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Henry A, Gautam S, Khanna S, Rabie K, Shongwe T, Bhattacharya P, Sharma B, Chowdhury S. Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System. Sensors (Basel) 2023; 23:s23020890. [PMID: 36679684 PMCID: PMC9866711 DOI: 10.3390/s23020890] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/03/2023] [Accepted: 01/05/2023] [Indexed: 05/14/2023]
Abstract
Recently, with the massive growth of IoT devices, the attack surfaces have also intensified. Thus, cybersecurity has become a critical component to protect organizational boundaries. In networks, Intrusion Detection Systems (IDSs) are employed to raise critical flags during network management. One aspect is malicious traffic identification, where zero-day attack detection is a critical problem of study. Current approaches are aligned towards deep learning (DL) methods for IDSs, but the success of the DL mechanism depends on the feature learning process, which is an open challenge. Thus, in this paper, the authors propose a technique which combines both CNN, and GRU, where different CNN-GRU combination sequences are presented to optimize the network parameters. In the simulation, the authors used the CICIDS-2017 benchmark dataset and used metrics such as precision, recall, False Positive Rate (FPR), True Positive Rate (TRP), and other aligned metrics. The results suggest a significant improvement, where many network attacks are detected with an accuracy of 98.73%, and an FPR rate of 0.075. We also performed a comparative analysis with other existing techniques, and the obtained results indicate the efficacy of the proposed IDS scheme in real cybersecurity setups.
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Affiliation(s)
- Azriel Henry
- Department of Computer Sciences and Engineering, Institute of Advanced Research, Gandhinagar 382426, Gujarat, India
| | - Sunil Gautam
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India
| | - Samrat Khanna
- Department of Computer Sciences and Engineering, Institute of Advanced Research, Gandhinagar 382426, Gujarat, India
| | - Khaled Rabie
- Department of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK
- Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Auckland Park, P.O. Box 524, Johannesburg 2006, South Africa
- Correspondence: (K.R.); (P.B.)
| | - Thokozani Shongwe
- Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Auckland Park, P.O. Box 524, Johannesburg 2006, South Africa
| | - Pronaya Bhattacharya
- Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Kolkata, 700135, West Bengal, India
- Correspondence: (K.R.); (P.B.)
| | - Bhisham Sharma
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
| | - Subrata Chowdhury
- Department of Masters of Computer Application, Sri Venkateswara College of Engineering and Technology (A), Chittoor 517127, Andhra Pradesh, India
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Naeem M, De Pietro G, Coronato A. Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems. Sensors (Basel) 2021; 22:s22010309. [PMID: 35009848 PMCID: PMC8749942 DOI: 10.3390/s22010309] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 01/02/2023]
Abstract
The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented.
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Hsieh C, Chan K, Chien F. Energy-Efficient Power Allocation and User Association in Heterogeneous Networks with Deep Reinforcement Learning. Applied Sciences 2021; 11:4135. [DOI: 10.3390/app11094135] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper studies the problem of joint power allocation and user association in wireless heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based approach. This is a challenging problem since the action space is hybrid, consisting of continuous actions (power allocation) and discrete actions (device association). Instead of quantizing the continuous space (i.e., possible values of powers) into a set of discrete alternatives and applying traditional deep reinforcement approaches such as deep Q learning, we propose working on the hybrid space directly by using the novel parameterized deep Q-network (P-DQN) to update the learning policy and maximize the average cumulative reward. Furthermore, we incorporate the constraints of limited wireless backhaul capacity and the quality-of-service (QoS) of each user equipment (UE) into the learning process. Simulation results show that the proposed P-DQN outperforms the traditional approaches, such as the DQN and distance-based association, in terms of energy efficiency while satisfying the QoS and backhaul capacity constraints. The improvement in the energy efficiency of the proposed P-DQN on average may reach 77.6% and 140.6% over the traditional DQN and distance-based association approaches, respectively, in a HetNet with three SBS and five UEs.
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