Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

被引:34
作者
Abu Al-Haija, Qasem [1 ]
Al-Badawi, Ahmad [2 ]
机构
[1] Princess Sumaya Univ Technol, Dept Comp Sci Cybersecur, Amman 11941, Jordan
[2] Rabdan Acad RA, Dept Homeland Secur, Abu Dhabi 22401, U Arab Emirates
关键词
cybersecurity; Internet of Things; network layer; intrusion detection; intrusion classification; ensemble learning; GENERATION; INTERNET;
D O I
10.3390/s22010241
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Network Intrusion Detection Systems (NIDSs) are indispensable defensive tools against various cyberattacks. Lightweight, multipurpose, and anomaly-based detection NIDSs employ several methods to build profiles for normal and malicious behaviors. In this paper, we design, implement, and evaluate the performance of machine-learning-based NIDS in IoT networks. Specifically, we study six supervised learning methods that belong to three different classes: (1) ensemble methods, (2) neural network methods, and (3) kernel methods. To evaluate the developed NIDSs, we use the distilled-Kitsune-2018 and NSL-KDD datasets, both consisting of a contemporary real-world IoT network traffic subjected to different network attacks. Standard performance evaluation metrics from the machine-learning literature are used to evaluate the identification accuracy, error rates, and inference speed. Our empirical analysis indicates that ensemble methods provide better accuracy and lower error rates compared with neural network and kernel methods. On the other hand, neural network methods provide the highest inference speed which proves their suitability for high-bandwidth networks. We also provide a comparison with state-of-the-art solutions and show that our best results are better than any prior art by 1~20%.
引用
收藏
页数:16
相关论文
共 60 条
[1]   Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection [J].
Abdulhammed, Razan ;
Musafer, Hassan ;
Alessa, Ali ;
Faezipour, Miad ;
Abuzneid, Abdelshakour .
ELECTRONICS, 2019, 8 (03)
[2]  
Abu Al-haija Qasem, 2022, Soft Computing for Security Applications: Proceedings of ICSCS 2021. Advances in Intelligent Systems and Computing (1397), P27, DOI 10.1007/978-981-16-5301-8_3
[3]  
Abu Al-Haija Qasem, 2020, 2020 International Conference on Computational Science and Computational Intelligence (CSCI), P1586, DOI 10.1109/CSCI51800.2020.00293
[4]   Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management [J].
Abu Al-Haija, Qasem ;
Smadi, Abdallah A. ;
Allehyani, Mohammed F. .
ENERGIES, 2021, 14 (21)
[5]   High Performance Classification Model to Identify Ransomware Payments for Heterogeneous Bitcoin Networks [J].
Abu Al-Haija, Qasem ;
Alsulami, Abdulaziz A. .
ELECTRONICS, 2021, 10 (17)
[6]   On the Security of Cyber-Physical Systems Against Stochastic Cyber-Attacks Models [J].
Abu Al-Haija, Qasem .
2021 IEEE INTERNATIONAL IOT, ELECTRONICS AND MECHATRONICS CONFERENCE (IEMTRONICS), 2021, :155-160
[7]   An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks [J].
Abu Al-Haija, Qasem ;
Zein-Sabatto, Saleh .
ELECTRONICS, 2020, 9 (12) :1-26
[8]  
Abu Al-Haija Q, 2019, IEEE INT C BIOINFORM, P2661, DOI 10.1109/BIBM47256.2019.8983101
[9]  
Abu Taher K, 2019, 2019 1ST INTERNATIONAL CONFERENCE ON ROBOTICS, ELECTRICAL AND SIGNAL PROCESSING TECHNIQUES (ICREST), P643, DOI [10.1109/ICREST.2019.8644161, 10.1109/icrest.2019.8644161]
[10]  
Al-Haija Q. A., 2020, IET Conference Proceedings, V2020, DOI 10.1049/icp.2021.0971