Neural Network and Artificial Immune Systems for Malware and Network Intrusion Detection

2010 ◽  
pp. 485-513 ◽  
Author(s):  
Vladimir Golovko ◽  
Sergei Bezobrazov ◽  
Pavel Kachurka ◽  
Leanid Vaitsekhovich
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Artificial Immune Systems ◽  
Network Intrusion Detection ◽  
Artificial Immune ◽  
Immune Systems ◽  
Network Intrusion

A Network Intrusion Detection Model Based on Artificial Immune

2011 ◽  
Vol 361-363 ◽  
pp. 687-690 ◽  
Author(s):  
Xin Xiao ◽  
Rui Rui Zhang
Keyword(s):  
Intrusion Detection ◽  
Clustering Algorithm ◽  
False Positive Rate ◽  
Artificial Immune Systems ◽  
Affinity Maturation ◽  
Network Intrusion Detection ◽  
Artificial Immune ◽  
Detection Model ◽  
Model Based ◽  
Network Intrusion

For the existing artificial immune systems applied to network intrusion detection have some shortages, an improved network intrusion detection model based on the dynamic clone selection algorithm which was put forward by Kim is proposed. The model introduces the concept of self group, which is obtained by the clustering algorithm AiNet and represents common features of normal data. The self group deals with network data before they are tested by detectors. In addition, the model adopts a design of distributed network intrusion detection, and a central server manages all the immune cells, receives vaccines and vaccinats the whole network detection hosts. Experimental results show that the number of selves and detectors are reduced, the process of affinity maturation for the detector population is speeded up, and the model achieves higher detection rate and lower false positive rate with the evolution generation increases.

Artificial Immune Systems for IT-Security (Künstliche Immunsysteme für IT-Sicherheit)

2006 ◽  
Vol 48 (3) ◽  
Author(s):  
Thomas Stibor ◽  
Claudia Eckert ◽  
Jonathan Timmis
Keyword(s):  
Information Processing ◽  
Immune System ◽  
Artificial Immune Systems ◽  
Processing System ◽  
Database Security ◽  
Network Intrusion Detection ◽  
It Security ◽  
Artificial Immune ◽  
Immune Systems ◽  
Network Intrusion

SummaryThe immune system is an impressive information processing system with many appealing properties for solving problems. Artificial immune systems are a paradigm inspired by the immune system and are used for solving computational and information processing problems. In this paper, we outline two different immune-inspired approaches typically used for IT-security problems. Specifically, we present one of the first proposed immune inspired approaches for network intrusion detection, this is then complimented with an overview of recent investigations on the latest immunological theories and how they may be exploited in IT-security. We then present an artificial immune system concept for database security which encompasses issues such as confidentiality of database information and prevention of privacy-preserving data mining.

scholarly journals HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 834
Author(s):  
Muhammad Ashfaq Khan
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Recurrent Neural Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Network Intrusion Detection ◽  
Complex Nature ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

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