Genetic Algorithm Methodology for Intrusion Detection System

International Journal of Computer Science and Informatics

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.

2014

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).

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.

2010

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.

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

2019

Developing a better intrusion detection systems (IDS) has attracted many researchers in the area of computer network for the past decades. In this paper, Genetic Algorithm (GA) is proposed as a tool that capable to identify harmful type of connections in a computer network. Different features of connection data such as duration and types of connection in network were analyzed to generate a set of classification rule. For this project, standard benchmark dataset known as KDD Cup 99 was investigated and utilized to study the effectiveness of the proposed method on this problem domain. The rules comprise of eight variables that were simulated during the training process to detect any malicious connection that can lead to a network intrusion. With good performance in detecting bad connections, this method can be applied in intrusion detection system to identify attack thus improving the security features of a computer network.

The Internet has become a part of daily life and an essential tool today. Internet has been used as an important component of business models. Therefore, It is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations.

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.

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.

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...