SLAM: A Malware Detection Method Based on Sliding Local Attention Mechanism

Chen, Jun; Guo, Shize; Ma, Xin; Li, Haiying; Guo, Jinhong; Chen, Ming; Pan, Zhisong

doi:10.1155/2020/6724513

Cited by 13 publications

(4 citation statements)

References 17 publications

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“…It interferes with the normal operation of the system but actually lacks the due safety protection capability, or may affect the efficiency, or even refuse service [16]. Take security as a requirement and consider it at the beginning of system development, so that security requirements can be a part of system objectives from the beginning and play a leading role in the process of system development [17,18]. At different levels of the system, different safety control mechanisms are used to implement different precision safety control.…”

Section: Experiments and Analysismentioning

confidence: 99%

Encryption Technology for Computer Network Data Security Protection

Yu¹

2022

Security and Communication Networks

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In order to solve the network security problem of data encryption technology, this paper proposes a research method of computer network information security maintenance based on data encryption technology. Taking the network reporting system of a mining enterprise as the research object, this paper comprehensively expounds on the design of network reporting system and data encryption in network communication from the aspects of demand investigation, demand analysis, system design, software system development, and so on and implements the development by using BS architecture + independent client. The experimental results show that the AES algorithm can be independently designated as 128 bits, 192 bits, and 256 bits. Conclusion. This scheme can realize the rapid and safe transmission of data information and provide an effective means for the transmission of confidential documents on the network, so it has a good application prospect.

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Section: Experiments and Analysismentioning

confidence: 99%

Encryption Technology for Computer Network Data Security Protection

Yu¹

2022

Security and Communication Networks

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“…For example, when detecting SQL injection attacks, models can identify atypical query patterns or structurally abnormal query statements by learning the differences between normal and malicious SQL queries, thereby effectively identifying and blocking SQL injection attacks. Moreover, models can accurately identify attack behaviors in more complex scenarios, such as phishing and malware dissemination, as the attention mechanism dynamically adjusts the model's focus to accurately identify traces of attack behaviors from extensive network communication data [42,43]. For instance, in phishing email detection, models can focus on deceptive vocabulary or sentence structures in email content to effectively identify and intercept phishing emails.…”

Section: Attention Mechanismmentioning

confidence: 99%

Enhancing Network Attack Detection Accuracy through the Integration of Large Language Models and Synchronized Attention Mechanism

Bai,

Sun,

Zhang

et al. 2024

Applied Sciences

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In this study, we propose a novel method for detecting cyberattack behaviors by leveraging the combined strengths of large language models and a synchronized attention mechanism. Extensive experiments conducted on diverse datasets, including server logs, financial behaviors, and comment data, demonstrate the significant advantages of this method over existing models such as Transformer, BERT, OPT-175B, LLaMa, and ChatGLM3-6B in key performance metrics such as precision, recall, and accuracy. For instance, on the server log dataset, the method achieved a precision of 93%, a recall of 91%, and an accuracy of 92%; on the financial behavior dataset, it reached a precision of 90%, a recall of 87%, and an accuracy of 89%; and on the comment data dataset, it excelled with a precision of 95%, a recall of 93%, and an accuracy of 94%. The introduction of a synchronized attention mechanism and a newly designed synchronized loss function proved especially effective, enhancing the method’s ability to process multi-source data and providing superior performance in identifying complex cyberattack patterns. Ablation experiments further validated the crucial roles of these innovations in boosting model performance: the synchronous attention mechanism substantially improved the model’s precision, recall, and accuracy to 93%, 89%, and 91% respectively, far exceeding other attention mechanisms. Similarly, the synchronized loss showcased a significant advantage, achieving the best performance across all tested metrics compared to traditional cross-entropy loss, focal loss, and MSE. These results underscore the method’s ability to deeply mine and analyze semantic information and contextual relationships within text data as well as to effectively integrate and process multimodal data, thereby offering strong technical support for the accurate and efficient detection of cyberattack behaviors.

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“…The ndings show that the model can match the performance of a variety of cutting-edge models that determine if a le is benign or malignant using extracted data, such as API calls [49], which were recovered and turned into a sequence [50]. To reduce False positives and False negatives in the prediction, the majority's knowledge of which characteristics contribute to malware's malignant nature or which sequences are present in all malwares has a fundamental error.…”

Section: C) Comparison Of Performancementioning

confidence: 99%

Deep Convolution Neural Networks and Image Processing for Malware Detection

Ksibi

Zakariah

Almuqren

et al. 2023

Preprint

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Current anti-malware technologies have exposed its glaring vulnerabilities as a result of a signature-based approach as more sophisticated malware has been appearing in recent years, particularly in the android operating system. The state-of-the-art literature offers a wide range of possibilities, but none of them are flawless in terms of providing clear and timely solutions. The current study used a CNN-based deep learning architecture to address this problem. The proposed method collected RGB images from unprocessed malware binaries. We explored complex high-level aspects that effectively identify malware families using an image-based method rather than feature representations in order to detect and identify malware families. The RGB graphics were extracted from the raw APK files because colour images may hold more data in the source code. We developed deep CNNs using produced images that extract higher-level semantics associated with malware. This has allowed us to develop more complex software with enhanced security and made it more challenging for attackers to avoid detection. We trained a customised CNN and a refined Vgg-16 architecture using their covered images. The Android malware datasets CIC-AndMal2017 and CICMalDroid 2020 were used in this analysis. Results show that the shallow CNN model is defeated by the pretrained vgg-16. The accuracy of the suggested method, which is substantially better than earlier studies on the topic, has been assessed to detect malware samples with a 97.81% accuracy rate.

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SLAM: A Malware Detection Method Based on Sliding Local Attention Mechanism

Cited by 13 publications

References 17 publications

Encryption Technology for Computer Network Data Security Protection

Encryption Technology for Computer Network Data Security Protection

Enhancing Network Attack Detection Accuracy through the Integration of Large Language Models and Synchronized Attention Mechanism

Deep Convolution Neural Networks and Image Processing for Malware Detection

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