A Comparative Study of Real-Valued Negative Selection to Statistical Anomaly Detection Techniques

2005 ◽  
pp. 262-275 ◽  
Author(s):  
Thomas Stibor ◽  
Jonathan Timmis ◽  
Claudia Eckert
Keyword(s):  
Anomaly Detection ◽  
Comparative Study ◽  
Negative Selection ◽  
Detection Techniques ◽  
Statistical Anomaly Detection

scholarly journals A Comparative Study of Unsupervised Anomaly Detection Techniques Using Honeypot Data

2010 ◽  
Vol E93-D (9) ◽  
pp. 2544-2554 ◽  
Author(s):  
Jungsuk SONG ◽  
Hiroki TAKAKURA ◽  
Yasuo OKABE ◽  
Daisuke INOUE ◽  
Masashi ETO ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Comparative Study ◽  
Detection Techniques ◽  
Unsupervised Anomaly Detection

Comparative Study of One-Class Based Anomaly Detection Techniques for a Bicomponent Mixing Machine Monitoring

2020 ◽  
Vol 51 (7) ◽  
pp. 649-667
Author(s):  
Esteban Jove ◽  
José-Luis Casteleiro-Roca ◽  
Roberto Casado-Vara ◽  
Héctor Quintián ◽  
Juan Albino Méndez Pérez ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Comparative Study ◽  
Detection Techniques ◽  
Machine Monitoring

scholarly journals A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks

Sensors ◽  
2016 ◽  
Vol 16 (6) ◽  
pp. 868 ◽  
Author(s):  
Victor Garcia-Font ◽  
Carles Garrigues ◽  
Helena Rifà-Pous
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Comparative Study ◽  
Smart City ◽  
Wireless Sensor ◽  
Detection Techniques

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 Indirectly Supervised Anomaly Detection of Clinically Meaningful Health Events from Smart Home Data

2021 ◽  
Vol 12 (2) ◽  
pp. 1-18
Author(s):  
Jessamyn Dahmen ◽  
Diane J. Cook
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Bayesian Optimization ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Health Events ◽  
Warm Start ◽  
Health Related ◽  
Unsupervised Algorithms

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm-start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically relevant behavior anomalies from over 2M sensor readings collected in five smart homes, reflecting 26 health events. Results indicate that indirectly supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness.

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