarial attacks have posed a hazard to DL‐based models. The key issue of enhancing the antiattack performance of malware detection systems that are used to detect adversarial attacks is to generate effective adversarial samples. However, numerous existing methods to generate adversarial samples are manual feature extraction or using white‐box models, which makes it not applicable in the actual scenarios. This paper presents an effective binary manipulation‐based attack framework, which generates adversarial samples with an evolutionary learning algorithm. The framework chooses some appropriate action sequences to modify malicious samples. Thus, the modified malware can successfully circumvent the detection system. The evolutionary algorithm can adaptively simplify the modification actions and make the adversarial sample more targeted. Our approach can efficiently generate adversarial samples without human intervention. The generated adversarial samples can effectively combat DL‐based malware detection models while preserving the consistency of the executable and malicious behavior of the original malware samples. We apply the generated adversarial samples to attack the detection engines of VirusTotal. Experimental results illustrate that the adversarial samples generated by our method reach an evasion success rate of 47.8%, which outperforms other attack methods. By adding adversarial samples in the training process, the MalConv network is retrained. We show that the detection accuracy is improved by 10.3%.
Highlights
With the commercialization and popularization of 5G, the Internet of Things (IoT) is coming closer to reality [1]
(1) This paper proposes a new method of generating adversarial samples by the use of the evolutionary algorithm, which can automatically generate valid adversarial samples
To make deep learning (DL)-based IoT malware detection models more robust and effective, we propose a framework for generating adversarial samples and their defense
Summary
With the commercialization and popularization of 5G, the IoT is coming closer to reality [1]. With the scale expansion of connected terminals, data storage, and utilization, security issues are becoming more and more complex. Malware attacks remain as one of the most urgent security issues users facing. Deep neural network-based malware detection has fulfilled remarkable achievements [3]. A growing community of researchers is attempting to apply deep learning to malware detection and classification tasks [4,5,6,7,8,9]. Saxe and Berlin [10] extracted the binary features of PE files, which are portable executable ones under Windows operation systems and utilized a four-layer feed-forward neural network to detect malware. The DL-based malicious detection and classification models are widely used
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