Enhancing Intrusion Detection System using Deep Q-Network Approaches based on Reinforcement Learning
Keywords:
K-Nearest Neighbors, Random Forest, Artificial Neural Network, Convolutional Neural Network, Support Vector Machine, Deep Q-Networks, reinforcement learning, Intrusion Detection SystemAbstract
This study presents a comparative analysis of various algorithms for Intrusion Detection Systems (IDS), including KNN, RF, ANN, CNN, SVM, and a multi-method approach combining KNN, RF, NN, and NB. The proposed method, which integrates these techniques, achieves a notable accuracy of 96.8%. Additionally, the study explores a Deep Q-Networks (DQN) based IDS, detailing steps from data pre-processing and environment definition to model training and deployment. This DQN approach, with its structured learning and adaptation mechanism, complements the comprehensive analysis, highlighting the potential of combined and advanced techniques in enhancing IDS accuracy and effectiveness.
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Sharon A, Mohanraj P, Abraham TE, Sundan B, Thangasamy A. An intelligent intrusion detection system using hybrid deep learning approaches in cloud environment. InInternational Conference on Computer, Communication, and Signal Processing 2022 Feb 24 (pp. 281-298). Cham: Springer International Publishing.
Friha O, Ferrag MA, Benbouzid M, Berghout T, Kantarci B, Choo KK. 2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT. Computers & Security. 2023 Apr 1;127:103097.
Sultana N, Chilamkurti N, Peng W, Alhadad R. Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Networking and Applications. 2019 Mar;12:493-501.
Rao KN, Rao KV, PVGD PR. A hybrid intrusion detection system based on sparse autoencoder and deep neural network. Computer Communications. 2021 Dec 1;180:77-88.
Amir MS, Bhatti G, Anwer M, Iftikhar Y. Efficient & Sustainable Intrusion Detection System Using Machine Learning & Deep Learning for IoT. In2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) 2023 Mar 17 (pp. 1-6). IEEE.
Elsayed R, Hamada R, Hammoudeh M, Abdalla M, Elsaid SA. A Hierarchical Deep Learning-Based Intrusion Detection Architecture for Clustered Internet of Things. Journal of Sensor and Actuator Networks. 2022 Dec 28;12(1):3.
Rullo A, Midi D, Mudjerikar A, Bertino E. Kalis2. 0-a SECaaS-Based Context-Aware Self-Adaptive Intrusion Detection System for the IoT. IEEE Internet of Things Journal. 2023 Nov 20.
Balamurugan E, Mehbodniya A, Kariri E, Yadav K, Kumar A, Haq MA. Network optimization using defender system in cloud computing security based intrusion detection system withgame theory deep neural network (IDSGT-DNN). Pattern Recognition Letters. 2022 Apr 1;156:142-51.
Seifi S, Beaubrun R, Bellaiche M, Halabi T. A Study on the Efficiency of Intrusion Detection Systems in IoT Networks. In2023 International Conference on Computer, Information and Telecommunication Systems (CITS) 2023 Jul 10 (pp. 1-8). IEEE.
Iwendi C, Srivastava G, Khan S, Maddikunta PK. Cyberbullying detection solutions based on deep learning architectures. Multimedia Systems. 2023 Jun;29(3):1839-52.
Sethi M, Verma J, Snehi M, Baggan V, Chhabra G. Web Server Security Solution for Detecting Cross-site Scripting Attacks in Real-time Using Deep Learning. In2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) 2023 Apr 21 (pp. 1-5). IEEE.
Javeed D, Gao T, Jamil Z. Artificial Intelligence (AI)-based Intrusion Detection System for IoT-enabled Networks: A State-of-the-Art Survey. InProtecting User Privacy in Web Search Utilization 2023 (pp. 269-289). IGI Global.
Raheema AQ. Threat Analysis in IOT Network Using Evolutionary Sparse Convolute Network Intrusion Detection System. International Journal of Online & Biomedical Engineering. 2023 Mar 1;19(3).
Isaza G, Ramirez F, Duque N, Lopez JA, Montes J. DDoS Attacks Detection with Deep Learning Model Using a Cloud Architecture. InSustainable Smart Cities and Territories International Conference 2023 Jun 21 (pp. 87-96). Cham: Springer Nature Switzerland.
Flak P, Czyba R. RF Drone Detection System Based on a Distributed Sensor Grid With Remote Hardware-Accelerated Signal Processing. IEEE Access. 2023 Dec 5.
Satheesh N, Rathnamma MV, Rajeshkumar G, Sagar PV, Dadheech P, Dogiwal SR, Velayutham P, Sengan S. Flow-based anomaly intrusion detection using machine learning model with software defined networking for OpenFlow network. Microprocessors and Microsystems. 2020 Nov 1;79:103285.
Hamidouche M, Popko E, Ouni B. Enhancing IoT Security via Automatic Network Traffic Analysis: The Transition from Machine Learning to Deep Learning. arXiv preprint arXiv:2312.00034. 2023 Nov 20.
C. Hardegen, B. Pfülb, S. Rieger and A. Gepperth, "Predicting Network Flow Characteristics Using Deep Learning and Real-World Network Traffic," in IEEE Transactions on Network and Service Management, vol. 17, no. 4, pp. 2662-2676, Dec. 2020, doi: 10.1109/TNSM.2020.3025131.
Y. Uhm and W. Pak, "Service-Aware Two-Level Partitioning for Machine Learning-Based Network Intrusion Detection With High Performance and High Scalability," in IEEE Access, vol. 9, pp. 6608-6622, 2021, doi: 10.1109/ACCESS.2020.3048900.
S. Mo, X. Pei and C. Wu, "Safe Reinforcement Learning for Autonomous Vehicle Using Monte Carlo Tree Search," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 6766-6773, July 2022, doi: 10.1109/TITS.2021.3061627.
X. Mo, S. Tan, B. Li and J. Huang, "MCTSteg: A Monte Carlo Tree Search-Based Reinforcement Learning Framework for Universal Non-Additive Steganography," in IEEE Transactions on Information Forensics and Security, vol. 16, pp. 4306-4320, 2021, doi: 10.1109/TIFS.2021.3104140.
J. Lu, D. He and Z. Wang, "Secure Routing in Multihop Ad-Hoc Networks With SRR-Based Reinforcement Learning," in IEEE Wireless Communications Letters, vol. 11, no. 2, pp. 362-366, Feb. 2022, doi: 10.1109/LWC.2021.3128582.
P. Ladosz et al., "Deep Reinforcement Learning With Modulated Hebbian Plus Q-Network Architecture," in IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 5, pp. 2045-2056, May 2022, doi: 10.1109/TNNLS.2021.3110281.
P. Xu et al., "Active Power Correction Strategies Based on Deep Reinforcement Learning—Part I: A Simulation-driven Solution for Robustness," in CSEE Journal of Power and Energy Systems, vol. 8, no. 4, pp. 1122-1133, July 2022, doi: 10.17775/CSEEJPES.2020.07090.
H. Shuai and H. He, "Online Scheduling of a Residential Microgrid via Monte-Carlo Tree Search and a Learned Model," in IEEE Transactions on Smart Grid, vol. 12, no. 2, pp. 1073-1087, March 2021, doi: 10.1109/TSG.2020.3035127.
J. Kim, B. Kang and H. Cho, "SpecMCTS: Accelerating Monte Carlo Tree Search Using Speculative Tree Traversal," in IEEE Access, vol. 9, pp. 142195-142205, 2021, doi: 10.1109/ACCESS.2021.3120384.
G. Piro, L. A. Grieco, G. Boggia, and P. Camarda, ‘‘Nano-sim: Simulating electromagnetic-based nanonetworks in the network simulator 3,’’ in Proc. 6th Int. Conf. Simulation Tools Techn., Jul. 2013, pp. 203–210.
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