Intrusion Detection System Using Machine Learning Approach

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.

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.

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.

— By increasing the advantages of network based systems and dependency of daily life with them, the efficient operation of network based systems is an essential issue. Since the number of attacks has significantly increased, intrusion detection systems of anomaly network behavior have increasingly attracted attention among research community. Intrusion detection systems have some capabilities such as adaptation, fault tolerance, high computational speed, and error resilience in the face of noisy information. So, construction of efficient intrusion detection model is highly required for increasing the detection rate as well as decreasing the false detection.. This paper investigates applying the following methods to detect the attacks intrusion detection system and understand the effective of GA on the ANN result: artificial Neural Network (ANN) for recognition and used Genetic Algorithm (GA) for optimization of ANN result. We use KDD CPU 99 dataset to obtain the results; witch shows the ANN result before the efficiency of GA and compare the result of ANN with GA optimization.

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.

2012

Intrusion Detection systems are increasingly a key part of system defence. Various approaches to Intrusion Detection are currently being used but false alarm rate is higher in those approaches. Network Intrusion Detection involves differentiating the attacks like DOS, U2L, R2L and Probe from the Normal user on the internet. Due to the variety of network behaviors and the rapid development of attack fashions, it’s necessary to develop an efficient model to detect all kinds of attacks. Building an effective IDS is an enormous knowledge engineering task. Characteristics of computational intelligence systems such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information fit the requirements of building a good intrusion model. In this paper, we propose a network intrusion detection model based on evolutionary optimization technique called Genetic Network Programming (GNP) with sub attribute utilization mechanism. The proposed model is ...

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

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).

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.

Journal of Computer-Mediated Communication

The following methods detect the attacks intrusion detection system: ANN (artificial neural network) for recognition and GA (genetic algorithm) for optimization of ANN results. We use KDD-CUP dataset to obtain the results, which shows around 0.9998 accuracy of applied methods in detecting the threads. ANN with GA requires 18 features.