Fitness Function for Genetic Algorithm used in Intrusion Detection System (original) (raw)
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Analysis of Fitness Function in Designing Genetic Algorithm Based Intrusion Detection System
Network Intrusion detection system is tool to monitor & identify intrusion in computers networks. The genetic algorithm is employed to derive a set of classification rules from network audit data. Different data sets are used as an audit data .From these data sets only specific features are selected and represented as chromosomes, which represent rules. The weighted sum model, support-confidence framework or reward penalty framework is utilized as fitness function to judge the quality of each rule. Best rule collection or knowledge base improves IDS performance by improving detection rate and reducing false alarm rate. The weighted sum model is generally more helpful for identification of network anomalous behaviors. The support –confidence framework is simply identifying network intrusions or precisely classifying the types of intrusions. Reward penalty technique used to give reward to the good chromosome and to apply penalty on the bad chromosome. This paper gives detail study about research carried out in fitness function of genetic based intrusion detection system.
Our network is facing a rapidly evolving threat landscape full of modern applications, exploits, malware and attack strategies that are capable of avoiding traditional methods of detection. Intrusion detection can perform the task of monitoring usability systems to detect any apparition of insecure states. To overcome above mentioned issues we have employed genetic algorithm to improve detection rate of intrusion detection system. To generate healthy rule pool we have focused in design of fitness function. We have proposed a new fitness function based on reward & penalty. This function make chromosome stronger by applying reward and remove weakness from it by deducting penalty. So such a healthy chromosomes generates a best fit population which is reducing false alarm rate and increasing a detection rate. In our work, we have classified a dataset as a normal record or attack record using seven network features and calculated detection rate and false alarm rate. Further we have classified DOS, Probe, and U2R and R2L type of attack from attack cluster. We measured improved efficiency of proposed system by observing improvement in detection rate and reduction in false alarm rate.
Genetic Algorithm Methodology for Intrusion Detection System
International Journal of Engineering Research and Technology (IJERT), 2012
https://www.ijert.org/genetic-algorithm-methodology-for-intrusion-detection-system https://www.ijert.org/research/genetic-algorithm-methodology-for-intrusion-detection-system-IJERTV1IS10450.pdf 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.
GENETIC APPROACH TO INTRUSION DETECTION SYSTEM
IAEME PUBLICATION, 2019
This paper exhibits a general diagram of hereditary methodology interruption discovery frameworks and the strategies utilized in these frameworks, giving brief purposes of the structure standards and the significant patterns. In this paper, we will concentrate on the hereditary calculation strategy and how it could be utilized in interruption location frameworks giving a few instances of frameworks and analyses proposed in this field. At that point utilized a man-made brainpower procedures are broadly utilized here, for example, hereditary calculations.
Genetic Algorithms in Intrusion Detection Systems: A Survey
The paper provides an introduction to the basic concepts of intrusion detection and genetic algorithms. The generic implementation of genetic algorithms using pseudo code is presented. Pseudo code for genetic algorithm based intrusion detection method is also included for clear understanding. The paper also provides an overview of the advantages and disadvantages of genetic algorithms in general, and as applied to intrusion detection in particular. This survey will provide helpful insight into the related literature and implementation of genetic algorithms in intrusion detection systems. It will also be a good source of information for people interested in the genetic algorithms based intrusion detection systems.
Intrusion Detection System Using Genetic Algorithm-A Review
2012
Today we are suffering from many problems because of intruder interference in our communication with other person/organisation. We need a very safe and secure intrusion detection system. So, intrusion detection has become an important area of research The existing systems are not completely flawless and secure. So, there is the need to improve the existing system. In this paper, firstly we are discussing about the existing network intrusion detection system SNORT and its drawback then discuss about different research areas which were taking place to improve the performance of existing system with the help of genetic algorithm. Keyword: Intrusion Detection System, Genetic Algorithm, Snort, Network attack, Denial of service.
Genetic algorithm for intrusion detection system in computer network
Indonesian Journal of Electrical Engineering and Computer Science
Internet connection nowadays has become one of the essential requirements to execute our daily activities effectively. Among the major applications of wide Internet connections is local area network (LAN) which connects all internet-enabled devices in a small-scale area such as office building, computer lab etc. This connection will allow legit user to access the resources of the network anywhere as long as authorization is acquired. However, this might be seen as opportunities for some people to illegally access the network. Hence, the occurrence of network hacking and privacy breach. Therefore, it is very vital for a computer network administrator to install a very protective and effective method to detect any network intrusion and, secondly to protect the network from illegal access that can compromise the security of the resources in the network. These resources include sensitive and confidential information that could jeopardise someone’s life or sovereignty of a country if man...
An Evolutionary Approach to Intrusion Detection System using Genetic Algorithm
2012
With the rapid change and development in the sector of Information Technology and in Network technologies; the value of data and information is also increased. Today lot of valuable data is generated using many computers based application and stored back to the company database. But unfortunately, the threat to the same data is also increasing rapidly. So, development of a proper Intrusion Detection System which provides a right alarm is a hot topic today. There are many areas which helps to build such devices and software applications like Data mining techniques, network protocol system, decision tree, clustering, SNORT, Genetic Algorithm etc. This paper presents a technique of applying evolutionary algorithm i.e. Genetic Algorithm to Intrusion Detection System. It also provides a brief introduction to the parameters and evolution process of a GA and how to implement it in real IDS. Keywords—Data mining, DDOS, Evolutionary algorithm, Genetic Algorithm, Intrusion, IDS, SNORT, Threats