Wireless Security Research Articles

Zero‐Bias Uncooled Broadband Terahertz Detection with a TaFe1.25Te3/Graphene Heterostructure for Imaging and Communication

AbstractTerahertz (THz) waves offer large bandwidth with less interference and provide detailed information on materials in a non‐destructive manner, making them valuable for wireless communication, security screening, and biomedical identification. Despite the growing significance, the demand for high‐performance and ultra‐broadband THz detectors remains unmet for current limitations. In this paper, we propose a high‐sensitivity broadband THz detector utilizing the semimetal TaFe1.25Te3, leveraging its gapless electron excitation under THz illumination and fast carrier dynamics through the photothermoelectric effect at room temperature. The TaFe1.25Te3 detector presents a fast response speed of 11.1/5.6 µs, a responsivity of 7.58 V W−1, and a noise‐equivalent power (NEP) of 156 pW/Hz1/2 at 0.1 THz. By incorporating graphene within a van der Waals heterostructure, the self‐powered response is dramatically enhanced by an order of magnitude. The TaFe1.25Te3/graphene detector features an extended response up to 0.3 THz, with an improved responsivity of 279.15 V W−1 and a lower NEP of 9 pW/Hz1/2 at 0.1 THz, thanks to the efficient energy transfer and optimized photothermoelectric characteristics in the asymmetry architecture. Our research deepens the understanding of ultrafast hot carrier dynamics in TaFe1.25Te3 and offers a promising approach to manipulate the photothermoelectric response, advancing their functionality and versatility for practical applications.

Enhanced RSSD-based Unknown Radio Emitter Localization Via Weighted Grey Correlation Degree

Abstract Accurate tracking of indoor unknown radio emitter (URE) is a key task in the field of wireless communication security. Fingerprint positioning methods based on received signal strength difference (RSSD) perform well on URE localization challenges with unknown transmitting power and frequency. Since the noise level of RSSD parameters is amplified compared to received signal strength, the matching accuracy between RSSD signal features and geographical locations is reduced. This paper presents a RSSD-based positioning algorithm using improved singular value decomposition (SVD) and weighted grey correlation degree (GCD), called ISVD-WGCD, to increase noise robustness and positioning accuracy. Firstly, local least squares curve fitting is applied to optimize the selection of singular value truncation point to reduce the noise level of RSSD offline database. Secondly, combining the offline RSSD singular value variation coefficient and online RSSD standard deviation, an AP weight allocation scheme is designed to set the trust degrees of different APs reasonably. Finally, a RSSD-based weighted GCD method is proposed by AP weight allocation to improve the matching accuracy between offline and online RSSDs, and to select the optimal reference points. Compared with the conventional weighted K-nearest neighbors (WKNN), SVD-KNN, Bayes, convolutional neural network (CNN) and FUZZY-GREY algorithms, simulation and experimental results show that the proposed method can obtain superior positioning performance under different AP numbers, grid distances and noise levels.

Security in Wireless Sensor Networks Using OMNET++: Literature Review

With the essential increase in the use of wireless sensor networks, security is a major concern in every field. Intrusions have become frequent and present a significant challenge in today’s world. It is valuable to explore the feasibility of designing and rigorously assessing intrusion detection systems within network simulation environments. Wireless sensor network security risk prediction is a key aspect of wireless network security technology. Analyzing the current state of wireless networks, security is a crucial step in ongoing research in the field of network security. In this paper, we discuss how OMNET++ is used for intrusion detection for different types of attacks in wireless sensor networks, what frameworks and protocols are used in OMNET++, and why OMNET++ is used, along with a few security attacks in wireless networks.

Enhancing Wireless Security through Deep Learning: Network Attack Detection and Advanced Classification

Enhancing Wireless Security through Deep Learning: Network Attack Detection and Advanced Classification

Multi‐Channel Secret Sharing and Camouflage in Programmable Metasurface‐Based Wireless Communication

AbstractInformation security has become increasingly critical with the widespread development of wireless technologies. However, current information security technologies predominantly rely on either software‐layer or physical‐layer encryption as standalone solutions, both of which present fundamental constraints. In this article, a novel architecture for multi‐channel secret sharing and camouflage is proposed by combining both software‐ and physical‐layer security mechanism based on a programmable metasurface. At the software layer, a one‐time pad (OTP) algorithm employs exclusive‐OR (XOR) operations with dynamic keys and predefined camouflage data to encrypt and share secrets. At the physical layer, the programmable metasurface spatially disperses the shared messages through reconfigurable multi‐beam steering and polarization control. Over‐the‐air experiments demonstrate that the proposed architecture successfully realizes dynamic OTP secret sharing, a capability unattainable with static metasurface‐based systems. The robustness of the system is also verified, underscoring its potential for real‐world applications. Crucially, legitimate users must collaboratively combine all distributed keys and encrypted messages for secret recovery, whereas eavesdroppers intercepting individual channels obtain only non‐decryptable fragments, which correspond to predefined camouflage data. This work synergistically integrates software‐layer encryption with the physical‐layer functionalities of programmable metasurfaces, offering transformative potential for future highly secure wireless networks and distributed data storage architectures.

WiFi Security- A Tutorial

In this paper, the subject of WiFi security is addressed in a tutorial fashion. The topic is not covered in Network security textbooks. Most information is available in white papers, blogs, etc. It is felt that a comprehensive treatment of this subject helps teachers to teach this subject in a Network security course and enables students to be familiar with the evolution of WiFi security and the current state of the art.

Network Encryption Traffic Anomaly Detection Based on Integrated Machine Learning

Network Encryption Traffic Anomaly Detection Based on Integrated Machine Learning

Secure Wireless Communication for Correlated Legitimate User and Eavesdropper Channels via Movable-Antenna Enhanced Frequency Diverse Array

Physical-layer (PHY) security is widely used as an effective method for ensuring secure wireless communications. However, when the legitimate user (LU) and the eavesdropper (Eve) are in close proximity, the channel coupling can significantly degrade the secure performance of PHY. Frequency diverse array (FDA) technique addresses channel coupling issues by introducing frequency offsets among array elements. However, FDA’s ability to secure communication relies mainly on frequency domain characteristics, lacking the spatial degrees of freedom. The recently proposed movable antenna (MA) technology serves as an effective approach to overcome this limitation. It offers the flexibility to adjust antenna positions dynamically, thereby further decoupling the channels between LU and Eve. In this paper, we propose a novel MA-FDA approach, which offers a comprehensive solution for enhancing PHY security. We aim to maximize the achievable secrecy rate through the joint optimization of all antenna positions at the base station (BS), FDA frequency offsets, and beamformer, subject to the predefined regions for antenna positions, frequency offsets range, and energy constraints. To solve this non-convex optimization problem, which involves highly coupled variables, the alternating optimization (AO) method is employed to cyclically update the parameters, with the projected gradient ascent (PGA) method and block successive upper-bound minimization (BSUM) method being employed to tackle the challenging subproblems. Simulation results demonstrate that the MA-FDA approach can achieve a higher secrecy rate compared to the conventional phased array (PA) or fixed-position antenna (FPA) schemes.

Implementation of Hardening for Optimization of Wireless Local Area Network Security

Computer networks use two main methods for data transmission, namely wired and wireless networks or what is known as a Wireless Local Area Network (WLAN). In WLAN networks, the security standard usually used is the WiFi Protected Access 2 Pre-Shared Key or WPA2-PSK protocol, which utilizes SSID and password. Despite using security mechanisms such as WPA2-PSK, criminal activities such as intrusion into the network still occur. Therefore, it is necessary to improve the network security system to ensure that the WLAN network is more secure and can minimize potential risks to users. This study aims to improve and optimize the WLAN network security system through vulnerability scanning using the Nessus tool to test the level of security on the WLAN network and the application of hardening methods to strengthen and disguise vulnerabilities on the WLAN network, which includes various security techniques such as applying raw firewalls and firewall filters, limiting ports and services used and disabling services that are not needed to minimize vulnerabilities, disabling the Mikrotik neighbor discovery protocol service on the router, and implementing port knocking as an additional layer of security. The results of the study show that applying vulnerability scanning techniques and hardening methods can help minimize the level of risk of vulnerabilities found and make the resilience of the WLAN network more optimal so as to avoid potential intrusions and threats of attacks.

AI-enabled security mechanisms for WLANs: ensuring robust and adaptive protection in wireless networks

Wireless Local Area Networks (WLANs) have become important to current digital infrastructures, linking various devices and enabling smooth data transmission. However, their ubiquity makes them attractive targets for cybercriminals, who constantly look for imaginative techniques to access wireless networks. With its predictive, adaptive, and very responsive defense, artificial intelligence (AI) presents a potent weapon for enhancing WLAN security. Examining how machine learning models detect and minimize risks with unheard-of speed and accuracy, this paper investigates the possibilities of AI-enabled security measures. Different approaches—including autonomous decision-making and real-time data analysis—are explored to show how artificial intelligence might find zero-day exploits and advanced cyberattacks. Furthermore, illuminating effective practices for strong wireless defense are important results from literature and pragmatic uses. The paper ends by stressing the possibilities and difficulties of including artificial intelligence in WLAN security and outlining future directions.

Quantum Key Distribution (QKD) as a Wireless Telecommunications Security Solution

The purpose of this research is to analyze and explore quantum theorems as a wireless telecommunications security solution in the future. This study uses a quantitative approach through simulation and performance analysis of QKD systems in wireless channels, by combining literature studies and comparative analysis between QKD and PQC. Meanwhile, the wireless communication simulation model, using the QKD protocol and PQC algorithm, with Free Space Optics (FSO), WiFi, and LiFi-based network scenarios. The simulation was carried out using MATLAB. This study is based on QKD, which uses BB84 and CV-QKD protocols on FSO 100m and LiFi wireless channels. And based on PQC, which uses the Kyber algorithm, with AES authentication for communication. Both models were simulated and tested based on the parameters of Quantum Bit Error Rate, Key Generation Rate, latency, and resistance to third-party attacks (wiretapping detection). Furthermore, the analysis was carried out quantitatively and comparatively to compare the performance of QKD and PQC based on the QBER, latency, KGR, and wiretapping detection of implementation. This analysis are combined with data based on the literature, to formulate optimal implementation strategies in the context of future wireless networks. The results indicate that QKD, specifically CV-QKD, has significant potential for use in attack-sensitive wireless communications, such as military, government, and industrial applications. However, this model requires more complex hardware and infrastructure investments in its implementation. Meanwhile, PQC offers a more ready-to-use and cost-effective solution for everyday communications that remains resilient to quantum attacks.

An enhanced asymmetric quantization Boolean encryption for secure routing in wireless sensor networks

For Wireless Sensor Networks (WSNs) to be both secure and effective, a secure routing method is essential. Enhancing data aggregation, data security, and routing security has been the focus of recent research; nonetheless, these strategies frequently face major obstacles, such as time complexity, vulnerability to malicious assaults, and worries about data insecurity. In order to tackle these problems, a secure routing protocol for WSNs named Asymmetric Quantization Boolean Encryption Growth Network with Leopard Seal Routing Protocol (AQBEGNet-LSRP) has been suggested. In order to cluster sensor nodes, this model uses the Growth Optimizer (GO) technique, choosing the Cluster Head (CH) according to node similarity. A Deep Progressive Asymmetric Quantization Neural Network (DPAQNNet) is used for data aggregation, and Boolean Network Encryption with Asynchronous Updating Algorithm (BNEAUA) protects data during transmission and thwarts attacks. For encrypted data transit, the best routes are then chosen by the upgraded Leopard Seal Routing Protocol (LSRP). With its remarkable performance characteristics, which include a 110 Kbps throughput, an 80% network lifetime, and a low latency of 0.02[Formula: see text]ms, in addition to its 99% Packet Delivery Ratio (PDR) and 99% network security, the suggested model is a dependable and effective method for transmitting secure data.

"Advancements in Ethical Hacking Techniques and Tools: Strengthening Cybersecurity Resilience"

Ethical hacking, or white-hat hacking, has become a cornerstone of modern cybersecurity, offering proactive measures to identify and mitigate vulnerabilities in systems, networks, and applications. This comprehensive review explores the advancements in ethical hacking techniques and tools, emphasizing their critical role in bolstering cybersecurity defences. Key phases of ethical hacking include network reconnaissance, vulnerability assessment, exploitation, social engineering, web application testing, and wireless security testing. Each phase employs specialized techniques to uncover weaknesses, such as port scanning, penetration testing, and phishing simulations, which help organizations understand their security posture. It discusses the techniques used in each phase of ethical hacking and provides an overview of the tools employed by ethical hackers to assess and enhance the security posture of organizations. By leveraging these techniques and tools, ethical hackers play a crucial role in proactively identifying and addressing security weaknesses, thereby mitigating the risk of cyber threats and enhancing overall cybersecurity resilience.

A Machine Learning-Based Hybrid Encryption Approach for Securing Messages in Software-Defined Networking

The security of a network is based on the foundation of confidentiality, integrity, and availability, often referred to as the CIA triad. The privacy of data over a network, maintained by confidentiality, has long been one of the major issues in network settings. With the decoupling of the data plane and control plane in the software-defined networking (SDN) environment, this challenge is significantly amplified. This paper aims to address the challenges of confidentiality in SDN by introducing a genetic algorithm-based hybrid encryption network policy to secure messages across the network. The proposed approach achieved an average entropy of 0.989, revealing a significant improvement in the strength of the encryption with the hybrid mechanism. However, the method exhibited processing overhead, significantly increasing the transmission time for encrypted messages compared to unencrypted transmission. Compared to standalone AES, DES, and RSA, this approach shows better encryption randomness, but a trade-off between security and network performance is evident in the absence of load-balancing techniques.

The Implementation of Neural Network Encryption Algorithms for Side-Channel Attack Protection

The Implementation of Neural Network Encryption Algorithms for Side-Channel Attack Protection

Decentralized Trace-Resistant Self-Sovereign Service Provisioning for Next-Generation Federated Wireless Networks

With the advent of NextG wireless networks, the reliance on centralized identity and service management systems poses significant challenges, including limited interoperability, increased privacy vulnerabilities, and the risk of unauthorized tracking or monitoring of user activity. To address these issues, there is a critical need for a decentralized framework that empowers users with self-sovereignty over their subscription information while maintaining trust and privacy among network entities. This article presents a novel framework to enable Self-Sovereign Federated NextG (SSFXG) wireless communication networks. The SSFXG framework separates identity management from the service management layer typically controlled by network operators to foster interoperability functionalities with enhanced privacy and trace-resistant assurances in the NextG landscape. The proposed model relies on blockchain technology as an infrastructure to enable single-authority-free service provisioning and boost mutual trust among federated network components. Further, the SSFXG framework facilitates subscribers’ self-sovereignty over their subscription information while ensuring anonymity and enhanced privacy preservation, avoiding unnecessary network activity monitoring or tracking. Preliminary evaluations demonstrated the effectiveness and efficiency of the proposed framework, making it a promising solution for advancing secure and interoperable NextG wireless networks.

Investigation of Users’ Attitude, Effectiveness and Awareness towards WLAN Security in Selected Federal Universities in Southwestern Nigeria

Investigation of Users’ Attitude, Effectiveness and Awareness towards WLAN Security in Selected Federal Universities in Southwestern Nigeria

A Scalable Framework for Secure and Reliable Wireless-based Fog Cloud Communication

A Scalable Framework for Secure and Reliable Wireless-based Fog Cloud Communication

A Review on Securing Wireless Networks against IIRS Proliferation and Jamming Attacks

The internet of thins (IoT) framework has helped in design of high speed connected networks which include Wide Area Networks (WANs). Cognitive Radio can be considered to be one of the key enablers of high speed data transfer with evolving generation of wireless networks. Cognitive networks find their applications in military and defence as well with the advent of internet of things and its allied applications in military warfare. Cognitive Radio Networks often share common resources such as bandwidth or spectrum among several users or stations. Due to continued sharing of resources, cognitive networks often come under security attacks, most common of which are jamming and eavesdropping attacks. In this paper, the basics of cognitive IoT networks have been presented with the focus of security aware cognitive IoT networks. Previous work in the allied domain has been discussed along with their salient features. Keywords: Wide Area Network (WAN), Internet of Things (IoT), cognitive radio network (CRN), security aware spectrum assignment, cyber-attacks, false alarm, and throughput.

A dynamic integrity and data confidentiality based wireless N2N data communication and security protocol on large networks

WANET is a network built in an ad hoc network where various connecting devices and moving wireless nodes make connection with the exchange valuable information and wireless medium to one another. As the size of the wireless networks and internet architectures are increasing, there is an exponential demand for data processing and sharing resources in the WANETs. Most of the traditional node authentication models in WANET’s require high computational memory and resource constraints for data security and node authentication. In order to overcome these problems in the traditional models, a novel non-linear integrity-based intrusion detection models is designed and implemented in WANETs. In this work, a hybrid security protocol is designed and developed for the node-to-node communication system. In the proposed security protocol, a hybrid integrity approach and encryption framework is implemented on N2N communication process. Experimental results show that the present security protocol has better improvement on different metrics over traditional security approaches.