scholarly journals A New Neural Network-Based Intrusion Detection System for Detecting Malicious Nodes in WSNs

2020 ◽  
Vol 1 (3) ◽  
pp. 1-8
Author(s):  
Narmatha C ◽  
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Key Management ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Fuzzy Rule ◽  
Sensor Nodes ◽  
Abnormal Behavior ◽  
Malicious Nodes

The Wireless Sensor Networks (WSNs) are vulnerable to numerous security hazards that could affect the entire network performance, which could lead to catastrophic problems such as a denial of service attacks (DoS). The WSNs cannot protect these types of attacks by key management protocols, authentication protocols, and protected routing. A solution to this issue is the intrusion detection system (IDS). It evaluates the network with adequate data obtained and detects the sensor node(s) abnormal behavior. For this work, it is proposed to use the intrusion detection system (IDS), which recognizes automated attacks by WSNs. This IDS uses an improved LEACH protocol cluster-based architecture designed to reduce the energy consumption of the sensor nodes. In combination with the Multilayer Perceptron Neural Network, which includes the Feed Forward Neutral Network (FFNN) and the Backpropagation Neural Network (BPNN), IDS is based on fuzzy rule-set anomaly and abuse detection based learning methods based on the fugitive logic sensor to monitor hello, wormhole and SYBIL attacks.

scholarly journals Study of Intrusion Detection System

2021 ◽  
Vol 9 (VI) ◽  
pp. 788-792
Author(s):  
Miss. Manoshri A. Ghawade
Keyword(s):  
Intrusion Detection ◽  
Key Management ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Secure Routing ◽  
Abnormal Behavior ◽  
Sybil Attack ◽  
Malicious Activity ◽  
Network Intrusion

An intrusion detection system (IDS) could be a device or software application that observes a network for malicious activity or policy violations. Any malicious activity or violation is often reported or collected centrally employing a security information and event management system. Some IDS’s are proficient of responding to detected intrusion upon discovery. These are classified as intrusion prevention systems (IPS). A system that analyzes incoming network traffic is thought as Network intrusion detection system (NIDS). A system that monitors important software files is understood as Host intrusion detection system (HIDS). Wireless sensor networks (WSNs) are vulnerable to different kinds of security threats which will degenrate the performance of the entire network; that may lead to fatal problems like denial of service (DoS) attacks, direction attacks, Sybil attack etc. Key management protocols, authentication protocols and secure routing cannot provide security to WSNs for these varieties of attacks. Intrusion detection system (IDS) could be a solution to the present problem. It analyzes the network by collecting sufficient amount of knowledge and detects abnormal behavior of sensor node(s).

Weighted K Nearest Neighbour-Based Cooperation Intrusion Detection System for Wireless Sensor Networks

2012 ◽  
Vol 263-266 ◽  
pp. 2972-2978
Author(s):  
Ju Long Pan ◽  
Ling Long Hu ◽  
Wen Jin Li ◽  
Hui Cui ◽  
Zi Yin Li
Keyword(s):  
Wireless Sensor Networks ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Nearest Neighbour ◽  
Malicious Nodes ◽  
Detection Scheme

To identify the malicious nodes timely in wireless sensor networks(WSNs), a cooperation intrusion detection scheme based on weighted k Nearest Neighbour(kNN) is proposed. Given a few types of sensor nodes, the test model extracts the properties of sensor nodes related with the known types of malicious nodes, and establishes sample spaces of all sensor nodes which participate in network activities. According to the known node’s attributes sampled, the unknown type sensor nodes are classified based on weighted kNN. Considering of energy consumption, an intrusion detection system selection algorithm is joined in the sink node. Simulation results show that the scheme has a lower false detection rate and a higher detection rate at the same time, and it can preserve energy of detection nodes compared with an existing intrusion detection scheme.

scholarly journals A Machine Learning Based Intrusion Detection System for Mobile Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 461 ◽  
Author(s):  
Amar Amouri ◽  
Vishwa T. Alaparthy ◽  
Salvatore D. Morgera
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Power Level ◽  
Denial Of Service ◽  
Mobile Internet ◽  
Malicious Nodes ◽  
Detection Rates ◽  
Super Node

Intrusion detection systems plays a pivotal role in detecting malicious activities that denigrate the performance of the network. Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) are a form of wireless network that can transfer data without any need of infrastructure for their operation. A more novel paradigm of networking, namely Internet of Things (IoT) has emerged recently which can be considered as a superset to the afore mentioned paradigms. Their distributed nature and the limited resources available, present a considerable challenge for providing security to these networks. The need for an intrusion detection system (IDS) that can acclimate with such challenges is of extreme significance. Previously, we proposed a cross layer-based IDS with two layers of detection. It uses a heuristic approach which is based on the variability of the correctly classified instances (CCIs), which we refer to as the accumulated measure of fluctuation (AMoF). The current, proposed IDS is composed of two stages; stage one collects data through dedicated sniffers (DSs) and generates the CCI which is sent in a periodic fashion to the super node (SN), and in stage two the SN performs the linear regression process for the collected CCIs from different DSs in order to differentiate the benign from the malicious nodes. In this work, the detection characterization is presented for different extreme scenarios in the network, pertaining to the power level and node velocity for two different mobility models: Random way point (RWP), and Gauss Markov (GM). Malicious activity used in the work are the blackhole and the distributed denial of service (DDoS) attacks. Detection rates are in excess of 98% for high power/node velocity scenarios while they drop to around 90% for low power/node velocity scenarios.

scholarly journals Multiple intrusion detection in RPL based networks

2020 ◽  
Vol 10 (1) ◽  
pp. 467 ◽  
Author(s):  
Manjula C. Belavagi ◽  
Balachandra Muniyal
Keyword(s):  
Intrusion Detection ◽  
Detection System ◽  
False Positive Rate ◽  
Denial Of Service ◽  
Network Size ◽  
Sensor Nodes ◽  
Machine Learning Techniques ◽  
Lossy Networks ◽  
Malicious Nodes ◽  
Denial Of Service Attack

<span lang="EN-US">Routing Protocol for Low Power and Lossy Networks based networks consists of large number of tiny sensor nodes with limited resources. These nodes are directly connected to the Internet through the border router. Hence these nodes are susceptible to different types of attacks. The possible attacks are rank attack, selective forwarding, worm hole and Denial of service attack. These attacks can be effectively identified by intrusion detection system model. The paper focuses on identification of multiple intrusions by considering the network size as 10, 40 and 100 nodes and adding 10%, 20% and 30% of malicious nodes to the considered network. Experiments are simulated using Cooja simulator on Contiki operating system. Behavior of the network is observed based on the percentage of inconsistency achieved, energy consumption, accuracy and false positive rate. Experimental results show that multiple intrusions can be detected effectively by machine learning techniques.</span>

scholarly journals Malicious Detection using Secure Mutual Trust Based Routing on an Intrusion Detection System in WSN

2019 ◽  
Vol 8 (3) ◽  
pp. 3144-3150
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Trust Management ◽  
Detection System ◽  
Trust Model ◽  
The Other ◽  
Sensor Nodes ◽  
Mutual Trust ◽  
Malicious Nodes ◽  
Routing Methods

The Lack of infrastructure makes secured data distribution, challenging task in Wireless Sensor Networks (WSNs). In traditional routing methods, either security or routing optimization is addressed separately; however, both are not addressed at similar instances. Hence, if there exists a bottleneck while handling security or routing, where either one is affected by the other. In this paper, Mutual Trust Management (MTM) framework is designed between the sensor nodes is proposed in WSN to identify the malicious nodes. The trust model is connected with an Intrusion Detection System (IDS) to effectively analyse the malicious nodes and routing of packets between the nodes is designed with the Structure of a Multilayer Perceptron (MLP) Network to route the packets through the secured path. The simulations are conducted using the NS-2 setup for validating the trustworthiness and packet delivery through the secured route. The proposed method is compared against the existing methods to test the efficacy of MTM-MLP model and the results show that the MTM-MLP achieves higher detection against ransomware than the other methodsThe Lack of infrastructure makes secured data distribution, challenging task in Wireless Sensor Networks (WSNs). In traditional routing methods, either security or routing optimization is addressed separately; however, both are not addressed at similar instances. Hence, if there exists a bottleneck while handling security or routing, where either one is affected by the other. In this paper, Mutual Trust Management (MTM) framework is designed between the sensor nodes is proposed in WSN to identify the malicious nodes. The trust model is connected with an Intrusion Detection System (IDS) to effectively analyse the malicious nodes and routing of packets between the nodes is designed with the Structure of a Multilayer Perceptron (MLP) Network to route the packets through the secured path. The simulations are conducted using the NS-2 setup for validating the trustworthiness and packet delivery through the secured route. The proposed method is compared against the existing methods to test the efficacy of MTM-MLP model and the results show that the MTM-MLP achieves higher detection against ransomware than the other methods

Development of Intrusion Detection System for Security Threats in Internet of Things Using Artificial Neural Network

2019 ◽  
Vol 16 (8) ◽  
pp. 3242-3245
Author(s):  
R. Ramadevi ◽  
N. R. Krishnamoorthy ◽  
D. Marshiana ◽  
Sujatha Kumaran ◽  
N. Aarthi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Security Threats ◽  
Data Set ◽  
Artificial Neural

Internet of things (IoT) is a revolutionary technology which changes our life and work. Many industry sectors such as manufacturing, transportation, utilities, health care, consumer electronics and automobiles are invested and adopted towards IoT technology. The major inconvenience with IoT is its safety, as it is prone to attack by hackers. Detection Systems are used to detect these intrusions to protect the information and communication systems. Hence it is essential to design an intrusion detection system for security threats of IoT networks. This paper focuses, on the development of Artificial Neural Network (ANN) based Intrusion Detection System for threat analysis in IoT network. KDD-99 data set with Denial of Service (DoS) type attack is used to train and test three different ANN models. In this research, a Feed Forward Back Propagation (FFBP) network is used to detect the DoS attack. The process of optimization of a FFBP network involves comparison of classification accuracy during both training and testing in terms of true positive and false positive rates. For the data set considered the optimised network has achieved 100% efficiency during both training and testing.

scholarly journals HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 834
Author(s):  
Muhammad Ashfaq Khan
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Recurrent Neural Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Network Intrusion Detection ◽  
Complex Nature ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

DECISION MAKING FOR NETWORK HEALTH ASSESSMENT IN AN INTELLIGENT INTRUSION DETECTION SYSTEM ARCHITECTURE

2004 ◽  
Vol 03 (02) ◽  
pp. 281-306 ◽  
Author(s):  
AMBAREEN SIRAJ ◽  
RAYFORD B. VAUGHN ◽  
SUSAN M. BRIDGES
Keyword(s):  
Intrusion Detection ◽  
Computer Security ◽  
Intrusion Detection System ◽  
Detection System ◽  
Health Assessment ◽  
Fuzzy Rule ◽  
Fuzzy Cognitive Maps ◽  
Causal Knowledge ◽  
Knowledge Reasoning ◽  
Decision Engine

This paper describes the use of artificial intelligence techniques in the creation of a network-based decision engine for decision support in an Intelligent Intrusion Detection System (IIDS). In order to assess overall network health, the decision engine fuses outputs from different intrusion detection sensors serving as "experts" and then analyzes the integrated information to present an overall security view of the system for the security administrator. This paper reports on the workings of a decision engine that has been successfully embedded into the IIDS architecture being built at the Center for Computer Security Research, Mississippi State University. The decision engine uses Fuzzy Cognitive Maps (FCM)s and fuzzy rule-bases for causal knowledge acquisition and to support the causal knowledge reasoning process.

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