Genetic Algorithms in Intrusion Detection Systems: A Survey

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.

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

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...

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.

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.

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.

2015

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. Intrusion detection is one of the important security constraints for maintaining the integrity of information. Various approaches have been applied in past that are less effective to curb the menace of intrusion. There are large amount of network traffic captured in terms of number of features and number of record, so it is very difficult to process all the network traffic before making any decision about normal or abnormal. So it is having longer training time and complexity. Thus the purpose is to provide an intrusion detection system (IDS), by modifying the genetic algorithm to network intrusion detection system. As we have applied attribute subset reduction on the basis of Information gai...

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.

2017

Abstract— In order to provide complete security in a computer system and to prevent intrusion, intrusion detection systems (IDS) are required to detect if an attacker crosses the firewall, antivirus, and other security devices. Data and options to deal with it. In this paper, we are trying to provide a model for combining types of attacks on public data using combined methods of genetic algorithm and neural network. The goal is to make the designed model act as a measure of system attack and combine optimization algorithms to create the ultimate accuracy and reliability for the proposed model and reduce the error rate. To do this, we used a feedback neural network, and by examining the worker, it can be argued that this research with the new approach reduces errors in the classification.with the rapid development of communication and information technology and its applications, especially in computer networks, there is a new competition in information security and network security.

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.