IJSRSET151370 | Received: 15 June 2015 | Accepted: 21 June 2015 | May-June 2015 [(1)3: 309-315] © 2015 IJSRSET | Volume 1 | Issue 3 | Print ISSN : 2395-1990 | Online ISSN : 2394-4099 Themed Section: Engineering and Technology 309 Efficient calculation of fitness function by calculating reward Penalty for a GA-based Network Intrusion Detection System Prof. Jahnavi. S. Vithalpura, Prof. H. M. Diwanji Department of Computer-IT, L. D. College of engineering, Ahmadabad, Gujarat, India ABSTRACT 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. Keywords: Genetic Algorithm, Intrusion, Network Intrusion Detection System, Fitness Function, Reward Penalty. I. INTRODUCTION Intrusion detection system is a process of monitoring network activities [1], presented and available in computer network and investigate it’s for detection of violating threats which could affect computer security strategies and security practices. Now-a-day our network system should play an important role in society to prevent computers from malicious threats. At a situation of transferring files and communication to be held on the network we should probably focusing network security. Intruders are more available in network to capture important files and materials and do some malicious activities in the computer system. Attacks are categorized into [2], four types. There are DOS, U2R, R2L and Probe. These attacks are classified by some optimization methodologies. Normally, intrusions are causes damage to the computer system resources with the term of unauthorized activities (modifications) of important files and folders presented in the system. In [3], especially told information about intrusion detection and also told its two kinds of detection principles. There are anomaly detection and misuse detection. In [4] told methods of classification of intrusion detection system. Three kinds of methods to be introduced are audit source location, detection method and detection paradigm. In audit source location consider the factors of network packets, application log files, host log files and IDS sensor alerts. Behaviour and knowledge based policies are executed using detection methods. Finally, in detection paradigm to be considering factors of state based and transition based detections. Usually, networking attacks are detecting and preventing by some classification methods. Normally used classification methods are svm, genetic and k-NN. Some of the network intrusion detection and prevention methods are discussed in [5]. In our proposed system we have design new the optimal fitness function solution here; fitness function can be calculated by reward