Attacks In Wireless Networks Research Articles

A robust detection scheme against selective forwarding attacks in wireless sensor networks under variable harsh environments

Event-driven wireless sensor networks (EWSNs) often operate in harsh environments and are susceptible to selective forwarding attacks, where malicious nodes selectively drop packets. These attacks are difficult to detect due to similarities with normal node behavior under adverse conditions. To address this, we propose a detection scheme combining Variable Autoencoder (VAE) and Gaussian Mixture Model (GMM) with an Autoencoder (AE). The scheme extracts features from the node's single round forwarding rate (SFR), with VAE generating latent variables and GMM clustering them to differentiate behaviors. The AE model, trained on data representing normal behavior, uses reconstruction error to identify malicious nodes. Simulation results show that our scheme improves network throughput and achieves low False Detection Rate (FDR) and Missed Detection Rate (MDR), with both rates as low as 0.5% in normal conditions and below 2% in mobile harsh environments. With a computational complexity of O(n), this method is suitable for resource-constrained environments, demonstrating superior robustness compared to existing approaches.

Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance: A Cooperative Sensing-Based Adaptive Model Predictive Control Approach

Obstacle detection and platoon control for mixed traffic flows, comprising human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs), face challenges from uncertain disturbances, such as sensor faults, inaccurate driver operations, and mismatched model errors. Furthermore, misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’ perception and platoon safety. In this paper, we develop a two-dimensional robust control method for a mixed platoon, including a single leading CAV and multiple following HDVs that incorporate robust information sensing and platoon control. To effectively detect and locate unknown obstacles ahead of the leading CAV, we propose a cooperative vehicle–infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV. This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner. Additionally, we propose a distributed car-following control scheme with robustness to guarantee the following HDVs, considering uncertain disturbances. We also provide theoretical proof of the string stability under this control framework. Finally, extensive simulations are conducted to validate our approach. The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers, significantly reduce the mean-square deviation in obstacle sensing, and approach the theoretical error lower bound. Moreover, the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

A deep experimental analysis of energy‐proficient firewall policies and security practices for resource limited wireless networks

AbstractThe role of firewalls and security principles in resource‐limited wireless networks and Wireless Sensor Networks (WSN) is more expected than in any dedicated network environment. The advanced wireless technologies and emerging fashion of autonomous wireless network nodes are mostly expecting resilient firewall services with multi‐level security policies. Wireless networks are classified under sensor networks, Internet of Things (IoT) mobile networks, and so forth. According to the expectations, security frameworks are gradually invented around the communication platform using various ideologies. The novel ideology and respective implementation effort open better solutions against wireless network attacks. Anyhow, the minimal production of time complexity, energy complexity, and computation overhead from any novel security approach is always considered under the best practices. The complexity levels directly affect the wireless node's energy consumption ability and operational spans severely. The energy optimization techniques and load‐balancing techniques integrated into multi‐class firewall rules are extremely useful solutions for resource‐limited wireless networks. This article has been motivated to analyze the recent firewall techniques and secure data communication mechanisms used for securing wireless networks. Consequently, the practice of comparative literature analysis helps to improve the current limitations identified under the classified categories of security mechanisms such as energy‐optimized security principles and load‐balanced security principles. The establishment of secure and energy‐optimized multi‐class firewall rules in each wireless node assures a healthy focus on next‐generation networks. The experiment section shows the benefits of energy‐optimized secure network communication in terms of better accuracy and the benefits of load‐balanced secure network communication through minimal overhead in computing platforms.

Lightweight log-monitoring-based mitigation tool against WLAN attacks

<p>Wireless network attacks are some of the most common network security threats dealt with daily. Their ease of execution and effectiveness make them commonplace within most public networks. The goal of this paper is to develop a tool which provides defenses against these attacks, one which can also generate the attacks to test its own effectiveness in defending against them. The research involved the design, testing, and implementation of attacks/defenses tool, which benefits from a user-friendly user interface that simplifies the testing process. The attacks were generated using existing tools, linked to one central interface. The defense methodology was script-based and created entirely from scratch. It was also linked to a single interface which continuously monitors logs to detect and prevent attacks in an efficient timely manner. The results showed that the proposed defenses to the studied wireless attacks were effective at mitigation, or outright prevention. They were also more lightweight than existing solutions, making them more appealing for less powerful hardware.</p>

Analysis of Machine Learning Methods for Intrusion Detection Systems in Wireless Networks

Wireless networks have become integral to modern communication systems, making them vulnerable to various security threats. Intrusion detection systems (IDSs) are essential for detecting and mitigating these threats and can also powerfully screen network traffic for pernicious activities that are planned to abuse the classification, honesty, realness, and accessibility of the network. Machine learning (ML) and Deep learning (DL) techniques are effective in identifying and classifying network attacks. This study proposes a novel intrusion detection system that employs ML and DL models to classify and distinguish network attacks in wireless networks. The proposed system enhanced detection accuracy and efficiency in IDS, the scalability of IDS systems, and estimated the performance of IDS in wireless networks. It also investigates IDS techniques using machine learning, designs and implements IDS in wireless networks using machine learning, and trains several IDS models regarding wireless networks that are fitted. It contrasts the exhibition of proposed models and existing procedures. The suggested system can therefore be utilized as an effective IDS for wireless networks, providing real-time detection and classification of network attacks.

WIRELESS LAN DESIGN AND FUTURE INTERNET ARCHITECTURE

In this research thesis, an advance work is being done on the Wireless Local Area Network (WLAN) design and future internet architecture for intrusion detection. There has been a large shift worldwide in using technology in almost every aspect of our lives. This trend has been termed in many industries as the "Internet of Things" (IoT), where the Internet and cloud- based activities are seeing more and more use for control, monitoring and data storage applications. As stated earlier, these assumptions were made because of the extreme difficulty in obtaining any related telemetry from an actual WLAN system or business-critical network. To improve the validity of the simulation, it would be beneficial to obtain even sanitized telemetry from an actual system. Then, the simulation could be refined to better reflect the dynamics of the signals in a real system. If actual WLAN system data is unobtainable, then the test by itself can be modified to reflect more processes seen in these systems. First, a small flow loop with sensing and control elements could be constructed and driven by the servers of the test bed. The primary server in the test bed that acts as the WLAN server could then be programmed to poll the sensors, issue commands and collect data. Time synchronous averaging techniques are used to resample telemetry sources that have different sampling rates. The research provides several case studies to prove the efficacy of the newly developed variable grouping technique for WLAN. Recall that variable grouping methods are needed to extract sets or subsets of variables that give the lowest model prediction errors. The research also presents AAKR and AAMSET model results for several successfully detected intrusion activities from two different data sets. The models utilized four different clusters of telemetry taken from the Linux kernel to determine which class of telemetry was more effective for intrusion detection. The knowledge-based system is certainly the most numerous and widely used type that uses signatures or features of known wireless network attacks for detection purposes. While this type is easy to implement and has a low rate of false/missed alarms, it will always miss the so-called zero-day wireless network attacks. Recall that a zero-day wireless network attack is a new, unknown intrusion. Because the knowledge-based system has never seen a zero-day intrusion event, it will classify this new behavior as normal. In contrast, the behavior-based system can detect known and zero-day wireless network attacks with an accuracy of 97.86% correctly. This type uses data-driven modeling techniques to learn normal system behavior. Once trained, any future monitored telemetry that deviates from this learned behavior will be labeled as an intrusion event.

Dynamic properties of the multimalware attacks in wireless sensor networks: Fractional derivative analysis of wireless sensor networks

Abstract Due to inherent operating constraints, wireless sensor networks (WSNs) need help assuring network security. This problem is caused by worms entering the networks, which can spread uncontrollably to nearby nodes from a single node infected with computer viruses, worms, trojans, and other malicious software, which can compromise the network’s integrity and functionality. This article discusses a fractional S E 1 E 2 I R {\mathsf{S}}{{\mathsf{E}}}_{1}{{\mathsf{E}}}_{2}{\mathsf{I}}{\mathsf{R}} model to explain worm propagation in WSNs. For capturing the dynamics of the virus, we use the Mittag–Leffler kernel and the Atangana–Baleanu (AB) Caputo operator. Besides other characteristics of the problem, the properties of superposition and Lipschitzness of the AB Caputo derivatives are studied. Standard numerical methods were employed to approximate the Atangana–Baleanu–Caputto fractional derivative, and a detailed analysis is presented. To illustrate our analytical conclusions, we ran numerical simulations.

Energy-Efficient Framework to Mitigate Denial of Sleep Attacks in Wireless Body Area Networks

Energy-Efficient Framework to Mitigate Denial of Sleep Attacks in Wireless Body Area Networks

Smart homes under siege: Assessing the robustness of physical security against wireless network attacks

Nowadays domestic smart security devices, such as smart locks, smart doorbells, and security cameras, are becoming increasingly popular with users, due to their ease of use, convenience, and declining prices. Unlike conventional non-smart security devices, such as alarms and locks, performance standards for smart security devices, such as the British TS 621, are not easily understandable by end users due to the technical language employed. Users also have very few sources of unbiased information regarding product performance in real world conditions and protection against attacks from cyber attacker-burglars and, as a result, tend to take manufacturer claims at face value. This means that, as this work proves, users may be exposed to threats, such as theft, impersonation (should an attacker steal their credentials), and even physical injury, if the device fails and is used to prevent access to hazardous environments. As such, this paper deploys several attacks using popular wireless attack vectors (i.e., 433 MHz radio, Bluetooth, and RFID) against domestic smart security devices to assess the protection offered against a cyber attacker-burglar. Our results suggest that users are open to considerable cyber physical attacks, irrespective if they use lesser known (i.e., no name) or branded smart security devices, due to the poor security offered by these devices.

Analyze textual data: deep neural network for adversarial inversion attack in wireless networks

Deep neural networks (DNN) are highly effective in a number of tasks related to machine learning across different domains. It is quite challenging to apply the information gained to textual data because of its graph representation structure. This article applies innovative graph structures and protection techniques to secure wireless systems and mobile computing applications. We develop an Intrusion Detection System (IDS) with DNN and Support Vector Machine (SVM) to identify adversarial inversion attacks in the network system. It employs both normal and abnormal adversaries. It constantly generates signatures, creates attack signatures, and refreshes the IDS signature repository. In conclusion, the assessment indicators, including latency rates and throughput, are used to evaluate the effectiveness and efficiency of the recommended framework with Random Forest. The results of the proposed model (SVM with DNN) based on adversarial inversion attacks were better and more efficient than traditional models, with a detection rate of 93.67% and 95.34% concerning latency rate and throughput. This article also compares the proposed model (SVM with DNN) accuracy with other classifiers and the accuracy comparison for feature datasets of 90.3% and 90%, respectively.

Mitigation of network security attacks in wireless multimedia sensors networks using intrusion detection system

<p>In this work, research propose a deep transfer learning-based Lecun network P-Lenet technique to move the knowledge contained in source domain data to the target domain and integrate to produce an efficient intrusion detection system (IDS) that would enhance the detection accuracy for any wireless multimedia sensors networks (WMSN) ecosystems. To prevent attacks on the software defined network (SDN) platform in real time, the proposed method placed a strong emphasis on anomaly detection as the primary mechanism. The Lecun network (LeNet) was investigated in this work, and a new variant of the network called the Lenet was proposed. In addition, research make use of techniques like as feature normalization to increase the accuracy of the algorithm predictions and to optimize the training process in such a way that it consumes the least amount of time and resources. The performance of the proposed model that was recommended was superior to that of existing network intrusion detection system (NIDS) algorithms.</p>

A Naive Bayes-Driven Mechanism for Mitigating Packet-Dropping Attacks in Autonomous Wireless Networks

A Naive Bayes-Driven Mechanism for Mitigating Packet-Dropping Attacks in Autonomous Wireless Networks

Performance Evaluation of Random Forest Algorithm for DDoS Attacks Detection in Wireless Sensor Network Using Numericalization and Normalization as Feature Engineering Techniques

Distributed Denial-Of-Service (DDoS) attacks in Wireless Sensor Networks (WSNs) aims to decrease the availability of a service by exhausting the network resources available for traffic, thus preventing legitimate users from accessing the services. This paper evaluates the performance of Random Forest algorithm for distributed denial of service attacks detection in Wireless Sensor Networks (WSNs) with numericalization and normalization as feature engineering techniques and exploratory data analysis to build the Distributed Denial of Service (DDoS) attacks detection model. The dataset was streamlined for optimum performance of the selected algorithms. The experimental result shows the Random Forest achieved an accuracy of 90%, F1 score of 90%, precision of 91% and recall of 90%. The outcomes revealed that the random forest model performed better than the competing models in terms of accuracy, F1 score, precision, and recall. The results of this study shed light on the efficiency of machine learning models in identifying DDoS attacks and suggest areas for future study. The study stresses the significance of data preparation and cleaning in the process and offers a framework for the application of machine learning models in network intrusion detection systems. Keywords: Wireless Sensor Networks, Distributed Denial of Service, Intrusion Detection System, Random Forest, Machine Learning. Proceedings Citation Format Sulaiman, H, Oyefolahan, I.O., Bashir, S.A., Kolo, I.M. & Ndunagu J.N. (2023): Performance Evaluation of Random Forest Algorithm for DDoS Attacks Detection in Wireless Sensor Network Using Numericalization and Normalization as Feature Engineering Techniques. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 19-26. https://cybersecurenigeria.org/conference-proceedings/volume-2-2023/ dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P2.

An Anonymous Authentication Scheme Based on Chinese Residue Theorem in Wireless Body Area Networks

Considering the resource limitations of low-cost wireless sensors, there is a growing inclination to utilize cryptographic primitives that are optimized for efficiency, such as symmetric key encryption/decryption and hash functions, when designing authentication schemes. However, designing a lightweight authentication scheme that can meet various security requirements poses a significant challenge. In recent years, numerous lightweight authentication schemes have been proposed in order to address these security needs. Nevertheless, recent research has revealed that many of these schemes exhibit security vulnerabilities and design deficiencies, including challenges related to asynchronization and impractical gateway-node search operations. Due to the inadequate security of existing schemes, this study introduces a novel privacy-preserving authentication scheme that aims to provide adaptive resilience against desynchronization attacks in wireless body area networks (WBANs). The proposed scheme utilizes lightweight cryptographic modules to optimize efficiency. To ensure user anonymity, the Chinese Remainder Theorem technique is employed, whereas forward secrecy and resistance to desynchronization attacks are achieved through the use of one-way hash chains and serial numbers, respectively. Through extensive analysis and comparisons, the proposed scheme is demonstrated to strike a fine balance between security and efficiency.

Survey on data aggregation based security attacks in wireless sensor network

<p>Wireless sensor network (WSN) has applications in military, health care, environmental monitoring, infrastructure, industrial and commercial applications. The WSN is expected to maintain data integrity in all its network operations. However, due to the nature of wireless connectivity, WSN is prone to various attacks that alter or steal the data exchanged between the nodes. These attacks can disrupt the network processes and also the accuracy of its results. In this survey paper, we have reviewed various attacks available in the literature till date. We have also listed existing methods that focus on data aggregation based security mechanisms in WSN to counter the attacks. We have classified and compared these methods owing to their encryption techniques. This paper intends to support researchers to understand the basic attacks prevalent in WSN and schemes to counter such attacks.</p>

An epidemic model for the investigation of multi‐malware attack in wireless sensor network

AbstractThe protection of wireless sensor networks (WSN) against malware attacks is crucial. The paper discusses the issue of malware attacks in WSN, which are commonly used for monitoring and surveillance in various applications. Due to resource constraints, sensor nodes in WSN are vulnerable to malware attacks, which can spread rapidly and paralyze the network. The development of new technologies such as IoT, Industry 4.0 has increased the importance of WSN, and it has become essential to address the challenges posed by the resource‐constrained nature of sensor nodes and security concerns. In this paper, a model is considered with two exposed states to investigate the behaviour of malware spreading in WSN, and a SE1E2IR (Susceptible—Exposed State 1 ‐ Exposed State 2 ‐ Infectious—Recovered) model is proposed. The model is formulated as a system of differential equations, and its equilibrium and stability are examined. The basic reproduction number (R0) is also calculated as a key parameter that characterizes the spread of malware in the network. This parameter helps to identify the conditions under which the network will remain malware‐free or when it will experience an outbreak of malware. The proposed model provides a mechanism for the earlier detection of malware occurrences in WSN, and also discusses the effect of connectivity and coverages on the propagation of malware in the network. The paper also includes a comparative study of the proposed model with existing models; extensive theoretical study and computation analysis are performed to validate the proposed model.

Hybrid intelligent intrusion detection system for multiple Wi‐Fi attacks in wireless networks using stacked restricted Boltzmann machine and deep belief networks

SummaryThe recent cutting edge mobile and internet advancements have increased Internet of Things based gadgets more pervasive and cohesive into our day to day lives. There emerges a need to beat the security challenges, prevalently came about because of the uncovered idea of a remote medium, for example a Wi‐Fi network setup. The linked gadgets are ubiquitous, creating high‐dimensional information on an expansive scale. It tends to be seen those illegitimate actions like illegal information access, information stealing, information alteration different other interruption exercises are quickly developing during a decade ago. Wireless network data transfers will be anticipated to surge strongly in the coming years. Feature learning, still, can evade the probable complications, possibly formed by means of large wireless network dimensions. Therefore, the proposed system focuses on developing a Hybrid Intelligent Intrusion Detection System (IDS) for detecting multiple assaults such as Impersonation, Injection, flooding along with other unknown malicious Wi‐Fi attacks using Deep Learning and Machine Learning algorithms. The proposed system is investigated on well referenced Aegean Wi‐Fi Intrusion Dataset (AWID) and the experimental results prove that the utility of deep learning algorithms in feature representation and classification has achieved higher detection accuracy along with lower false alarm rate. To get an optimum feature set and perform dimensionality reduction on Wi‐Fi network data, the proposed framework uses Deep Belief Network (DBN) with stacked layers of Restricted Boltzmann Machine (RBM). In order to identify and categories various Wi‐Fi threats, Deep Neural Networks, and algorithms like SVM, MLKNN, and RAkEL are employed. The anticipated DBN model was given an optimization strategy to enhance classification performance. The optimization strategy involved selecting the best possible feature subset using a feature selection technique, optimizing the DBN model using the best possible hyperparameter values, less reconstruction error values, and utilizing the regularization method to account for weight decay. The experimental findings demonstrate that the use of deep learning algorithms in feature representation and classification has produced improved detection accuracy coupled with a decreased false alarm rate when applied to the proposed hybrid scheme based Intelligent IDS using the well‐referenced AWID dataset.

Defense techniques against spoofing attacks in wireless sensor networks

Defense techniques against spoofing attacks in wireless sensor networks

Detection of Management-Frames-Based Denial-of-Service Attack in Wireless LAN Network Using Artificial Neural Network

Wireless Local Area Networks (WLANs) have become an increasingly popular mode of communication and networking, with a wide range of applications in various fields. However, the increasing popularity of WLANs has also led to an increase in security threats, including denial of service (DoS) attacks. In this study, management-frames-based DoS attacks, in which the attacker floods the network with management frames, are particularly concerning as they can cause widespread disruptions in the network. Attacks known as denial of service (DoS) can target wireless LANs. None of the wireless security mechanisms in use today contemplate defence against them. At the MAC layer, there are multiple vulnerabilities that can be exploited to launch DoS attacks. This paper focuses on designing and developing an artificial neural network (NN) scheme for detecting management-frames-based DoS attacks. The proposed scheme aims to effectively detect fake de-authentication/disassociation frames and improve network performance by avoiding communication interruption caused by such attacks. The proposed NN scheme leverages machine learning techniques to analyse patterns and features in the management frames exchanged between wireless devices. By training the NN, the system can learn to accurately detect potential DoS attacks. This approach offers a more sophisticated and effective solution to the problem of DoS attacks in wireless LANs and has the potential to significantly enhance the security and reliability of these networks. According to the experimental results, the proposed technique exhibits higher effectiveness in detection compared to existing methods, as evidenced by a significantly increased true positive rate and a decreased false positive rate.

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.