Robust event classification for a fiber optic perimeter intrusion detection system using level crossing features and artificial neural networks

2010 ◽  
Author(s):  
Seedahmed S. Mahmoud ◽  
Jim Katsifolis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Fiber Optic ◽  
Detection System ◽  
Level Crossing ◽  
Event Classification ◽  
Artificial Neural

scholarly journals Artificial Neural Networks-Based Intrusion Detection System for Internet of Things Fog Nodes

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 73907-73918
Author(s):  
Jesus Pacheco ◽  
Victor H. Benitez ◽  
Luis C. Felix-Herran ◽  
Pratik Satam
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Artificial Neural

scholarly journals A Novel Intrusion Detection System Based on Neural Networks

2019 ◽  
Vol 292 ◽  
pp. 03017
Author(s):  
Antonios Andreatos ◽  
Vassilios Moussas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intrusion Detection ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
State Of The Art ◽  
Detection System ◽  
Satisfactory Performance ◽  
Time Overhead

This paper proposes a novel intrusion detection system (IDS) based on Artificial Neural Networks (ANNs). The system is still under development. Two types of attacks have been tested so far: DDoS and PortScan. The experimental results obtained by analyzing the proposed IDS using the CICIDS2017 dataset show satisfactory performance and superiority in terms of accuracy, detection rate, false alarm rate and time overhead, compared to state of the art existing schemes.

scholarly journals Network Data Classification through Artificial Neural Networks and GenClust++ Algorithm

2019 ◽  
Vol 8 (10) ◽  
pp. 2743-2750 ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intrusion Detection ◽  
Clustering Algorithm ◽  
Detection System ◽  
Hybrid Approach ◽  
Machine Learning Techniques ◽  
False Alarms ◽  
Internet Applications ◽  
Artificial Neural

Information systems are becoming more and more complex and closely linked due to the exponential use of internet applications. These systems are encountering an enormous amount of traffic, this traffic can be a normal one destined for natural use or it may be a malicious one intended to violate the security of the system. Therefore, a defense method needs to be in place. One of the commonly used tools for network security is the Intrusion Detection System (IDS). An IDS, while ensuring real - time connectivity, tries to identify fraudulent activity using predetermined signatures or pre-established network behavior while monitoring incoming traffic. Intrusion detection systems based on signature or behavior cannot detect new attacks and fall when small deviations occur. Also, current anomaly detection approaches suffer often from high false alarms. As a solution to these problems, machine learning techniques are a new and promising tool for the identification of attacks. In this paper, the authors present a hybrid approach, combining artificial neural networks and a hybrid clustering algorithm based on k-means and genetic algorithm called GenClust++. The final framework leads to a fast, highly scalable and precise packets classification system. We tested our work on the newly published dataset CICIDS 2017. The overall process was fast, showing high accuracy classification results.

scholarly journals Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2538
Author(s):  
Shuang Zhang ◽  
Feng Liu ◽  
Yuang Huang ◽  
Xuedong Meng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spread Spectrum ◽  
High Efficiency ◽  
Detection System ◽  
Direct Sequence Spread Spectrum ◽  
Detection Performance ◽  
Detection Methods ◽  
Direct Sequence ◽  
Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

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