A comparative performance evaluation of intrusion detection techniques for hierarchical wireless sensor networks

Objectives: The main aim of this paper is security by applying Intelligent Intrusion detection System to detect one of the critical attack know as sinkhole attack in Wireless Sensor network. Methods/Analysis: The major problem occurs while transmitting the data is security. The malicious node attracts the packets from the other normal sensor nodes and drops the sensitive packets which may leads to selective forwarding attack and black hole attack. Findings: In the existing system, Leader based intrusion detection system is used to monitor the surrounding node. Here the leader can monitor the nodes only region wise and does not have any clustering technique. By using the clustering technique we can monitor the surrounding nodes in efficient manner and better solution. In the proposed system, an Intelligent Intrusion Detection System (IIDS) mechanism to detect the intruder in the network, which uses a Hybrid Approach to detect and prevent sinkhole attack, with two approaches such as Zone-Based Leader Election Method and Leader Based Monitoring. In Zone-Based Leader Election method, a set of nodes is located in different regions and a Cluster Based Zone Leader (CBZL) is allocated to every region in the network. In the Leader Based Monitoring Approach, when a node gets detected as a compromised node, it communicates the status of that node to the other leader within the WSN. Application/Improvement: This technique can identify the intruder node and improve the efficiency of the network. As a simulation results, we are implementing in Network Simulator. In environment monitoring and military applications the proposed approach can be deployed.

Wireless sensor networks cannot be secured against internal attacker with only cryptographic techniques because their nodes are not physically protected and can be easily captured by the attacker. Therefore, in this work we consider intrusion detection systems that monitor behavior of sensor nodes and detect the malicious ones among them. Our work is exploratory in that we propose metrics for evaluation of these systems. There are evaluation metrics for intrusion detection systems for conventional wired and wireless networks. However, to our best knowledge there is no work that proposes evaluation metrics for intrusion detection systems for wireless sensor networks. We divide the proposed metrics into two groups. In the first group there are metrics for detection techniques without a response mechanism. In the second group there are metrics for detection techniques together with a response mechanism. The proposed metrics will help an administrator of a network to choose the best intrusion detection system from a set of systems or to optimize a configuration of a certain intrusion detection system for a given network with a particular topology, sensor nodes capabilities and anticipated types of attack.

In this paper, we propose an Intrusion Detection System (IDS) against Sinkhole attack in wireless sensor networks with mobile sink. In the detection model, the network area is divided into a flat grid of cells, and we use the signature-based technique, which is represented by the detection rate of a cell, to distinguish between real and fake sink nodes. The proposed IDS considers two types of sink mobility: periodic and random. In addition, as the cell leaders do not activate their IDS agent simultaneously, the additional energy consumption incurred by the IDS is low. Simulation results show the efficiency of the proposed IDS in terms of detection rate, efficiency, and energy consumption.

Wireless sensor networks (WSNs) are increasingly being used for data monitoring and collection purposes. Typically, they consist of a large number of sensor nodes that are used remotely to collect data about the activities and conditions of a particular area, for example, temperature, pressure, motion. Each sensor node is usually small, inexpensive, and relatively easy to deploy compared to other sensing methods. For this reason, WSNs are used in a wide range of applications and industries. However, WSNs are vulnerable to different kinds of security threats and attacks. This is primarily because they are very limited in resources like power, storage, bandwidth, and processing power that could have been used in developing their defense. To ensure their security, an effective Intrusion detection system (IDS) need to be in place to detect these attacks even under these constraints. Today, traditional IDS are less effective as these malicious attacks are becoming more intelligent, frequent, and complex. Denial of service (DOS) attack is one of the main types of attacks that threaten WSNs. For this reason, we review related works that focus on detecting DoS attacks in WSN. In addition, we developed and implemented several Deep learning (DL) based IDS. These systems were trained on a specialized dataset for WSNs called WSN-DS in detecting four types of DoS attacks that affects WSNs. They include the Blackhole, Grayhole, Flooding, and Scheduling attacks. Finally, we evaluated and compared the results and we discuss possible future works.

Enhancing cyber security in WSN using optimized self-attention-based provisional variational auto-encoder generative adversarial network

A Wireless Sensor Network (WSN) encompasses interconnected Sensor Nodes (SNs) that interact wirelessly to collect and transfer data. Security in the context of WNS refers to protocols and measures implemented for the overall functionality of the network, along with protecting the availability, confidentiality, and integrity of data against tampering, unauthorized access, and other possible security risks. An Intrusion Detection System (IDS) utilizing Deep Learning (DL) and Feature Selection (FS) leverages advanced methods to enhance effectiveness in the detection of malicious activities in a network by enhancing relevant data features and leveraging the power of Deep Neural Networks (DNNs). This study presents a Dynamic Arithmetic Optimization Algorithm within a DL-based IDS (DAOADL-IDS) in WSNs. The purpose of DAOADL-IDS is to recognize and classify intrusions in a WSN using a metaheuristic algorithm and DL models. To accomplish this, the DAOADL-IDS technique utilizes a Z-score data normalization approach to resize the input dataset in a compatible format. In addition, DAOADL-IDS employs a DAOA-based FS (DAOA-FS) model to select an optimum set of features. A Stacked Deep Belief Network (SDBN) model is employed for the Intrusion Detection (ID) process. The hyperparameter selection of the SDBN model is accomplished using the Bird Swarm Algorithm (BSA). A wide experimental analysis of the proposed DAOADL-IDS method was performed on a benchmark dataset. The performance validation of the DAOADL-IDS technique showed an accuracy of 99.68%, demonstrating superior performance over existing techniques under various measures.

Wireless Sensor Networks (WSNs) have become a hot research topic in recent years. They have many potential applications for both civil and military tasks. However, the unattended nature of WSNs and the limited computational and energy resources of their nodes make them susceptible to many types of attacks. Intrusion detection is one of the major and efficient defence methods against attacks in a network infrastructure. Intrusion Detection Systems can be seen as the second line of defence and they complement the security primitives that are adopted in order to prevent attacks against the computer network being protected. The peculiar features of a wireless sensor network pose stringent requirements to the design of intrusion detection systems. In this paper, we propose a hybrid, lightweight, distributed Intrusion Detection System (IDS) for wireless sensor networks. This IDS uses both misuse-based and anomaly-based detection techniques. It is composed of a Central Agent, which performs highly accurate intrusion detection by using data mining techniques, and a number of Local Agents running lighter anomaly-based detection techniques on the motes. Decision trees have been adopted as classification algorithm in the detection process of the Central Agent and their behaviour has been analysed in selected attacks scenarios. The accuracy of the proposed IDS has been measured and validated through an extensive experimental campaign. This paper presents the results of these experimental tests.

Wireless sensor networks (WSNs) are gaining popularity in a variety of applications, including smart homes, industrial facilities, and environmental monitoring systems. However, using WSNs exposes them to a variety of attacks that might have major effects, such as data loss, privacy breaches, and safety issues. As a result, understanding the sorts of threats and detection/prevention strategies in WSNs is critical to ensuring their security, especially from an educational perspective. We present an overview of the sorts of attacks that can occur in WSNs, including denial of service attacks, spoofing attacks, jamming attacks, physical assaults, and node compromise attacks. We also examine encryption, authentication and authorization procedures, intrusion detection systems, and physical security measures as methods for detecting and blocking attacks in WSNs. We examine the benefits and drawbacks of each strategy and give case studies to show their efficacy. Finally, we propose future research directions, such as the development of more effective intrusion detection systems and the enhancement of physical security measures. We can assure the security and reliability of WSNs in diverse applications by knowing the types of attacks and detection/prevention mechanisms.

Wireless sensor networks have been widely applied in many areas due to their unique characteristics. These have exposed them to different types of active and passive attacks. In the literature, several solutions have been proposed to mitigate these attacks. Most of the proposed solutions are too complex to be implemented in wireless sensor networks considering the resource-constraint of sensor nodes. In this work, we proposed a hierarchical trust mechanism based on clustering approach to detect and prevent denial of service attacks in wireless sensor networks. The approach was validated through simulation using Network Simulator (NS2). The following metrics were used to evaluation the proposed scheme: packet delivery ratio, network lifetime, routing delay, overhead, and number of nodes. The proposed approach is capable of detecting compromised sensor nodes vulnerable to a denial of service attacks. Moreover, it is able to detect all sensed data that have been compromised during transmission to the base station. The results show that our method can effectively detect and defend against denial of service attacks in sensor wireless sensor networks.

The latest wireless sensor network (WSN) developments in critical applications have introduced security risks, like jamming. Intrusion Detection System (IDS) in WSN is the method of recognizing malevolent or unauthorized activities in the network. The intruder's presence to launch different attacks within the network cannot be disregarded. Despite a great deal of effort by the researcher workers, IDS still experienced difficulties enhancing recognition performance while minimizing the false alarm rate and identifying novel intrusions. Recently, Deep Learning (DL) and Machine Learning (ML) based IDS system has been deployed as promising solution to effectively identify intrusion across the network. Therefore, the study presents a Moth Search Optimization with DL-based Intrusion Detection (MSODL-ID) method in the WSN. The MSODL-ID technique aims to effectually identify the occurrence of malicious activities or intrusions in the network. To accomplish this, the MSODL-ID technique undergoes two stages of preprocessing: data conversion and data scaling. In addition, the MSODL-ID technique employs Convolutional Recurrent Neural Network (CRNN) model with a Hopfield layer for intrusion detection purposes. For optimal hyperparameter selection of the CRNN model, the MSO algorithm is used and thereby enhances the classification performance of the CRNN model. The stimulation analysis of the MSODL-ID system is tested by means of Kaggle datasets, and the outcomes exhibit the promising performance of the MSODL-ID system over other current DL approaches.

Intrusion Detection Systems (IDSs) have played a significant responsibility in recognizing and preventing security attacks in Wireless Sensor Networks (WSNs). Modelling of IDS should be done in WSN to guarantee dependability and security of WSN services. In this work, an approach is designed to detect various kinds of clone attack in WSN. In specific, an Adaptive random Forest based Multi-objective Cuckoo Search algorithm (RF-MOCS) is designed to identify the source of clone attack using KDD cup dataset. The proposed model provides significant performance in terms of accuracy, sensitivity, specificity, F-measure respectively. The proposed design shows better trade off when compared to existing techniques like ANN, Naive bayes, SVM.

Insiders might have incentives and objectives opposed to those of the belonging organization. It is hard to detect them because of their privileges that partially protect them. In Wireless Sensor Networks (WSNs), significant security issues arise, including compromised nodes by insiders that disrupt the normal network operation. Immediate defensive actions to isolate malicious nodes would mitigate any related impacts. A multiplayer game model is proposed as a solution to the problem of insider attacks in WSNs, the Game of Wireless Sensor Networks (GoWiSeN). It is an imperfect information game, formulated with the use of non-cooperative game theory, holding the assumption that all players are rational. The model consists of several Local Intrusion Detection Systems (LIDSs), which are located to different nodes and communicate with a Global Intrusion Detection System (GIDS). Each LIDS gives suggestions whether the monitoring node is trusted or not. The game is being played between a potential attacker, the nodes and the GIDS. The GIDS is responsible for making a final decision and for isolating a compromised node in case of an internal attack. The theoretical model represents these interactions in an extensive form game. The formal elements of the game are specified, the outcomes of the game are quantified by first specifying players’ preferences, and then, by using the von Neumann-Morgenstern utility function, and payoffs are obtained. The game is constructed and solved, by locating NE in pure and mixed strategies. Experimental evaluations conducted on real network datasets, using IDSs of different capabilities, simulate special cases and compromised nodes in a WSN, verify the model efficiency, and show how the game should be played.

Chapter 5 - Intrusion Prevention and Detection Systems

MP-MID: Multi-Protocol Oriented Middleware-level Intrusion Detection method for wireless sensor networks

Wireless Sensor Networks (WSNs) have been recognized for their utility in a variety of different fields including military sensing and tracking, environmental monitoring, patient monitoring and tracking smart environments. The more scientists try to develop further cost and energy efficient computing devices and algorithms for WSNs, the more challenging it becomes to fit the security of WSNs into such a constrained environment. Thus, familiarity with the security aspects of WSNs is essential before designing WSN systems. In order to provide effective integrity, confidentiality, and authentication during communication, the need for additional security measures in WSNs emerges. In this paper, the authors review the security requirements for WSNs, the different kinds of possible attacks, and security mechanisms used to overcome these attacks. The authors also present some statistical data for such attacks in WSN and some tables that indicate a comparison between different security mechanisms.