Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

Yang, Yongjie; Tu, Shanshan; Ali, Raja Hashim; Alasmary, Hisham; Waqas, Muhammad; Amjad, Muhammad Nouman

doi:10.32604/cmc.2023.031907

Cited by 44 publications

(4 citation statements)

References 35 publications

(37 reference statements)

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“…Yang et al [19] have conducted an intrusion detection model based on a two-layered bidirectional long short-term memory (Bi-LSTM) on fog. In the rst stage fog nodes identify attacks based on tra c received from IoT devices and then send them to the cloud to summarize the global security condition.…”

Section: Related Workmentioning

confidence: 99%

A novel reinforcement learning-based hybrid intrusion detection system on fog-to-cloud computing

Najafli,

Haghighat,

Karasfi

2024

Preprint

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The increasing growth of the Internet of Things and its open and shared character has led to a rise in new attacks exponentially. As a consequence, the quick detection of attacks in IoT environments is essential. The intrusion detection system (IDS) is responsible for protecting and detecting the type of attacks. Creating an IDS that works in real-time and adapts to environmental changes is critical. In this paper, we propose a deep reinforcement learning-based (DRL) adaptive IDS that addresses the mentioned challenges. DRL-based IDS helps to create a decision agent, who controls the interaction with the indeterminate environment and performs binary detection (normal/intrusion) in fog. For multi-class classification of attacks, we use the ensemble method in the cloud. The proposed approach is evaluated on the CICIDS2018 dataset, in binary and multiclass classification. The results show that the proposed model achieves a comparable performance in detecting intrusions and identifying attacks and it reduces the prediction time significantly. Furthermore, it is a suitable solution for underrepresented attacks. Overall, combining multiple methods can be a great way for an intrusion detection system.

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Section: Related Workmentioning

confidence: 99%

A novel reinforcement learning-based hybrid intrusion detection system on fog-to-cloud computing

Najafli,

Haghighat,

Karasfi

2024

Preprint

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“…Several experiments were designed, and the results proved that HAT-UDA improves detection rates of unknown attacks significantly. Yongjie et al [33] analyzed network traffic data over time. Their model was based on bi-directional long and short-term memory (Bi-LSTM) and was endowed with an attention mechanism to classify traffic data.…”

Section: Related Workmentioning

confidence: 99%

An Attention-Based Convolutional Neural Network for Intrusion Detection Model

Wang

Ghaleb

2023

IEEE Access

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Network technology has had a distinctive impact on the entire human civilization and has become an important factor of production in many countries and regions. However, with the widespread popularity of network technology, security flaws have been scattered in various fields, and potential crises may break out by attackers at any time. Therefore, it is crucial to establish a traffic monitoring mechanism for network systems. Some researchers have already implemented intrusion detection models by convolutional neural networks (CNNs) combined with attention mechanisms and achieved good results. However, few attempts have been made to improve the computational efficiency of the model by organizing the appropriate image structure, and the integration of attention mechanisms could be further enhanced. In this study, an attention-based CNN intrusion detection model has been proposed. Together with the image generation methods described in this paper, an efficient and accurate processing flow is formed. To optimize the use of the feature information in the experiments, the feature fields in the experimental images were arranged according to the results of their importance analysis. And a more integrated attention mechanism has been applied to the CNN for building the detection model. A series of comparative experiments were conducted on a subset of the CSE-CIC-IDS2018 dataset, and the results show that the detection process and model proposed in this paper can swiftly complete the detection procedure while maintaining high accuracy.

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“…Furthermore, the attention mechanism [16], an important technique in deep learning, allows models to "focus" on different parts by assigning different "attention" to them, thereby enhancing the model's ability to focus on the most relevant information in text and improving the ability to process complex language features. For example, An and colleagues [17] proposed a financial system attack detection model called Finsformer based on a transformer and synchronized attention mechanism.…”

Section: Introductionmentioning

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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Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

Cited by 44 publications

References 35 publications

A novel reinforcement learning-based hybrid intrusion detection system on fog-to-cloud computing

A novel reinforcement learning-based hybrid intrusion detection system on fog-to-cloud computing

An Attention-Based Convolutional Neural Network for Intrusion Detection Model

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

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