Attacks In Wireless Networks Research Articles

Feature Selection Model using Naive Bayes ML Algorithm for WSN Intrusion Detection System

Intrusion detection models using machine-learning algorithms are used for Intrusion prediction and prevention purposes. Wireless sensor network has a possibility of being attacked by various kinds of threats that will de-promote the performance of any network. These WSN are also affected by the sensor networks that send wrong information because of some environmental causes in- built disturbances misaligned management of the sensors in creating intrusion to the wireless sensor networks. Even though signified routing protocols cannot assure the required security in wireless sensor networks. The idea system provides a key solution for this kind of problem that arises in the network and predicts the abnormal behavior of the sensor nodes as well. But built model by the proposed system various approaches in detecting these kinds of intrusions in any wireless sensor networks in the past few years. The proposed system methodology gives a phenomenon control over the wireless sensor network in detecting the inclusions in its early stages itself. The Data set pre-processing is done by a method of applying the minimum number of features for intrusion detection systems using a machine learning algorithm. The main scope of this article is to improve the prediction of intrusion in a wireless sensor network using AI- based algorithms. This also includes the finest feature selection methodologies to increase the performance of the built model using the selected classifier, which is the Bayes category algorithm. Performance accuracy in the prediction of different attacks in wireless sensor networks is attained at nearly 95.8% for six selected attributes, a Precision level of 0.958, and the receiver operating characteristics or the area under the curve is equal to 0.989.

Analytical modeling of attacks in wireless sensor networks for attack detection

The article comprises issues of analytical modeling of attacks in wireless sensor networks. A proposed attack model specifies possible types of unauthorized influences at the network level aimed at compromising network nodes, data transmitted over the network, and information services provided by the wireless sensor network. A developed graph-based analytical model of a wireless sensor network aims at detecting network layer attacks by processing actual events received from network nodes and deciding whether the network is running under an attack. The correctness of the models is confirmed by their testing by using an example of a scenario for detecting a wormhole attack aimed at disrupting routing processes in a wireless sensor network.

МЕТОДИКА ПОСТРОЕНИЯ УСТОЙЧИВОЙ СИСТЕМЫ ЗАЩИТЫ НА ОСНОВЕ СОСТЯЗАТЕЛЬНОГО МАШИННОГО ОБУЧЕНИЯ В БЕСПРОВОДНЫХ СЕТЯХ 6G

Abstract The purpose of research is to develop the technique of analytical processing of big data of services and applications in the new generation communication networks to detect cybersecurity incidents and build sustainable protection systems based on adversarial machine learning. The methods of research: Analysis of modern methods of machine learning and neural network technologies, synthesis and formalization of algorithms for adversarial attacks on machine learning models. Scientific novelty: a technique for analytical processing of emulated data of services and applications for detecting cybersecurity incidents is presented, which provides a groundwork in the field of research into the security issues of complex intelligent services and applications in the infrastructure of wireless networks of the next generation. The result of research: The article proposes a technique of building a sustainable protection system against adversarial attacks in wireless ad hoc networks of the next generation. The main types of adversarial attacks, including poisoning attacks and evasion attacks, are formalized, and methods for generating adversarial examples on tabular, textual, and visual data are described. Several scenarios were generated and exploratory analysis of datasets was carried out using the DeepMIMO emulator. Potential application problems of binary classification and prediction of signal attenuation between a user and a base station for adversarial attacks are presented. The algorithmization of the processes of building and training a sustainable protection system against adversarial attacks in wireless networks of the next generation is presented on the example of emulated data

A New IDS for Detecting DDoS Attacks in Wireless Networks using Spotted Hyena Optimization and Fuzzy Temporal CNN

<p>Cyber-attacks are rapidly increasing in the internet era due to the growth of information technology. The distributed denial of service (DDoS) attacks are increasing dramatically due to the distributed services in cloud networks. In this paper, a new Intrusion Detection System (IDS) is proposed to improve the performance of the networks by detecting DDoS attacks effectively in wireless networks. In this work, we propose a new feature selection method called Split Filter Feature Selection and Spotted Hyena Optimization Based Feature Optimization Method (SFSH-FOM) to select the most contributed features that are helpful for enhancing the classification accuracy. In this work, a new deep learning algorithm named Fuzzy Temporal Features incorporated Convolutional Neural Network (FT-CNN) is proposed for performing effective classification. Here, a new cross layer feature fusion technique is also proposed by using FT-CNN and LSTM for enhancing the performance. The experiments have been carried out to evaluate the proposed IDS using the standard datasets, namely the KDD’99 dataset, the NSL-KDD dataset, and the DDoS dataset by considering the evaluation metrics such as detection accuracy, recall, precision, and F1-score, and it has also been proved as better than other IDSs in terms of accuracy and false alarm rate.</p> <p> </p>

Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

IEEE 802.11 Wi-Fi networks are prone to many denial of service (DoS) attacks due to vulnerabilities at the media access control (MAC) layer of the 802.11 protocol. Due to the data transmission nature of the wireless local area network (WLAN) through radio waves, its communication is exposed to the possibility of being attacked by illegitimate users. Moreover, the security design of the wireless structure is vulnerable to versatile attacks. For example, the attacker can imitate genuine features, rendering classification-based methods inaccurate in differentiating between real and false messages. Although many security standards have been proposed over the last decades to overcome many wireless network attacks, effectively detecting such attacks is crucial in today’s real-world applications. This paper presents a novel resource exhaustion attack detection scheme (READS) to detect resource exhaustion attacks effectively. The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack. The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in the WLAN. The proposed scheme consists of four modules which make it capable to alleviates the attack impact more effectively than the related work. The experimental results show the effectiveness of the proposed technique by gaining an 89.11% improvement compared to the existing works in terms of detection.

Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

This paper presents a machine-learning method for detecting jamming UAVs and classifying nodes during jamming attacks on Wireless Sensor Networks (WSNs). Jamming is a type of Denial of Service (DoS) attack and intentional interference where a malicious node transmits a high-power signal to increase noise on the receiver side to disrupt the communication channel and reduce performance significantly. To defend and prevent such attacks, the first step is to detect them. The current detection approaches use centralized techniques to detect jamming, where each node collects information and forwards it to the base station. As a result, overhead and communication costs increased. In this work, we present a jamming attack and classify nodes into different categories based on their location to the jammer by employing a single node observer. As a result, we introduced a machine learning model that uses distance ratios and power received as features to detect such attacks. Furthermore, we considered several types of jammers transmitting at different power levels to evaluate the proposed metrics using MATLAB. With a detection accuracy of 99.7% for the k-nearest neighbors (KNN) algorithm and average testing accuracy of 99.9%, the presented solution is capable of efficiently and accurately detecting jamming attacks in wireless sensor networks.

Comprehensive review on distributed denial of service attacks in wireless sensor networks

Distributed denial of service (DDoS) is the most popular disruptive type of attack in wireless sensor networks (WSNs). It is extremely harmful for the functioning of the network, since it generates a huge amount of traffic through flooding of the spam packets into the target system. The malicious nodes present in the network prevent the legitimate users from the access to the network through flooding. Hence, it is necessary to handle this issue. Intrusion detection systems (IDSs) are used to monitor all incoming packets and they can compare the traffic patterns to detect the anomalous network activity. In this paper, a survey of works on DDoS attacks launched by the malicious users and the detection of such attacks using IDS are discussed. Moreover, this survey focuses on the IDSs developed using classification techniques since they have been used for detecting and preventing the DDoS attacks more efficiently.

Multiplayer reputation-based coalition game model (MRCGM) with dominant strategy analysis for detecting malicious attacks in wireless sensor networks

Wireless Sensor Networks (WSNs) are turning into an essential portion of our lives. Devoid of assuring WSNs security (WS), there remain no broad implementations of WSNs. Because of sensor nodes’ constrained capacities concerning computation, communication, and energy, giving protection to WSNs remains competitive. Indeed, the procedure of applying WS remains adaptable and vibrant that develops consistently. Attack-defend’s crux in WS could be conveyed by collaborative schemes of interdependency when Game Theory (GT) could be employed to consider communications amidst schemes of logical decision-makers. Hence, learning WS alongside GT possesses greater logic. This study proffers a Multiplayer Reputation-based Coalition Game Model (MRCGM) for seeking the false data injection amidst the nodes. An attacker attempts to sustain an ideal level of belief by correlating the identifications within the network (NW) to initiate a victorious transfer. Concurrently, the attacker should acquire a few charges for sustaining the trustability of its identifications. This proffered procedure employs this notion and turns the attack expensive by fixing a global threshold for nodes or identifications to be trustable and functional within the NW. Additionally, the beneficial function of the attacker and defender is as well described. The MRCGM has been correlated with 2 advanced methodologies like security-aware routing scheme employing repeated game (SARSRGM) paradigm and a Game-based Fuzzy Q-learning (GBFQL) scheme concerning diverse criteria. Consequently, the proffered MRCGM attains 124 kbps of Packet Drop Rate, 76.14% of energy efficiency, 96.1% of detection accuracy, 25.45% of energy consumption, and 102.45 ms of routing latency.

Evaluation of Traffic Models under Multiple Black hole Attack in Wireless Mesh Network

Wireless networks are the networks in which data communication is done through the wireless channels and wireless nodes. In such type of the networks, cooperation of the all nodes is required. Due to which, chances of security attacks is possible and network performance and security is badly affected. Black hole attack is a security attack comes under the active, internal as well as external attacks. In black hole attack, malicious node presents a suitable, shortest and low cost path. If the above path is selected for data transmission, malicious node drops the received packets and as a result performance of the network is degraded. In this paper, impact of Random Direction (RD) and Reference Point Group Mobility (RPGM) models have been analyzed for wireless mesh network in the presence of multiple black hole attacks. By varying the number of network connections and node density, (Ad hoc on demand distance vector) AODV routing protocol is evaluated. Normalized routing load, average end to end delay, average throughput, and total number of packets received are considered as performance metrics. Bonn-motion tool has been used to create the scenario of mobility models. In the presence of black hole attack, and under RPGM mobility model, AODV routing protocol presents better results in respect of average throughput, normalized routing load. Simulation work has been carried out on network simulator version NS-2.35.

A multi-path secure routing for the detection of node capturing attack in wireless sensor network

Over the past few years, the devices in Wireless Sensor Networks (WSN) are growing exponentially due to the emergence of many sophisticated applications. This tremendous growth leads to serious security challenges, and the devices of WSN should be protected from various attacks. WSN can be configured dynamically without fixed infrastructure and the devices can be talked with one another in an ad-hoc manner. Due to the dynamic nature of WSN, routing is considered as the challenging task that should be performed efficiently with robust routing mechanism. Even though many routing schemes have been emerged for WSN, they are not well scalable in very large-scale environment. This work introduces multi path routing strategy, and the routing will be selected based on trusted nodes. First, the trusted nodes are identified using trusted metrics of each node in the network. These metrics are calculated based on the threshold value of nodes. Then, secure routing is established by isolating node capturing attacks from the path. The performance of the work is analyzed in terms of packet loss, computational time and throughput. The paper compares the performance with the state-of-the-art routing schemes such as EMBTR (Enhanced Multi Attribute Based Attack Resistance), TSRM (Trust based secure routing model), and TARF (Trust-aware routing framework for WSNs). The outcome of the simulation shows that the proposed scheme outperforms the other state-of-the-work in terms of computational cost, throughput, and delay.

Adversarial security mitigations of mmWave beamforming prediction models using defensive distillation and adversarial retraining

The design of a security scheme for beamforming prediction is critical for next-generation wireless networks (5G, 6G, and beyond). However, there is no consensus about protecting beamforming prediction using deep learning algorithms in these networks. This paper presents the security vulnerabilities in deep learning for beamforming prediction using deep neural networks in 6G wireless networks, which treats the beamforming prediction as a multi-output regression problem. It is indicated that the initial DNN model is vulnerable to adversarial attacks, such as Fast Gradient Sign Method , Basic Iterative Method , Projected Gradient Descent , and Momentum Iterative Method , because the initial DNN model is sensitive to the perturbations of the adversarial samples of the training data. This study offers two mitigation methods, such as adversarial training and defensive distillation, for adversarial attacks against artificial intelligence-based models used in the millimeter-wave (mmWave) beamforming prediction. Furthermore, the proposed scheme can be used in situations where the data are corrupted due to the adversarial examples in the training data. Experimental results show that the proposed methods defend the DNN models against adversarial attacks in next-generation wireless networks.

Two-Dimensional Projection Based Wireless Intrusion Classification Using Lightweight EfficientNet

Internet of Things (IoT) networks leverage wireless communication protocol, which adversaries can exploit. Impersonation attacks, injection attacks, and flooding are several examples of different attacks existing in Wi-Fi networks. Intrusion Detection System (IDS) became one solution to distinguish those attacks from benign traffic. Deep learning techniques have been intensively utilized to classify the attacks. However, the main issue of utilizing deep learning models is projecting the data, notably tabular data, into image-based data. This study proposes a novel projection from wireless network attacks data into grid-like data for feeding one of the Convolutional Neural Network (CNN) models, EfficientNet. We define the particular sequence of placing the attribute values in a matrix that would be captured as an image. By combining the most important subset of attributes and EfficientNet, we aim for an accurate and lightweight IDS module deployed in IoT networks. We examine the proposed model using the Wi-Fi attacks dataset, called AWID dataset. We achieve the best performance by a 99.91% F1 score and 0.11% false positive rate. In addition, our proposed model achieved comparable results with other statistical machine learning models, which shows that our proposed model successfully exploited the spatial information of tabular data to maintain detection accuracy. We also successfully maintain the false positive rate of about 0.11%. We also compared the proposed model with other machine learning models, and it is shown that our proposed model achieved comparable results with the other three models. We believe the spatial information must be considered by projecting the tabular data into grid-like data.

A systematic review of routing attacks detection in wireless sensor networks.

Wireless sensor networks (WSNs) consist of hundreds, or thousands of sensor nodes distributed over a wide area and used as the Internet of Things (IoT) devices to benefit many home users and autonomous systems industries. With many users adopting WSN-based IoT technology, ensuring that the sensor's information is protected from attacks is essential. Many attacks interrupt WSNs, such as Quality of Service (QoS) attacks, malicious nodes, and routing attacks. To combat these attacks, especially on the routing attacks, we need to detect the attacker nodes and prevent them from any access to WSN. Although some survey studies on routing attacks have been published, a lack of systematic studies on detecting WSN routing attacks can be seen in the literature. This study enhances the topic with a taxonomy of current and emerging detection techniques for routing attacks in wireless sensor networks to improve QoS. This article uses a PRISMA flow diagram for a systematic review of 87 articles from 2016 to 2022 based on eight routing attacks: wormhole, sybil, Grayhole/selective forwarding, blackhole, sinkhole, replay, spoofing, and hello flood attacks. The review also includes an evaluation of the metrics and criteria used to evaluate performance. Researchers can use this article to fill in any information gaps within the WSN routing attack detection domain.

Using a lightweight security mechanism to detect and localize jamming attack in wireless sensor networks

Recent years have seen a rise in the popularity of the Internet of Things (IoT) and its applications, due to its adaptability, usability, varied application potential, and ease of deployment. On the other hand, security threats get more attention. Where wireless sensor networks (WSN), which is the core of IoT, are vulnerable to the jammer's attack due to the fact that they are shared and have open access to a wireless channel. Moreover, the difficult conditions in which nodes are distributed, such as underwater, fires, volcanoes, animal habitats, or battlefields, node localization is a significant challenge for the majority of WSN applications. Additionally, the lack of accuracy in traditional localization algorithms has prompted researchers to propose optimization techniques capable of improving localization accuracies, such as genetic algorithms, gravitational search algorithms, neural networks, and deep learning; however, these techniques require additional hardware to perform their complex calculations. On the other side, uncomplicated algorithms like Virtual Force Iteration Localization (VFIL), Weighted Centroid Localization (WCL), and Centroid Localization (CL) are less accurate. To bridge that gap, this work proposes a lightweight algorithm for accurately locating the jammer's coordinates. Our algorithm consists of three phases firstly, a simple way to detect a jamming attack. Second novel designed technique to draw the jammed region using the mapping protocol. Moreover, a range-free technique is proposed to locate the jammer based on geometric calculations. Finally, A set of simulation experiments was undertaken to assess the suggested algorithm's performance. Experiments reveal that the suggested method is capable of precisely locating the jammer. Additionally, the presented method demonstrates superior efficiency in comparison to existing methods.

Detection of unique delete attack in wireless sensor network using gradient thresholding‐long short‐term memory algorithm

SUMMARYWireless sensor network (WSN) is widely used in many fields, including agriculture, military, healthcare, monitoring, and surveillance. WSN application increases day by day due to growth in embedded and internet technologies. However, data security in WSN is challenging, and different types of attacks are increasing. Traditional intrusion detection methods analyze and identify attacks such as Denial of Service, anomaly detection, wormhole, sybil, and blackhole. Traditional methods never addressed the application layer's novel attack, such as the delete attack. Unique DELETE attack (DA) forms in the application layer and targets web servers and applications. Since unique DA use uniform resource locator requests, distinguishing them from legitimate traffic becomes difficult. The unique DA classify as unique (or) single, multiple, and repeated unique DELETE attacks; for detecting the unique DA types, gradient thresholding‐long short‐term memory (GT‐LSTM) algorithm is proposed using packet per second and traffic rate data in WSN nodes. GT‐LSTM algorithm detects unique DA through different thresholding at the training option layer in LSTM. Different gradient thresholding values in LSTM reduce the exploding gradients which fail to detect the unique DA nodes. Gradient thresholding in LSTM layers lies 1 to 3 for detecting unique DELETE attack nodes. The proposed method reduces detection time, improves accuracy, and identifies the hidden node that performs a unique DA in WSN. The unique DELETE Attack identification and performance through the proposed GT‐LSTM is analyzed in the NS2 simulation environment and the test bed of WSN. Forms the experimental and simulation results, GT‐LSTM performs better than fuzzy, KNN, and linear regression‐based intruder detection systems. The proposed algorithm achieves 99% accuracy in detecting the DELETE attack nodes in WSN.

RPPTD: Robust Privacy-Preserving Truth Discovery Scheme

Benefitingfrom the rapid development of communication technology and Internet of Things (IoT) devices, crowdsensing is on the rise. Sensor data from IoT devices can be requested for data analysis and utilization, however, the collected data of an object from multiple devices are usually different. Therefore, how to extract the most reliable data from numerous data has become an important topic, and truth discovery receives great attention. These collected data often contain personal sensitive information, if users’ privacy cannot be protected, many users are unwilling to contribute their data, and the usability of the published data will be greatly reduced. In this article, a robust privacy-preserving truth discovery scheme is proposed to simultaneously achieve the reliability and privacy of data. Specifically, the data are collected and encrypted before it is sent from the user. Compared with the existing works, there are two additional benefits, trusted third party and noncolluding platforms are not necessary anymore, hence the robustness is improved and single-point failure bottlenecks are eliminated. Besides, the proposed RPPTD is secure against many known attacks in open wireless networks, and the human-factor-aware differential aggregation attack. Finally, the performance evaluation indicates that our scheme is efficient and suitable for the practical environment.

Distributed filtering for nonlinear systems under false data injection attack

This paper investigates a distributed secure estimation problem for a nonlinear stochastic system subject to false data injection attack. The target plant is disturbed by unknown input with non-prior information distribution. Considering the limited communication resources and potential malicious attack in wireless sensor network, a novel protector is designed for each sensor to resist the hostile attack, in which a dynamic decision rule is developed to further reduce the data receiving frequency, thus saving computing resources. Sufficient conditions are established to ensure that the estimation error is exponentially bounded in mean square. Furthermore, the critical attack probability when the steady-state estimation error exceeds the preset value is discussed in two cases. Finally, effectiveness of the proposed technique is demonstrated by a numerical example.

Mitigating Traffic Remapping Attacks in Autonomous Multihop Wireless Networks

Multihop wireless networks with autonomous nodes are susceptible to selfish traffic remapping attacks (TRAs). Nodes launching TRAs leverage the underlying channel access function to receive an unduly high Quality of Service (QoS) for packet flows traversing source-to-destination routes. TRAs are easy to execute, impossible to prevent, difficult to detect, and harmful to the QoS of honest nodes. Recognizing the need for providing QoS security, we use a novel network-oriented QoS metric to propose a self-enforcing game-theoretic mitigation approach. By switching between TRA and honest behavior, selfish nodes engage in a noncooperative multistage game in pursuit of high QoS. We analyze feasible node strategies and design a distributed signaling mechanism called DISTRESS, under which, given certain conditions, the game produces a desirable outcome: after an upper bounded play time, honesty tends to become a selfish node’s best reply behavior, while yielding acceptable QoS to most or all nodes. We verify these findings by Monte Carlo and ns-3 simulations of static and mobile nodes.

AI-Based Wormhole Attack Detection Techniques in Wireless Sensor Networks

The popularity of wireless sensor networks for establishing different communication systems is increasing daily. A wireless network consists of sensors prone to various security threats. These sensor nodes make a wireless network vulnerable to denial-of-service attacks. One of them is a wormhole attack that uses a low latency link between two malicious sensor nodes and affects the routing paths of the entire network. This attack is brutal as it is resistant to many cryptographic schemes and hard to observe within the network. This paper provides a comprehensive review of the literature on the subject of the detection and mitigation of wormhole attacks in wireless sensor networks. The existing surveys are also explored to find gaps in the literature. Several existing schemes based on different methods are also evaluated critically in terms of throughput, detection rate, low energy consumption, packet delivery ratio, and end-to-end delay. As artificial intelligence and machine learning have massive potential for the efficient management of sensor networks, this paper provides AI- and ML-based schemes as optimal solutions for the identified state-of-the-art problems in wormhole attack detection. As per the author’s knowledge, this is the first in-depth review of AI- and ML-based techniques in wireless sensor networks for wormhole attack detection. Finally, our paper explored the open research challenges for detecting and mitigating wormhole attacks in wireless networks.

Bipolar fuzzy information‐based PROMETHEE‐based outranking scheme for mitigating vampire attack in wireless sensor networks

AbstractThe sensor nodes comprising sensing devices, small processing units, and battery, cooperate in wireless sensor networks (WSNs) for gathering potential factors that aids in achieving significant decision‐making processes in deployed applications. WSNs are vulnerable to security threats due to their open nature, availability characteristics, and lack of energy, resulting in a diversified number of security attacks. Among the security attacks, vampire attack is the serious threat as they drain the energy of nodes, which in turn crumbles the network lifetime and energy stability in WSNs. In this article, Bi‐Polar Information‐Based Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE)‐based outranking scheme is proposed for detecting and isolating vampire attacks to improve throughput with minimized energy consumptions and delay for prolonging lifetime. This proposed BPFI‐POR scheme used bipolar information that facilitates an asymmetrical association between two decision making factors that influences the discrimination and selection of genuine nodes from vampire nodes during the routing process. It aids in characterizing precise and vague information through bipolar fuzzy linguistic terms that has the maximized probability of being represented as trapezoidal bipolar fuzzy numbers. It estimated the cooperation of intermediate sensor nodes' based on trapezoidal bipolar fuzzy numbers for determining their preference or selection using criteria that could be influenced during the routing process. It also used the ranking function for accessing the alternatives through crisp real preferences with entropy weighting information responsible for attributes weight computation. The simulation results of the BPFI‐POR scheme confirmed better throughput, detection time with minimized energy consumptions and latency on par with the competitive algorithms.