Deep Q-Learning Based Reinforcement Learning Approach for Network Intrusion Detection

Alavizadeh, Hooman; Jang-Jaccard, Julian; Alavizadeh, Hootan

doi:10.3390/computers11030041

Cited by 106 publications

(33 citation statements)

References 39 publications

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“…Q-learning, a model-free method, has been recognized as a promising approach, mainly when applied to intricate decision-making procedures. Q-learning offers several benefits, including excellent learning capabilities, effective results, and the capacity to integrate with other models s [25]. As per Rujin Ding et al, the fifth generation (5G) of cellular mobile communications is characterized by high data rates, ultra-low latency, high energy efficiency, and widespread device connectivity.…”

Section: Overview Of Related Workmentioning

confidence: 99%

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Adaptive Routing in Wireless Mesh Networks Using Hybrid Reinforcement Learning Algorithm

Mahajan

Kotecha

2022

IEEE Access

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Wireless mesh networks are popular due to their adaptability, easy-setup, flexibility, cost, and transmission time-reductions. The routing algorithm plays a vital role in transferring the data between the nodes. The network's performance is significantly impacted by the route opted by the algorithm. The router takes the decision to send the packet to the next router as per the policy of that algorithm. So even though that decision does not favor the right path selection, the router tends to follow its policy. This can be avoided by having intelligent routers that can make routing decisions on the fly.This paper presents the QL-Feed Forward routing algorithm(QFFR), a new generation of routing algorithms that combines reinforcement learning based on the Q-learning algorithm with a Feed Forward neural network. This algorithm(QFFR) can learn from the network environment and make routing decisions based on the algorithm's learnings. The AI agent's ability to select the fastest path, which enhances the efficiency of the routing operation, is demonstrated by the working of the suggested QFFR algorithm. This paper also evaluates the performance of traditional algorithms, namely, Ad-hoc On-Demand Distance-Vector, Optimized-Link-State-routing, Destination-Sequenced Distance-Vector and Distance Source routing. The evaluation parameters include throughput, packet delivery ratio, and delay. The parameters are the outcomes of the time the information takes to reach from source to destination. This analysis highlights the improvement in the routing decision ability of a router. As per analysis, Ad hoc On-Demand Distance Vector Algorithm outperforms with throughput 723.13 Kbps, delay 343.73 ns. Q-learning agent identifies the route and reaches the destination in average of 3.7s in non-grid architecture. The Q-learning agent takes 0.49sec with a grid size ten by ten and 0.53sec in three by four grid size. The suggested QFFR takes 7.62s score-over time with stable, consistent performance. INDEX TERMSDeep Learning , Reinforcement Learning, Q-learning , Markov Decision Process , routing algorithms, wireless mesh networks I. INTRODUCTION D URING this era of digital development, networks play a critical role. Figure 1 shows Wireless Mesh Networks with a mesh architecture. Wireless mesh networks' key advantages are their versatility and customizing capabilities. Any future modifications would be simple to accommodate, resulting in lower expenses and upkeep of the network. Wireless mesh networking is a recent development that emerged from a decade of Ad-hoc networking development [1]. A wireless mesh network (WMN) is a decentralized network system built on existing wireless technology, namely the 802.11 standards, that works on an Ad-hoc communication mode [2] [3]. The wireless mesh network is ideal for nextgeneration communication with its flexibility and extensive coverage. Multi-radio mesh routers and single-radio mesh clients form Wireless Mesh Networks(WMN) [4]. As a result, such networks are developed by connecting wireless access...

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Section: Overview Of Related Workmentioning

confidence: 99%

“…Because the input is only processed in one direction, the Feed Forward model is the most basic form of a Neural Network. The data flow in only one direction and never backwards, regardless of how many hidden nodes it passes through [25]. A feed-forward Neural Network is a biologically inspired classification technique.…”

Section: G Feed Forward Neural Networkmentioning

confidence: 99%

Adaptive Routing in Wireless Mesh Networks Using Hybrid Reinforcement Learning Algorithm

Mahajan

Kotecha

2022

IEEE Access

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“…Reinforcement learning (RL) is another machine learning technique that has promise in a variety of applications, including robots and gaming. Recently, several articles have examined the effects of RL in NIDS applications; however, less research has examined the effects of RL on the NIDS problem with unbalanced dataset [43,49].…”

Section: ) Network Intrusion Detectionmentioning

confidence: 99%

Deep Reinforcement Learning for Anomaly Detection: A Systematic Review

et al. 2022

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Anomaly detection has been used to detect and analyze anomalous elements from data for years. Various techniques have been developed to detect anomalies. However, the most convenient one is Machine learning which is performing well but still has limitations for large-scale unlabeled datasets. Deep Reinforcement Learning (DRL) based techniques outperform the existing supervised or unsupervised and other alternative techniques for anomaly detection. This study presents a Systematic Literature Review (SLR), which analyzes DRL models that detect anomalies in their application. This SLR aims to analyze the DRL frameworks for anomaly detection applications, proposed DRL methods, and their performance comparisons against alternative methods. In this review, we have identified 32 research articles from 2017-2022 that discuss DRL techniques for various anomaly detection applications. After analyzing the selected research articles, this paper presents 13 different applications of anomaly detection found in the selected research articles. We identified 50 different datasets applied in experiments on anomaly detection and demonstrated 17 distinct DRL models used in the selected papers to detect anomalies. Finally, we analyzed the performance of these DRL models and reviewed them. Additionally, we observed that detecting anomalies using DRL frameworks is a promising area of research and showed that DRL had shown better performance for anomaly detection where other models lack. Therefore, we provide researchers with recommendations and guidelines based on this review.

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“…A Deep Q-learning-based (DQL) reinforcement learning model to detect and categorize multiple network intrusion attack classes is presented in [ 16 ]. A labeled dataset is fed into the proposed DQL model, which subsequently employs a deep reinforcement learning technique based on deep Q networks.…”

Section: Introductionmentioning

confidence: 99%

Integrated Clinical Environment Security Analysis Using Reinforcement Learning

Ibrahim

Elhafiz

2022

Bioengineering

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Many communication standards have been proposed recently and more are being developed as a vision for dynamically composable and interoperable medical equipment. However, few have security systems that are sufficiently extensive or flexible to meet current and future safety requirements. This paper aims to analyze the cybersecurity of the Integrated Clinical Environment (ICE) through the investigation of its attack graph and the application of artificial intelligence techniques that can efficiently demonstrate the subsystems’ vulnerabilities. Attack graphs are widely used for assessing network security. On the other hand, they are typically too huge and sophisticated for security administrators to comprehend and evaluate. Therefore, this paper presents a Q-learning-based attack graph analysis approach in which an attack graph that is generated for the Integrated Clinical Environment system resembles the environment, and the agent is assumed to be the attacker. Q-learning can aid in determining the best route that the attacker can take in order to damage the system as much as possible with the least number of actions. Numeric values will be assigned to the attack graph to better determine the most vulnerable part of the system and suggest this analysis to be further utilized for bigger graphs.

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Deep Q-Learning Based Reinforcement Learning Approach for Network Intrusion Detection

Cited by 106 publications

References 39 publications

Adaptive Routing in Wireless Mesh Networks Using Hybrid Reinforcement Learning Algorithm

Adaptive Routing in Wireless Mesh Networks Using Hybrid Reinforcement Learning Algorithm

Deep Reinforcement Learning for Anomaly Detection: A Systematic Review

Integrated Clinical Environment Security Analysis Using Reinforcement Learning

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