Analysis of Fitness Function in Designing Genetic Algorithm Based Intrusion Detection System (original) (raw)
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International Conference on Aerospace Sciences & Aviation Technology, 2009
The purpose of the work described in this paper is to provide an intrusion detection system (IDS), by applying genetic algorithm (GA) to network intrusion detection system. Parameters and evolution process for GA are discussed in detail and implemented. This approach uses information theory to filter the traffic data and thus reduce the complexity. We use a linear structure rule to classify the network behaviors into normal and abnormal behaviors. This approach applied to the KDD99 benchmark dataset and obtained high detection rate up to 99.87% as well as low false positive rate 0.003%. Finally the results of this approach compared with available machine learning techniques.
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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.
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.
An effective approach to network intrusion detection system using genetic algorithm
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ABSTRACT This paper presents a general overview of Intrusion Detection Systems and the methods used in these systems, giving brief points of the design principles and the major trends. Artificial intelligence techniques are widely used in this area such as fuzzy logic and Genetic algorithms. In this paper, we will focus on the Genetic algorithm technique and how it could be used in Intrusion Detection Systems giving some examples of systems and experiments proposed in this field.
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...
Fitness Function for Genetic Algorithm used in Intrusion Detection System
Computer network usage increased rapidly at the last decades, the intruders tried to satisfy their needs by many types of attack depending on the intruder objectives, this encourage the researchers to find more and more solutions to detect those attacks. Intrusion Detection System used to detect the attack. Genetic Algorithm used to support IDS. Fitness Function is helpful in chromosome evaluation which is a Genetic Algorithm part. The problem is to find a suitable Fitness Function for a chromosome evaluation to get a solution for Intrusion Detection. This paper suggests a new reasonable Fitness Function using Reward-Penalty technique to evaluate population chromosomes efficiently. This technique used to give reward to the good chromosome and to apply a penalty on the bad chromosome. This paper will show the Fitness Function, discuss it and compare it with another Fitness Function to check its validity.
Study on Intrusion Detection Systems Using Genetic Algorithm
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With the extension in computer networks and appearing new attacks, seems that security is more necessary than before. Intrusion Detection System (IDS) is one of the most important methods to develop security in computer networks. There are different methods for IDS improvement. Machine learning is one of these methods and an approach for improving IDS with machine learning using Genetic Algorithm (GA).
An Implementation of Intrusion Detection System Using Genetic Algorithm
International Journal of Network Security & Its Applications, 2012
Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99 benchmark dataset and obtained reasonable detection rate.