Secure and privacy-preserving intrusion detection in wireless sensor networks: Federated learning with SCNN-Bi-LSTM for enhanced reliability

被引:55
作者
Bukhari, Syed Muhammad Salman [1 ]
Zafar, Muhammad Hamza [2 ]
Abou Houran, Mohamad [3 ]
Moosavi, Syed Kumayl Raza [4 ]
Mansoor, Majad [5 ]
Muaaz, Muhammad [6 ]
Sanfilippo, Filippo [2 ,7 ]
机构
[1] Capital Univ Sci & Technol, Dept Elect Engn, Islamabad 44000, Pakistan
[2] Univ Agder, Dept Engn Sci, N-4879 Grimstad, Norway
[3] Xi An Jiao Tong Univ, Sch Elect Engn, Xian 710049, Peoples R China
[4] Natl Univ Sci & Technol, SEECS, Islamabad 44000, Pakistan
[5] Univ Sci & Technol China, Dept Automat, Hefei 28796, Peoples R China
[6] Univ Agder, Dept Informat & Commun Technol, N-4879 Grimstad, Norway
[7] Kaunas Univ Technol, Dept Software Engn, LT-51368 Kaunas, Lithuania
关键词
WSNs; Network intrusion detection; Federated learning; Denial of Service; SCNN-Bi-LSTM; Stacked CNN;
D O I
10.1016/j.adhoc.2024.103407
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As the digital landscape expands rapidly due to technological advancements, cybersecurity concerns have become more prevalent. Intrusion Detection Systems (IDSs), which are crucial for identifying unusual network traffic indicative of malicious activity, have become a necessity. These systems can be either hardware or software -based. However, traditional IDS models often fail to adequately protect data privacy and detect complex, unique breaches, particularly within Wireless Sensor Networks (WSNs). To address these limitations, this paper proposes a novel Stacked Convolutional Neural Network and Bidirectional Long Short Term Memory (SCNN-Bi-LSTM) model for intrusion detection in WSNs. This model leverages Federated Learning (FL) to enhance intrusion detection performance and safeguard privacy. The FL -based SCNN-Bi-LSTM model is unique in its approach, allowing multiple sensor nodes to collaboratively train a central global model without revealing private data, thereby alleviating privacy concerns. The deep learning methodology of the SCNN-Bi-LSTM model effectively identifies sophisticated and previously unknown cyber threats by meticulously examining both local and temporal linkages in network patterns. The model has been specifically designed to detect and categorize different types of Denial of Service (DoS) attacks using specialized WSN-DS and CIC-IDS-2017 datasets. Compared to traditional Artificial Deep Neural Network (ADNN) models, our proposed FL-SCNN-BiLSTM model demonstrated superior detection rates for complex and unknown attacks, significantly improving IDS performance. The model achieved a notable classification accuracy of approximately 99.9% precision and recall on both datasets, substantially reducing false positives and negatives. Our research underscores the potential of federated learning and deep learning in enhancing the security and privacy of WSNs. The proposed FL-SCNN-Bi-LSTM architecture not only facilitates the identification of complex cyber threats but also exemplifies how deep learning techniques can be employed to bolster intrusion detection systems while preserving user data privacy.
引用
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页数:18
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