Research and Implementation of Intrusion Detection Based Genetic Algorithm

2014 ◽  
Vol 631-632 ◽  
pp. 946-951 ◽  
Author(s):  
Guang Cai Cui ◽  
Bai Tong Liu
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Real Time ◽  
Original Data ◽  
Network Data ◽  
Initial Population ◽  
Classification Rules ◽  
Detection Technology ◽  
Network Intrusions ◽  
Data Source

For traditional intrusion detection technology, the lack of intelligent and self-adaptive has become increasingly prominent when they cope with unknown attacks. A method based on genetic algorithm was presented for discovering and learning the intrusion detection rules. This algorithm uses the network data packet as an original data source, after pretreatment, initialized them to be the initial population of the genetic algorithm, then derive the classification rules. These rules were used to detect or classify network intrusions in a real-time network environment, selecting the intrusion packets. The experiment proves the efficiency of the presented method.

Research on Rule Extraction Technology Based on Genetic Algorithm in Intrusion Detection

2013 ◽  
Vol 760-762 ◽  
pp. 857-861
Author(s):  
Hui Ling Guo
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Fitness Function ◽  
Adaptive Method ◽  
Rule Base ◽  
Rule Generation ◽  
Extraction Technology ◽  
Audit Data ◽  
Network Intrusions

It is necessary to establish the rule base before intrusion detection. An adaptive method based on genetic algorithms was presented for learning the intrusion detection rules in order to realize the automation of attack rule generation. The genetic algorithm is employed to derive a set of classification rules from network audit data, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify network intrusions in a real-time environment.

scholarly journals Intrusion Detection System using Genetic Algorithm and Data Mining: An Overview

2012 ◽  
pp. 118-122
Author(s):  
V.P. Kshirsagar ◽  
Sonali M. Tidke ◽  
S.S. Vishnu
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Maintenance Cost ◽  
Detection Systems ◽  
Detection Technology

Network security is of primary concerned now days for large organizations. Various types of Intrusion Detection Systems (IDS) are available in the market like Host based, Network based or Hybrid depending upon the detection technology used by them. Modern IDS have complex requirements. With data integrity, confidentiality and availability, they must be reliable, easy to manage and with low maintenance cost. Various modifications are being applied to IDS regularly to detect new attacks and handle them. In this paper, we are focusing on genetic algorithm (GA) and data mining based Intrusion Detection System.

The Design of FPGA-Based Real Time Intrusion Detection NIC

2011 ◽  
Vol 58-60 ◽  
pp. 2585-2591
Author(s):  
Bing Yuan Cheng ◽  
Kai Jin Qiu ◽  
Zu Yong Yang
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Response Speed ◽  
Pci Bus ◽  
Operation System ◽  
Detection Algorithms ◽  
Network Bandwidth ◽  
Detection Technology ◽  
Organic Combination ◽  
Ideal Method

The amount of intrusion detection calculation based on software is heavy, which can not satisfy the needs of modern network bandwidth; the intrusion detection technology based on hardware is an ideal method for accelerating network processing. The thesis proposes a design scheme for FPGA-based real time intrusion detection NIC, and introduces the hardware and software structure of the system n details. The system communicates with the operation system directly via PCI bus, achieves the organic combination of software detection and hardware detection, and overcomes the slow response speed of the system when only software is used for intrusion detection. In system hardware detection, with FPGA as core, arranging various intrusion detection algorithms in FPGA for parallel running can improve the real time and reliability of the system.

The Role of Data Mining in Intrusion Detection Technology

2008 ◽  
pp. 463-473 ◽  
Author(s):  
Amalia Agathou ◽  
Theodoros Tzouramanis
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Real Time ◽  
Computer System ◽  
Intrusion Detection System ◽  
Detection System ◽  
The Internet ◽  
The Past ◽  
Detection Technology

Over the past few years, the Internet has changed computing as we know it. The more possibilities and opportunities develop, the more systems are subject to attack by intruders. Thus, the big question is about how to recognize and handle subversion attempts. One answer is to undertake the prevention of subversion itself by building a completely secure system. However, the complete prevention of breaches of security does not yet appear to be possible to achieve. Therefore these intrusion attempts need to be detected as soon as possible (preferably in real time) so that appropriate action might be taken to repair the damage. This is what an intrusion detection system (IDS) does. IDSs monitor and analyze the events occurring in a computer system in order to detect signs of security problems. However, intrusion detection technology has not yet reached perfection. This fact has provided data mining with the opportunity to make several important contributions and improvements to the field of IDS technology (Julisch, 2002).

scholarly journals Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
E. Bertino ◽  
M. R. Jahanshahi ◽  
A. Singla ◽  
R.-T. Wu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Original Data ◽  
Local Analysis ◽  
Civil Infrastructure ◽  
Time Requirements ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
The Times ◽  
Acquisition Processes

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

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