Wireless Network Security Research Articles

Adaptive Strategies in Enhancing Physical Layer Security: A Comprehensive Survey

Adaptive schemes in physical layer security, designed to dynamically respond to the evolving conditions of wireless channels, play a crucial role in fortifying the security of wireless communication systems. We offer a thorough analysis of the current state of research on adaptive schemes in physical layer security, introducing a novel taxonomy to categorize and understand these schemes more effectively. A detailed comparison is drawn between the insights provided in this survey and those from the literature, highlighting the unique contributions of our work. We delve into the contributions and challenges associated with various adaptive schemes, providing valuable lessons and summaries to guide further research. The future research directions of the adaptive scheme are discussed in Part 2 of the Appendix, aiming to address the current and emerging demands of wireless communication systems. Through this survey, we aim to enrich the discourse on adaptive schemes in physical layer security, paving the way for advanced research and development in enhancing the security of wireless networks.

Decision Tree Model-Based Security Situational Awareness Approach for Wireless Communication Networks

Network security situational awareness is gaining increasing attention due to its capability to globally and dynamically detect potential network security risks. However, traditional security situational awareness models often exhibit poor classification performance, resulting in lower-than-expected acceleration and scalability ratios. In this paper, we propose a novel security situational awareness approach for wireless communication networks based on a decision tree model. First, reconfigure the category division module to categorize the attack data into four different types. Then, using time windows to segment the data flow between the network and the host promotes the design of effective security event detection mechanisms in the model. Finally, a comprehensive network security situational awareness model was constructed at the joint level using decision tree algorithm. The experimental results show that the proposed method can significantly improve the acceleration ratio, and the space occupancy ratio can reach 80, indicating that the proposed method can have a high level of processing capability and accurate perception in network security situations, providing a guarantee for the security of wireless communication networks.

Enhancement of data security in wireless sensors network: application in internet of things

Enhancement of data security in wireless sensors network: application in internet of things

Machine Learning Techniques for Robust Security in Wireless Sensor Networks

Abstract: The security of wireless sensor networks (WSNs) is critical for ensuring the reliability and accuracy of real-time monitoring systems. A key vulnerability in WSNs is the risk of system breakdown or erroneous decision-making, which can lead to adverse consequences. Given the sensitivity of data handled by these networks, robust protection against various attacks and intrusions is imperative. However, existing security algorithms often fall short when applied to large-scale WSNs due to challenges such as high energy consumption, limited throughput, and excessive computational overhead. To address these challenges, this research introduces an intelligent middleware layer designed to enhance security in WSNs. The proposed solution utilizes Generative Adversarial Networks (GANs), an unsupervised machine learning technique, featuring generator (G) and discriminator (D) networks working adversarially to enhance WSN security. This intelligent middleware acts as a protective layer between WSNs and end-users, addressing vulnerabilities and mitigating threats. The approach ensures robust security while maintaining energy efficiency and scalability for real-world applications. By integrating advanced machine learning techniques, this research establishes a foundation for creating resilient and trustworthy wireless sensor networks capable of handling modern challenges effectively.

Physical Layer Security in wireless sensors networks with friendly jammer: Secrecy Outage Probability Analysis

Physical Layer Security in wireless sensors networks with friendly jammer: Secrecy Outage Probability Analysis

Wireless Network Management and Security Based on Wireless Gateway Security Controlling System

Seiring dengan perkembangan internet saat ini khususnya penggunaan Jaringan WiFi yang sudah tersedia di berbagai tempat, memaksa pemilik atau admin jaringan untuk bisa mengoptimalkan dan menjaga lalu lintas jaringan agar tetap stabil dan aman. Dimana sistem keamanan yang digunakan sudah harus mempertimbangkan jumlah user dan luas jaringan yang dibutuhkan. Pada penelitian ini penulis menerapkan sistem yang mengkolaborasikan Pengendali atau Controller Wireless UniFi Unify dan Router Mikrotik untuk mengoptimalkan Layanan Wireless agar mudah dan aman bagi user. Pendekatan yang digunakan dalam penelitian ini adalah pendekatan kualitatif. Adapun Langkah-langkah yang sudah dilakukan untuk pendekatan metode kualitatif adalah melakukan survey lokasi, merancang topologinya berdasarkan hasil survey lokasi dan menerapkan langsung menjadi sebuah system jaringan wireless dengan dukungan dari Unify Pengendali atau Controller Wireless UniFi dan Router Mikrotik, dimana dalam topologi ini dapat memantau, mengelola dan mengendalikan perangkat wireless dalam jumlah yang banyak dan dikendalikan dalam satu Controlling System dan Gateway. Hasil dari penelitian ini adalah kemudahan dalam mengelola Jaringan Wifi dalam skala yang luas. Manajemen perangkat wireless dalam jumlah besar dalam sistem pengendali atau controller wireless UniFi yang terpusat dan pemantauan dan pengaturan layanan kepada user secara terpusat baik itu Hak akses, yaitu kuota bandwidth dan waktu akses yang juga terpusat. Tentunya hal ini akan mempermudah admin untuk mengelola jaringan wireless dalam skala yang besar.

An Efficient Optimized Neural Network System for Intrusion Detection in Wireless Sensor Networks

In the realm of wireless network security, the role of intrusion detection cannot be overstated in identifying and thwarting malicious activities within communication channels. Despite the existence of various intrusion detection system (IDS) approaches, challenges persist in terms of accurate classification and specification. Consequently, this article introduces a novel and innovative approach, the African Vulture-based Modular Neural System (AVbMNS), to address these issues. This research aims to detect and categorize malicious events in wireless networks effectively. The methodology begins with preprocessing the dataset and extracting relevant features. These extracted features are then subjected to a novel training technique to enhance the detection and classification of network attacks. The integration of African Vulture optimization significantly enhances the detection rate, leading to more precise attack identification. The research's effectiveness is demonstrated through validation using the NSL-KDD dataset, with impressive results. The performance analysis reveals that the developed model achieves a remarkable 99.87% detection rate and 99.92% accuracy when applied to the NSL-KDD dataset. Furthermore, the outcomes of this novel model are compared with existing approaches to gauge the extent of improvement. The comparative assessment affirms that the developed model outperforms its counterparts, underscoring its effectiveness in addressing the challenges of intrusion detection in wireless networks.

Multi-RIS Aided VLC Physical Layer Security for 6G Wireless Networks

Multi-RIS Aided VLC Physical Layer Security for 6G Wireless Networks

Retraction notice: Literature review on network security in wireless mobile ad-hoc network for IoT applications: network attacks and detection mechanisms

Retraction notice: Literature review on network security in wireless mobile ad-hoc network for IoT applications: network attacks and detection mechanisms

ChessCrypt: enhancing wireless communication security in smart cities through dynamically generated S-Box with chess-based nonlinearity

In the contemporary landscape of smart cities, ensuring the security and confidentiality of transmitted data has become paramount. The proliferation of Internet of Things (IoT) devices, mobile communication platforms, and wireless sensor networks has magnified the need for robust cryptographic solutions to safeguard sensitive information from malicious adversaries. In response to these challenges, we introduce ChessCrypt—a novel approach to enhancing wireless communication security through the development of a new S-Box algorithm inspired by the nonlinear movement patterns of chess pieces. ChessCrypt leverages the dynamic and unpredictable nature of chess piece movements, namely knights, kings, and bishops, to introduce a high degree of nonlinearity and confusion into the encryption process. By incorporating elements of randomness and unpredictability, our proposed S-Box algorithm offers robust protection against a wide range of cyber threats, including eavesdropping, data interception, and cryptographic attacks. We demonstrate the effectiveness and resilience of ChessCrypt through rigorous testing and analysis, showcasing its superiority over traditional cryptographic methods in enhancing the security of wireless communication networks. Our results underscore the significance of ChessCrypt as a promising solution for fortifying wireless communication security in an increasingly interconnected world of ours.

Enhancing security in Wireless Body Area Networks (WBANs) with ECC-based Diffie-Hellman Key Exchange algorithm (ECDH).

Wireless Body Area Networks (WBANs) are integral to modern healthcare systems, providing continuous health monitoring and real-time data transmission. The sensitivity of medical data being transmitted makes security a significant concern in WBANs. This study explores the application of the Elliptic Curve Cryptography (ECC)-based Diffie-Hellman Key Exchange (ECDH) algorithm to enhance security within WBANs. The study investigates the suitability of ECC for this context and evaluates the performance and security implications of implementing ECDH in WBANs. The findings reveal that ECDH provides a robust and computationally efficient solution for secure key exchange in WBANs, addressing inherent vulnerabilities. The adoption of ECC-based ECDH is poised to bolster data confidentiality and integrity in WBANs, promoting trust and widespread use of these networks in healthcare applications. This research contributes to the growing body of knowledge regarding security measures in WBANs and opens new avenues for the secure transmission of sensitive medical information.

Wireless Network Security Prediction Using Machine Learning and Graph Neural Networks

Abstract: We study the application of graph neural networks and machine learning in the prediction of risks to wireless networks. Advanced predictive methods are necessary, since mere security measures cannot thwart the rising complexity of cyber-attacks. In the present work, we will use a hybrid model that combines several machine learning methods to reduce false negatives and positives and improve accuracy in the prediction of risks to wireless networks. Models are trained and validated with large amounts of data that include the performance indicators for accuracy, precision, recall, F1-score, and AUC-ROC. From this, it is also seen that the hybrid model performs much better than the standard models in real-time threat detection. The impact of the size of the dataset on the performance of the model was also studied, and it has come out that larger datasets improve predictive powers significantly. The result demonstrated that the latest advancements in machine learning techniques can lead to important improvements in the security of wireless networks.

ENERGY EFFICIENT ROUTING PROTOCOL FOR SECURITY ANALYSIS SCHEME USING HOMOMORPHIC ENCRYPTION

Wireless Sensor Networks (WSNs) have gained widespread interest as a result of developments in IT technology and the electronics industry. This ground-breaking sensing technology consists of multiple sensor nodes or motes that are placed in an atmosphere to detect constantly changing physical phenomena. These compact sensor nodes gather and interpret data via radio waves. The tiny size of these sensors is advantageous since they may be readily incorporated into any system or system. This capability has prompted the adoption of WSNs, particularly for form monitoring and tracking; most notably, monitoring apps. However, this small scale of sensor nodes limits the capacity of resources. Usually, the WSNs are installed in environments of unsafe or difficult human interference. Critical decisions in emergency applications can require sensible knowledge. It is necessary to check the network security. To extend the network security using Homomorphic Encryption effectively, the available resources must be expanded to a full view. This refers to the development of energy-efficient routing protocol strategies to ensure low energy consumption of common routing protocols and improve the availability and security of WSNs. Homomorphic encryption is effective in improving the security of wireless sensor networks.

Assessing the Impact of Cryptography on the Security of Wireless Ad Hoc Networks

The use of wireless networks for communication in bistros, airports, hotels, offices, colleges, and other public locations is increasing as they become more widespread. Although remote organisations and its applications are becoming increasingly widely recognised, security concerns related to them have grown to be of great concern. In this investigation, we'll develop a different plan to implement quantum cryptography for key distribution in 802.11 companies. They are helpful because of their adaptability and quick data transfer in residences, workplaces, and businesses. Given the principles of material science, quantum cryptography enables unfettered security for the exchange of cryptographic keys between two distant meetings. This investigation's main goal is to investigate potential vulnerabilities and security concerns in remote organization correspondence and give a structure to unqualified security in remote systems administration utilizing quantum key dispersion (QKD).

Jamming Attacks Detection Based on IGWO for Optimization of Fast Correlation-Based Feature extraction in Wireless Communication

Background: Wireless networks are essential communication technologies that prevent cable installation prices and burdens. Because of this technology's pervasive usage, wireless network safety is a significant problem. Owing to distributed and open wireless medium aspects, attackers might use different jamming methods to exploit physical and MAC layer protocol vulnerabilities. In addition, jamming attacks require to be accurately grouped so that suitable countermeasures can be considered. Given the potential severity of such attacks, precisely identifying and classifying them is critical for implementing effective responses. The motivation for this paper is the need to improve the detection and categorization of jamming signals using modern machine learning algorithms, consequently enhancing wireless network security and reliability. Objective: In this paper, we compare some machine learning models' efficiency for diagnosing jamming signals. Methods: Such algorithms refer to support vector machine (SVM) and k-nearest neighbors (KNN). We checked the signal features that recognize jamming signals. After the jamming attack model, the developed grey wolf optimizer version known as IGWO (improved grey wolf optimizer) has been discussed for feature extraction of software usability. Four separate metrics were employed as features to detect jamming attacks in order to evaluate the machine learning models. This novel feature extraction method is crucial for improving the accuracy of jamming detection. Results: The measurements of these parameters were gathered through a simulation of a real setting. And generated a large dataset using these parameters. Conclusion: The simulation results illustrate that the KNN algorithm based on jamming detection could diagnose jammers having a minimal likelihood of false alarms and a high level of accuracy.

Securing the Edge: CatBoost Classifier Optimized by the Lyrebird Algorithm to Detect Denial of Service Attacks in Internet of Things-Based Wireless Sensor Networks

The security of Wireless Sensor Networks (WSNs) is of the utmost importance because of their widespread use in various applications. Protecting WSNs from harmful activity is a vital function of intrusion detection systems (IDSs). An innovative approach to WSN intrusion detection (ID) utilizing the CatBoost classifier (Cb-C) and the Lyrebird Optimization Algorithm is presented in this work (LOA). As is typical in ID settings, Cb-C excels at handling datasets that are imbalanced. The lyrebird’s remarkable capacity to imitate the sounds of its surroundings served as inspiration for the LOA, a metaheuristic optimization algorithm. The WSN-DS dataset, acquired from Prince Sultan University in Saudi Arabia, is used to assess the suggested method. Among the models presented, LOA-Cb-C produces the highest accuracy of 99.66%; nevertheless, when compared with the other methods discussed in this article, its error value of 0.34% is the lowest. Experimental results reveal that the suggested strategy improves WSN-IoT security over the existing methods in terms of detection accuracy and the false alarm rate.

Retraction Note: Design and evaluation of novel hybrid quantum resistant cryptographic system for enhancing security in wireless body sensor networks

Retraction Note: Design and evaluation of novel hybrid quantum resistant cryptographic system for enhancing security in wireless body sensor networks

تحليل وتصميم شبكة محلية لاسلكية آمنة للحرم الجامعي في الجامعات الليبية

This research presents the wireless university campus network and discusses about the strengths and weaknesses of Design a secure wireless campus networks for ABC University in Libya, and about the costs and advantages of different vendor solutions. Wireless offers mobility and is available in locations where a wired outlet doesn't exist. The disadvantages of wireless include some minor interference problems and a quickly evolving and competitive group of standards. The purpose of the research is designing and implementing a secure campus network for Libyan university based on Cisco equipments. Moreover, the research can provide practical experience in network design, configuration, and security for helping organizations and institutions protect their network resources and data. Two end products or deliverables will result from the research: the first is a new architectural design for an efficient and advanced WLAN-friendly network to ensure the high performance of the data transaction process and ensure high availability of data transmissions. The second product is a report for network administrators or network architecture to guide users towards the achievement of WLAN-friendly by utilising advanced networking tools and technologies. Keywords: - Campus network; Wireless Mesh Network; wireless network; Encryption; Cisco equipment; security.

A practical scheme for enhancing consistency and independence in wireless key generation for Wi-Fi networks

Wi-Fi networks benefit from physical layer wireless key generation, a theoretically secure method for improving information transmission security. This paper introduces the Consistent Independent Key Generation (CIKG) scheme based on the Wi-Fi signal transmission model. It addresses the dual challenges of channel noise and virtual carrier low-pass effects. The scheme begins with extracting the channel frequency response (CFR) from the entire signal preamble, including both long and short symbols, followed by applying cubic spline interpolation to the short symbol-derived CFR to achieve fine-grained frequency fading corresponding to the long symbols. This refined CFR is averaged to bolster noise resistance, improving key consistency. Subsequently, the inverse Fourier transform is used to obtain the channel impulse response (CIR), and a deconvolution strategy reduces the impact of virtual carrier low-pass filtering on the multipath information of CIR, thus improving key independence. Implemented within Wi-Fi networks, the effectiveness of the CIKG scheme is rigorously tested across diverse scenarios. Quantitative evaluations indicate that the scheme substantially improves key consistency, achieving a 5 dB enhancement in signal-to-noise ratio over traditional CFR-based schemes and elevates information entropy by 20%, significantly boosting key independence. These advances affirm the potential of the CIKG scheme as a formidable solution for developing robust and secure wireless communication networks.

Channel state information based physical layer authentication for Wi‐Fi sensing systems using deep learning in Internet of things networks

AbstractSecurity problems loom big in the fast‐growing world of Internet of Things (IoT) networks, which is characterised by unprecedented interconnectedness and data‐driven innovation, due to the inherent susceptibility of wireless infrastructure. One of the most pressing concerns is user authentication, which was originally intended to prevent unwanted access to critical information but has since expanded to provide tailored service customisation. We suggest a Wi‐Fi sensing‐based physical layer authentication method for IoT networks to solve this problem. Our proposed method makes use of raw channel state information (CSI) data from Wi‐Fi signals to create a hybrid deep‐learning model that combines convolutional neural networks and long short‐term memory networks. Rigorous testing yields an astonishing 99.97% accuracy rate, demonstrating the effectiveness of our CSI‐based verification. This technology not only strengthens wireless network security but also prioritises efficiency and portability. The findings highlight the practicality of our proposed CSI‐based physical layer authentication, which provides lightweight and precise protection for wireless networks in the IoT.