26,704 research outputs found

    The Development of Agent Information for Intrusion Detection

    Get PDF
    As the challenges and problems surround intrusion rises rapidly, the intrusion detection system has been gradually developed. Agent-based approach for intrusion detection system has developed from single to multi agent, and later developed mobile agents in order to increase system's capability to face with a more complex challenge and change. A number of studies had been identified that mobile agent can reduce network traffic, however the study related to intrusion detection using static and mobile agent for finding intruder has not been fully achieved.Keywords:  Information, Intrusion, mobile, network

    Distributed Hierarchical IDS For MANET Over AODV+.

    Get PDF
    In this paper, we introduce background knowledge of wireless ad hoc networking mobile ad hoc network (MANET) as well as intrusion detection system (IDS) and mobile agents. This research study surveys, studies and compares the existing intrusion detection based on mobile agent for mobile ad hoc networks. Based on our best knowledge from previous researches we design distributed hierarchical /D^S inclusive of network-based and host-based intrusion detection system with due consideration to their characteristics on ad hoc on4emand distance vector routing protocol (AODV+)

    Network Attacks Detection by Hierarchical Neural Network

    Get PDF
    Intrusion detection is an emerging area of research in the computer security and net-works with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behavior or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with low false alarm rate. In this paper,we propose to use a hybrid intelligent approach using a combination of classifiers in order to make the decision intelligently, so that the overall performance of the resul-tant model is enhanced. The general procedure in this is to follow the supervised or un-supervised data filtering with classifier or cluster first on the whole training dataset and then the output are applied to another classifier to classify the data. In this re- search, we applied Neural Network with Supervised and Unsupervised Learning in order to implement the intrusion detection system. Moreover, in this project, we used the method of Parallelization with real time application of the system processors to detect the systems intrusions.Using this method enhanced the speed of the intrusion detection. In order to train and test the neural network, NSLKDD database was used. Creating some different intrusion detection systems, each of which considered as a single agent, we precisely proceeded with the signature-based intrusion detection of the network.In the proposed design, the attacks have been classified into 4 groups and each group is detected by an Agent equipped with intrusion detection system (IDS).These agents act independently and report the intrusion or non-intrusion in the system; the results achieved by the agents will be studied in the Final Analyst and at last the analyst reports that whether there has been an intrusion in the system or not. Keywords: Intrusion Detection, Multi-layer Perceptron, False Positives, Signature- based intrusion detection, Decision tree, Nave Bayes Classifie

    Intrusion detection system for the Internet of Things based on blockchain and multi-agent systems

    Get PDF
    With the popularity of Internet of Things (IoT) technology, the security of the IoT network has become an important issue. Traditional intrusion detection systems have their limitations when applied to the IoT network due to resource constraints and the complexity. This research focusses on the design, implementation and testing of an intrusion detection system which uses a hybrid placement strategy based on a multi-agent system, blockchain and deep learning algorithms. The system consists of the following modules: data collection, data management, analysis, and response. The National security lab–knowledge discovery and data mining NSL-KDD dataset is used to test the system. The results demonstrate the efficiency of deep learning algorithms when detecting attacks from the transport layer. The experiment indicates that deep learning algorithms are suitable for intrusion detection in IoT network environment

    Hybrid Multi Agent-Neural Network Intrusion Detection with Mobile Visualization

    Get PDF
    A multiagent system that incorporates an Artificial Neural Networks based Intrusion Detection System (IDS) has been defined to guaranty an efficient computer network security architecture. The proposed system facilitates the intrusion detection in dynamic networks. This paper presents the structure of the Mobile Visualization Connectionist Agent-Based IDS, more flexible and adaptable. The proposed improvement of the system in this paper includes deliberative agents that use the artificial neural network to identify intrusions in computer networks. The agent based system has been probed through anomalous situations related to the Simple Network Management Protocol

    INTRUSION DETECTION SYSTEM USING DYNAMIC AGENT SELECTION AND CONFIGURATION

    Get PDF
    Intrusion detection is the process of monitoring the events occurring in a computer system or network and analysing them for signs of possible incidents, which are violations or imminent threats of violation of computer security policies, acceptable use policies, or standard security practices. An intrusion detection system (IDS) monitors network traffic and monitors for suspicious activity and alerts the system or network administrator. It identifies unauthorized use, misuse, and abuse of computer systems by both system insiders and external penetrators. Intrusion detection systems (IDS) are essential components in a secure network environment, allowing for early detection of malicious activities and attacks. By employing information provided by IDS, it is possible to apply appropriate countermeasures and mitigate attacks that would otherwise seriously undermine network security. However, Increasing traffic and the necessity of stateful analysis impose strong computational requirements on network intrusion detection systems (NIDS), and motivate the need of architectures with multiple dynamic sensors. In a context of high traffic with heavy tailed characteristics, static rules for dispatching traffic slices among sensors cause severe imbalance. The current high volumes of network traffic overwhelm most IDS techniques requiring new approaches that are able to handle huge volume of log and packet analysis while still maintaining high throughput. This paper shows that the use of dynamic agents has practical advantages for intrusion detection. Our approach features unsupervised adjustment of its configuration and dynamic adaptation to the changing environment, which improvises the performance of IDS significantly. KEYWORDS—Intrusion Detection System, Agent Based IDS, Dynamic Sensor Selection. I

    Multi-Agent Security System based on Neural Network Model of User's Behavior

    Get PDF
    It is proposed an agent approach for creation of intelligent intrusion detection system. The system allows detecting known type of attacks and anomalies in user activity and computer system behavior. The system includes different types of intelligent agents. The most important one is user agent based on neural network model of user behavior. Proposed approach is verified by experiments in real Intranet of Institute of Physics and Technologies of National Technical University of Ukraine "Kiev Polytechnic Institute”
    corecore