An Efficient Deep Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks

With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, the Internet of Things (IoT) has earned wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack that tends to be treated as normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with the cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide the comprehensive development of a new intelligent and autonomous deep-learning-based detection and classification system for cyber-attacks in IoT communication networks that leverage the power of convolutional neural networks, abbreviated as IoT-IDCS-CNN (IoT based Intrusion Detection and Classification System using Convolutional Neural Network). The proposed IoT-IDCS-CNN makes use of high-performance computing that employs the robust Compute Unified Device Architectures (CUDA) based Nvidia GPUs (Graphical Processing Units) and parallel processing that employs high-speed I9-core-based Intel CPUs. In particular, the proposed system is composed of three subsystems: a feature engineering subsystem, a feature learning subsystem, and a traffic classification subsystem. All subsystems were developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD) dataset, which includes all the key attacks in IoT computing. The simulation results demonstrated a greater than 99.3% and 98.2% cyber-attack classification accuracy for the binary-class classifier (normal vs. anomaly) and the multiclass classifier (five categories), respectively. The proposed system was validated using a K-fold cross-validation method and was evaluated using the confusion matrix parameters (i.e., true negative (TN), true positive (TP), false negative (FN), false positive (FP)), along with other classification performance metrics, including precision, recall, F1-score, and false alarm rate. The test and evaluation results of the IoT-IDCS-CNN system outperformed many recent machine-learning-based IDCS systems in the same area of study.

[1]  Prachi Shukla,et al.  ML-IDS: A machine learning approach to detect wormhole attacks in Internet of Things , 2017, 2017 Intelligent Systems Conference (IntelliSys).

[2]  Vrizlynn L. L. Thing,et al.  IEEE 802.11 Network Anomaly Detection and Attack Classification: A Deep Learning Approach , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[3]  Insoo Koo,et al.  Toward a Lightweight Intrusion Detection System for the Internet of Things , 2019, IEEE Access.

[4]  Athanasios V. Vasilakos,et al.  Secure Data Sharing and Searching at the Edge of Cloud-Assisted Internet of Things , 2017, IEEE Cloud Computing.

[5]  Zhen Liu,et al.  An Adaptive Ensemble Machine Learning Model for Intrusion Detection , 2019, IEEE Access.

[6]  Imran A. Zualkernan,et al.  Internet of things (IoT) security: Current status, challenges and prospective measures , 2015, 2015 10th International Conference for Internet Technology and Secured Transactions (ICITST).

[7]  Georgios Kambourakis,et al.  Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset , 2016, IEEE Communications Surveys & Tutorials.

[8]  Bogdan V. Ghita,et al.  Malware Squid: A Novel IoT Malware Traffic Analysis Framework Using Convolutional Neural Network and Binary Visualisation , 2019, NEW2AN.

[9]  Jiang Li,et al.  A few-shot deep learning approach for improved intrusion detection , 2017, 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON).

[10]  Nour Moustafa,et al.  UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) , 2015, 2015 Military Communications and Information Systems Conference (MilCIS).

[11]  Athanasios V. Vasilakos,et al.  LAM-CIoT: Lightweight authentication mechanism in cloud-based IoT environment , 2020, J. Netw. Comput. Appl..

[12]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  K. A. Taher,et al.  Network Intrusion Detection using Supervised Machine Learning Technique with Feature Selection , 2019, 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST).

[14]  Georgios Kambourakis,et al.  Attacks and Countermeasures on 802.16: Analysis and Assessment , 2013, IEEE Communications Surveys & Tutorials.

[15]  Sung-Bong Jang.  Collaborative Documentation Process Model , 2015 .

[16]  Gui Yun Tian,et al.  Deep Learning Models for Cyber Security in IoT Networks , 2019, 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC).

[17]  Bogdan V. Ghita,et al.  A machine-learning approach to Detect users' suspicious behaviour through the Facebook wall , 2019, ArXiv.

[18]  Abdulrahman Alruban,et al.  IoT Malware Network Traffic Classification using Visual Representation and Deep Learning , 2020, 2020 6th IEEE Conference on Network Softwarization (NetSoft).

[19]  Pavan Pongle,et al.  A survey: Attacks on RPL and 6LoWPAN in IoT , 2015, 2015 International Conference on Pervasive Computing (ICPC).

[20]  Vasos Vassiliou,et al.  Classifying Security Attacks in IoT Networks Using Supervised Learning , 2019, 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS).

[21]  Claus Nebauer,et al.  Evaluation of convolutional neural networks for visual recognition , 1998, IEEE Trans. Neural Networks.

[22]  Mansoor Alam,et al.  A Deep Learning Approach for Network Intrusion Detection System , 2016, EAI Endorsed Trans. Security Safety.

[23]  Francesco Chiti,et al.  Context-aware wireless mobile autonomic computing and communications: research trends and emerging applications , 2016, IEEE Wireless Communications.

[24]  Erol Gelenbe,et al.  Deep Learning with Dense Random Neural Network for Detecting Attacks against IoT-connected Home Environments , 2018, FNC/MobiSPC.

[25]  Lyudmila Sukhostat,et al.  Anomaly detection in network traffic using extreme learning machine , 2016, 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT).

[26]  Salvatore J. Stolfo,et al.  Cost-based modeling for fraud and intrusion detection: results from the JAM project , 2000, Proceedings DARPA Information Survivability Conference and Exposition. DISCEX'00.

[27]  Murad Khan,et al.  Internet of Things: A Comprehensive Review of Enabling Technologies, Architecture, and Challenges , 2018 .

[28]  Georgios Kambourakis,et al.  DDoS in the IoT: Mirai and Other Botnets , 2017, Computer.

[29]  Robert C. Atkinson,et al.  Threat analysis of IoT networks using artificial neural network intrusion detection system , 2016, 2016 International Symposium on Networks, Computers and Communications (ISNCC).