Intrusion Detection System with FGA and MLP Algorithm

Normally, The Internet grows rapidly and most useful in each domain but network vulnerability and intrusions are still an important issue that causes attacks. A good system to detect the illegal user is to monitoring the packets and using the different algorithms, methods and applications which are created and implemented to solve the problem of detecting the attacks in intrusion detection systems. We consider network intrusion detection using supervised learning algorithm to classify attacks in the datasets. Fuzzy rule is a machine learning algorithm that can classify network attack data and protect the system from damage, while a genetic algorithm is an optimization algorithm that can help finding appropriate fuzzy rule and give the optimal solution. We consider both well-known KDD99 dataset. We evaluate our IDS in terms of detection speed, detection rate and false alarm rate.

PROF MUHAMMAD NAFIU , 2024

Network security is becoming an issue of paramount importance in the information technology era. Nowadays with the dramatic growth of communication and computer networks, security has become a critical subject for computer system. Intrusion detection is the art of detecting computer abuse and any attempt to break the networks. Intrusion detection system is an effective security tool that helps to prevent unauthorized access to applications are created and implemented to solve the problem of detecting the attacks in intrusion detection systems. Most methods detect attacks and categorize in two groups, normal or threat. One of the most promising areas of research in the area of Intrusion Detection deals with the applications of the Artificial Intelligence (AI) techniques. This proposed system presents a new approach of intrusion detection system based on artificial neural network. Architecture is used for Intrusion Detection System. The performance and evaluations are performed by using the set of benchmark data from a KDD (Knowledge discovery in Database) dataset. The proposed system detects the attacks and classifies them in six groups.

2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2013

ABSTRACT In this work, we consider network intrusion detection using fuzzy genetic algorithm to classify network attack data. Fuzzy rule is a machine learning algorithm that can classify network attack data, while a genetic algorithm is an optimization algorithm that can help finding appropriate fuzzy rule and give the best/optimal solution. In this paper, we consider both wellknown KDD99 dataset and our own network dataset. The KDD99 dataset is a benchmark dataset that is used in various researches while our network dataset is an online network data captured in actual network environment. We evaluate our IDS in terms of detection speed, detection rate and false alarm rate. From the experiment, we can detect network attack in real-time (or within 2-3 seconds) after the data arrives at the detection system. The detection rate of our algorithm is approximately over 97.5%.

International Journal Of Engineering And Computer Science, 2016

In this paper, we present an intrusion detection model based on genetic algorithm and neural network. The key idea is to take advantage of classification abilities of genetic algorithm and neural network for intrusion detection system. The new model has ability to recognize an attack, to differentiate one attack from another i.e. classifying attack, and the most important, to detect new attacks with high detection rate and low false negative. 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 this System. We used the KDD99 benchmark dataset and obtained reasonable detection rate

2015

Due to tremendous development of Internet and Computer networks in last one decade the problem of security of latest computer systems from hacking, denial-ofservice, and other threats has become a major source of threat. As in today’s Modern world a lot of corporate houses are turning towards web services as a main and major source of income this problem is even bigger in terms of security measures as it concerns the whole world and a lot of Income effecting economic system of many Developed and Developing Countries. Security Threats posses significant problems to these countries as distributed attacks can make their cyber systems inoperable for a long period of time. This happens very often so an entire area of research, called Intrusion Detection is developed for detecting the activity. Many Soft Computing based methods have been proposed for intrusion detection has been proposed till now. A Multi Layer Perceptron (MLP) is used for intrusion detection based on an off-line analysis...

As networks grow both in importance and size, there is an increasing need for effective security monitors such as Network Intrusion Detection System to prevent such illicit accesses. Intrusion Detection Systems technology is an effective approach in dealing with the problems of network security. In this paper, we present an intrusion detection model based on hybrid fuzzy logic and neural network. The key idea is to take advantage of different classification abilities of fuzzy logic and neural network for intrusion detection system. The new model has ability to recognize an attack, to differentiate one attack from another i.e. classifying attack, and the most important, to detect new attacks with high detection rate and low false negative. Training and testing data were obtained from the Defense Advanced Research Projects Agency (DARPA) intrusion detection evaluation data set.

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.

2018

Almost of them doing job through internet like, online business , shopping, transfer money, voice call, online application filling data in hospital, booking bus ticket, train, apply jobs etc., such way may attack intruder easily if not secure. Example mobile apps getting updated daily, intrusion also being upgraded rapidly. Intruder can monitor or detection with the help of IDS. Two type of IDS are software or hardware and it helpful in safe guard the entire network or local network, the activities of the system and also helpful in finding the harmful operations. As the hacking techniques getting stronger daily so for improve the IDS but still not having strong intrusion detection system. In this paper we present IDS’s various types of attacks and it’s features to detect the intrusions on a network and possible ways to secure those intrusions. International Journal of Pure and Applied Mathematics Volume 119 No. 15 2018, 1517-1525 ISSN: 1314-3395 (on-line version) url: http://www.aca...

— 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.

2016

Now a day is very important to maintain a high level security to keep information safe and secure between different organizations. The data communication over internet is always under threat of intrusions and misuses. Thus the intrusion Detection Systems have become a need in network security. Fuzzy logic and Genetic Algorithm are two techniques that can use combine to classify network attack information. Fuzzy rule is a machine learning algorithm that can classify network attack data, while a genetic algorithm is an optimization algorithm that can use to find appropriate fuzzy rule and give the best solution. The objective of this paper is to describe a fuzzy genetic based learning algorithm and discuss its usage to detect intrusion in a computer network.