Adaptive Database Intrusion Detection Using Evolutionary Reinforcement Learning

2017 ◽  
pp. 547-556 ◽  
Author(s):  
Seul-Gi Choi ◽  
Sung-Bae Cho
Keyword(s):  
Reinforcement Learning ◽  
Intrusion Detection ◽  
Database Intrusion Detection

scholarly journals A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures

2021 ◽  
Vol 21 (4) ◽  
pp. 1-22
Author(s):  
Safa Otoum ◽  
Burak Kantarci ◽  
Hussein Mouftah
Keyword(s):  
Reinforcement Learning ◽  
Intrusion Detection ◽  
Comparative Study ◽  
Performance Metrics ◽  
Action Learning ◽  
Smart Devices ◽  
Critical Infrastructures ◽  
State Action ◽  
Detection Techniques ◽  
Depth Analysis

Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD’99 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with detection rate while SARSA-IDS and TD-IDS perform at the order of .

scholarly journals Self-Configurable Cyber-Physical Intrusion Detection for Smart Homes Using Reinforcement Learning

2021 ◽  
Vol 16 ◽  
pp. 1720-1735
Author(s):  
Ryan Heartfield ◽  
George Loukas ◽  
Anatolij Bezemskij ◽  
Emmanouil Panaousis
Keyword(s):  
Reinforcement Learning ◽  
Intrusion Detection ◽  
Smart Homes

Attention based multi-agent intrusion detection systems using reinforcement learning

2021 ◽  
Vol 61 ◽  
pp. 102923
Author(s):  
Kamalakanta Sethi ◽  
Y. Venu Madhav ◽  
Rahul Kumar ◽  
Padmalochan Bera
Keyword(s):  
Reinforcement Learning ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Detection Systems ◽  
Multi Agent

Deep-Reinforcement-Learning-Based Intrusion Detection in Aerial Computing Networks

IEEE Network ◽  
2021 ◽  
Vol 35 (4) ◽  
pp. 66-72
Author(s):  
Jing Tao ◽  
Ting Han ◽  
Ruidong Li
Keyword(s):  
Reinforcement Learning ◽  
Intrusion Detection
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