Network Security Research Articles

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.

Physical-Layer Security for RIS-V2V Networks With Different Eavesdropper Locations

Physical-Layer Security for RIS-V2V Networks With Different Eavesdropper Locations

Retraction Note: Optical network modelling-based data analytics for network monitoring and security analysis using hybrid computing models

Retraction Note: Optical network modelling-based data analytics for network monitoring and security analysis using hybrid computing models

The Integration of Artificial Intelligence in Secure Access Service Edge: Enhancing Network Security and Performance

Integrating Artificial Intelligence (AI) with Secure Access Service Edge (SASE) architecture represents a significant advancement in enterprise network security and management. This article examines the transformative impact of AI technologies on SASE implementations, focusing on enhanced threat detection capabilities, dynamic network optimization, and automated compliance management. The article comprehensively analyzes AI-driven security features, including real-time threat detection, zero-trust implementation, and privacy-preserving security measures. Through extensive evaluation of deployment scenarios and performance metrics, our research demonstrates significant improvements in security effectiveness and operational efficiency, including a 90% reduction in threat detection time, a 45% decrease in network latency, and a 65% reduction in manual configuration tasks. The article highlights the system's capability to maintain robust security while preserving data privacy through advanced encrypted traffic analysis techniques. The article addresses current integration challenges and provides insights into successful implementation strategies. This article contributes to the growing body of knowledge on AI-enhanced network security architectures and provides valuable insights for organizations seeking to modernize their security infrastructure while maintaining operational efficiency and compliance with evolving regulatory requirements.

Nash Bargaining-Based Coordinated Frequency-Constrained Dispatch for Distribution Networks and Microgrids

As the penetration of distributed renewable energy continues to increase in distribution networks, the traditional scheduling model that the inertia and primary frequency support of distribution networks are completely dependent on the transmission grid will place enormous regulatory pressure on the transmission grid and hinder the active regulation capabilities of distribution networks. To address this issue, this paper proposes a coordinated optimization method for distribution networks and microgrid clusters considering frequency constraints. First, the confidence interval of disturbances was determined based on historical forecast deviation data. On this basis, a second-order cone programming model for distribution networks with embedded frequency security constraints was established. Then, microgrids performed economic dispatch considering the reserves requirement to provide inertia and primary frequency support, and a stochastic optimization model with conditional value-at-risk was built to address uncertainties. Finally, a cooperative game between the distribution network and microgrid clusters was established, determining the reserve capacity provided by each microgrid and the corresponding prices through Nash bargaining. The model was further transformed into two sub-problems, which were solved in a distributed manner using the ADMM algorithm. The effectiveness of the proposed method in enhancing the operational security and economic efficiency of the distribution networks is validated through simulation analysis.

REVEALING ANOMALIES BY NETWORK PACKET FLOODING ON BUILT FTP AND OPENSSH SERVERS IN CONTROLLED LAB ENVIRONMENT

This scientific paper investigates the effects of network packet flooding on FTP (port 21) and SSH (port 22) protocols, aiming to reveal and document anomalies in server behavior under high-load conditions. By simulating packet flooding in a controlled lab environment, an analysis on vulnerabilities and anomalies unique to each protocol is conducted in order to the improve defensive capabilities. The results provide guidance on best practices to secure FTP and OpenSSH services against malicious traffic, such as Distributed Denial of Service (DDoS) attacks, supporting wider network security strategies.

Cybersecurity Challenges in Net Energy Metering: Legal Frameworks and Practical Solutions

Net Energy Metering (NEM) has emerged as a key factor in Modern Energy Management, empowering consumers to produce their own electricity and return Net Excess Generation (NEG) to the grid. However, the increased usage of NEM has introduced significant network security challenges. As millions of electronic devices are interconnected through communication networks across critical power facilities cybersecurity has become a concerning factor, directly affecting the reliability of such extensive infrastructure. This paper gives an insight into the rising cyber offences associated with NEM systems, stipulates the legal framework, e-governance, and liabilities under the Information Technology (IT) Act, 2000. Categorization of potential cyber threats like Access control, database security and encryption, intrusion detection, malicious software, operating system vulnerabilities and security, application security has been highlighted. Evaluation of Time of Day (ToD) pricing mechanism focusing on real time monitoring and energy efficiency during peak and off-peak consumption. Incorporating Virtual Private Networks (VPNs) and other cybersecurity measures to safeguard the integrity and reliability of NEM systems. The paper illustrates the impact of cybercrimes on NEM, by exploring existing applications and case studies. It underscores the necessity of implementing robust cybersecurity practices to protect these systems. The objective of the paper is to raise awareness about the cybersecurity challenges encountered in NEM systems and to propose practical solutions to mitigate these risks, ensuring a safe and regulated environment for the functioning of smart grids in a progressively growing digital world.

A Detailed Inspection of Machine Learning Based Intrusion Detection Systems for Software Defined Networks

The growing use of the Internet of Things (IoT) across a vast number of sectors in our daily life noticeably exposes IoT internet-connected devices, which generate, share, and store sensitive data, to a wide range of cyber threats. Software Defined Networks (SDNs) can play a significant role in enhancing the security of IoT networks against any potential attacks. The goal of the SDN approach to network administration is to enhance network performance and monitoring. This is achieved by allowing more dynamic and programmatically efficient network configuration; hence, simplifying networks through centralized management and control. There are many difficulties for manufacturers to manage the risks associated with evolving technology as the technology itself introduces a variety of vulnerabilities and dangers. Therefore, Intrusion Detection Systems (IDSs) are an essential component for keeping tabs on suspicious behaviors. While IDSs can be implemented with more simplicity due to the centralized view of an SDN, the effectiveness of modern detection methods, which are mainly based on machine learning (ML) or deep learning (DL), is dependent on the quality of the data used in their modeling. Anomaly-based detection systems employed in SDNs have a hard time getting started due to the lack of publicly available data, especially on the data layer. The large majority of existing literature relies on data from conventional networks. This study aims to generate multiple types of Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks over the data plane (Southbound) portion of an SDN implementation. The cutting-edge virtualization technology is used to simulate a real-world environment of Docker Orchestration as a distributed system. The collected dataset contains examples of both benign and suspicious forms of attacks on the data plane of an SDN infrastructure. We also conduct an experimental evaluation of our collected dataset with well-known machine learning-based techniques and statistical measures to prove their usefulness. Both resources we build in this work (the dataset we create and the baseline models we train on it) can be useful for researchers and practitioners working on improving the security of IoT networks by using SDN technologies.

Yara-Based Emotet Malware Scanner – A Factual Analysis

Malware is malicious software which has the ability to compromise sensitive data and disrupt systems. At present malicious software is the most efficient tool used in compromising the security of computers or any other electronic devices connected to the internet. This has become a menace owing to the rapid progress in technologies such as encryption and data hiding techniques. A highly adaptable and persistent type of malware, Emotet is well-known for its capacity to propagate via phishing emails and send out further malicious payloads, by impairing network and data security. Emotet poses a special risk since it serves as a gateway for other viruses, such as ransomware and data thieves, which can cause serious data breaches, monetary losses, and interruptions to business operations. It poses a serious danger to both individual users and big enterprises due to its advanced evasion techniques and quick network spread. In this project, distinct file signatures and byte patterns that are distinctive of the malware are identified by YARA rules in order to detect Emotet. These patterns include specific sequences used in Emotet’s payload and obfuscation techniques, allowing for precise detection within scanned files.

A Cross-Layer Secure and Energy-Efficient Framework for the Internet of Things: A Comprehensive Survey

This survey delves into cross-layer energy-efficient solutions and cutting-edge security measures for Internet of Things (IoT) networks. The conventional security techniques are considered inadequate, leading to the suggestion of AI-powered intrusion detection systems and novel strategies such as blockchain integration. This research aims to promote the development of smart cities by enhancing sustainability, security, and efficiency in the industrial and agricultural sectors through the use of IoT, blockchain, AI, and new communication technologies like 5G. In this paper, we provide a comprehensive review and analysis of secure and energy-efficient cross-layer IoT frameworks based on survey of more than 100 published research articles. We highlight the significance of developing IoT security for robust and sustainable connected systems. We discuss multi-layered security approaches and ways to enhance the energy efficiency of resource-constrained devices in IoT networks. Finally, we identify open research issues and future research directions in the emerging field of cross-layer design for secure and energy-efficient IoT networks. In order to improve cybersecurity and efficiency in smart cities, the research also focuses on developing a secure, energy-efficient IoT framework integrating blockchain, artificial intelligence, and quantum-safe cryptography.

A Systematic Literature Review on Cyber Attack Detection in Software-Define Networking (SDN)

The increasing complexity and sophistication of cyberattacks pose significant challenges to traditional network security tools. Software-defined networking (SDN) has emerged as a promising solution because of its centralized management and adaptability. However, cyber-attack detection in SDN settings remains a vital issue. The current literature lacks comprehensive assessment of SDN cyber-attack detection methods including preparation techniques, benefits and types of attacks analysed in datasets. This gap hinders the understanding of the strengths and weaknesses of various detection approaches. This systematic literature review aims to examine SDN cyberattack detection, identify strengths, weaknesses, and gaps in existing techniques, and suggest future research directions in this critical area. A systematic approach was used to review and analyse various SDN cyberattack detection techniques from 2017--2024. A comprehensive assessment was conducted to address these research gaps and provide a comprehensive understanding of different detection methods. The study classified attacks on SDN planes, analysed detection datasets, discussed feature selection methods, evaluated approaches such as entropy, machine learning (ML), deep learning (DL), and federated learning (FL), and assessed metrics for evaluating defense mechanisms against cyberattacks. The review emphasized the importance of developing SDN-specific datasets and using advanced feature selection algorithms. It also provides valuable insights into the state-of-the-art techniques for detecting cyber-attacks in SDN and outlines a roadmap for future research in this critical area. This study identified research gaps and emphasized the importance of further exploration in specific areas to increase cybersecurity in SDN environments.

Policing Europe: The Origins of a European Security Network, 1794–1815

Abstract The spread of revolutionary ideas reconfigured the machinery of surveillance within the Habsburg state between the Jacobin Trials of 1794 and the Congress of Vienna in 1815. The Habsburg Ministry of Police emerged from the Jacobin Trials with enlarged administrative capabilities, including a new relationship between the secret state police and the military and the increased surveillance of urban working populations. Not only that, but the period also witnessed a push to monitor subversive political activity in foreign territory, including Poland, the Ottoman Empire, the German states and Italy. This article argues that cooperation among European states in the policing of subversive political activity across borders antedated the Congress of Vienna. A focus on these critical decades questions the viability of the ‘Metternichian System’ and points instead to a growing security apparatus that Metternich sought to further internationalize after 1815.

An Efficient Network Intrusion Detection and Classification System using Machine Learning

In today's digital landscape, network security is of paramount importance, with intrusion detection systems (IDS) playing a crucial role in protecting sensitive data from malicious attacks. Traditional IDS, often reliant on signature-based methods, struggle with high false positive rates, difficulty in adapting to novel threats, and significant computational demands. This paper explores the development of an efficient network intrusion detection and classification system utilizing machine learning techniques to address these challenges. By leveraging datasets such as NSL-KDD and UNSW-NB15, our study employs a combination of supervised learning algorithms, including Support Vector Machines (SVM), Random Forests, and Neural Networks, alongside comprehensive data preprocessing and feature engineering strategies. The evaluation of our models through metrics like accuracy, precision, recall, and ROC-AUC demonstrates a marked improvement in detection capabilities and computational efficiency. Our findings suggest that machine learning-based IDS can significantly enhance network security by reducing false positives and adapting to emerging threats more effectively than traditional systems. This research not only underscores the potential of advanced machine learning techniques in IDS but also provides a robust framework for future developments in the field. In the rapidly evolving landscape of cybersecurity, effective network intrusion detection and classification systems are critical for safeguarding sensitive data and maintaining operational integrity. This paper presents a novel approach utilizing machine learning techniques to enhance the efficiency and accuracy of intrusion detection systems (IDS). By employing a combination of supervised and unsupervised learning algorithms, our system can identify and classify both known and unknown threats in real-time. We leverage advanced feature selection methods to optimize the performance of our models, ensuring high detection rates with minimal false positives. Our experimental results, validated on benchmark datasets, demonstrate significant improvements in detection accuracy and processing speed compared to traditional IDS solutions. The proposed system not only strengthens network defenses but also provides a scalable and adaptive framework for future cybersecurity challenges..

An Elliptic Curve Based Approach for Enhanced Security of Substitution-Permutation Networks

An Elliptic Curve Based Approach for Enhanced Security of Substitution-Permutation Networks

A Survey on Security of UAV Swarm Networks: Attacks and Countermeasures

The increasing popularity of Unmanned Aerial Vehicle (UAV) swarms is attributed to their ability to generate substantial returns for various industries at a low cost. Additionally, in the future landscape of wireless networks, UAV swarms can serve as airborne base stations, alleviating the scarcity of communication resources. However, UAV swarm networks are vulnerable to various security threats that attackers can exploit with unpredictable consequences. Against this background, this paper provides a comprehensive review on security of UAV swarm networks. We begin by briefly introducing the dominant UAV swarm technologies, followed by their civilian and military applications. We then present and categorize various potential attacks that UAV swarm networks may encounter, such as denial-of-service attacks, man-in-the-middle attacks and attacks against Machine Learning (ML) models. After that, we introduce security technologies that can be utilized to address these attacks, including cryptography, physical layer security techniques, blockchain, ML, and intrusion detection. Additionally, we investigate and summarize mitigation strategies addressing different security threats in UAV swarm networks. Finally, some research directions and challenges are discussed.

A Security Situation Prediction Model for Industrial Control Network Based on Explainable Belief Rule Base

Industrial Control Systems (ICSs) are vital components of industrial production, and their security posture significantly impacts operational safety. Given that ICSs frequently interact with external networks, cyberattacks can disrupt system symmetry, thereby affecting industrial processes. This paper aims to predict the network security posture of ICSs to ensure system symmetry. A prediction model for the network security posture of ICSs was established utilizing Evidence Reasoning (ER) and Explainable Belief Rule Base (BRB-e) technologies. Initially, an evaluation framework for the ICS architecture was constructed, integrating data from various layers using ER. The development of the BRB prediction model requires input from domain experts to set initial parameters; however, the subjective nature of these settings may reduce prediction accuracy. To address this issue, an ICS network security posture prediction model based on the Explainable Belief Rule Base (BRB-e) was proposed. The modeling criteria for explainability were defined based on the characteristics of the ICS network, followed by the design of the inference process for the BRB-e prediction model to enhance accuracy and precision. Additionally, a parameter optimization method for the explainable BRB-e prediction model is presented using a constrained Projection Equilibrium Optimization (P-EO) algorithm. Experiments utilizing industrial datasets were conducted to validate the reliability and effectiveness of the prediction model. Comparative analyses indicated that the BRB-e model demonstrates distinct advantages in both prediction accuracy and explainability when compared to other algorithms.

Analytics and measuring the vulnerability of communities for complex network security

Analytics and measuring the vulnerability of communities for complex network security

Revisiting streaming anomaly detection: benchmark and evaluation

Anomaly detection in streaming data is an important task for many real-world applications, such as network security, fraud detection, and system monitoring. However, streaming data often exhibit concept drift, which means that the data distribution changes over time. This poses a significant challenge for many anomaly detection algorithms, as they need to adapt to the evolving data to maintain high detection accuracy. Existing streaming anomaly detection algorithms lack a unified evaluation framework that validly assesses their performance and robustness under different types of concept drifts and anomalies. In this paper, we conduct a systematic technical review of the state-of-the-art methods for anomaly detection in streaming data. We propose a new data generator, called SCAR (Streaming data generator with Customizable Anomalies and concept dRifts), that can synthesize streaming data based on synthetic and real-world datasets from different domains. Furthermore, we adapt four static anomaly detection models to the streaming setting using a generic reconstruction strategy as baselines, and then compare them systematically with 9 existing streaming anomaly detection algorithms on 76 synthesized datasets that have various types of anomalies and concept drifts. The challenges and future research directions for anomaly detection in streaming data are also presented.

Research on University Network Data Anomaly Detection and Security Protection Algorithm Based on edge computing

Research on University Network Data Anomaly Detection and Security Protection Algorithm Based on edge computing

Quantum intrusion detection system using outlier analysis

In the field of cybersecurity, hackers often enter computer systems despite current security measures, owing to the huge amount of network traffic that makes intruder identification difficult. Differentiating between authorized traffic and abnormal data produced by Distributed Denial of Service (DDoS) attackers is still a major difficulty. This research provides a unique technique that uses Quantum Machine Learning (QML) to improve security protocols for secure communication between two parties. Our strategy enhances detection accuracy and speed by using the characteristics of quantum neural networks. The approach includes creating a dataset from network traffic patterns, preprocessing it, and then turning it into quantum bits via angle embedding. The research uses outlier analysis, min-entropy, and quantum state fidelity to distinguish between normal and abnormal data patterns. The dispersed randomness of network header data is measured using entropy, which aids in identifying security concerns. The suggested QML-based methodology outperforms conventional approaches and current models, including AMM-CNN and ANN models, with a detection accuracy of 99.87% for DDoS attacks. This advancement leads to more effective and secure communication networks.