Network Security Solutions Research Articles

Securing Real-Time Payment Systems: Challenges and Solutions for Network Security in Banking

Modern banking infrastructure relies heavily on real-time payment technologies, which have revolutionized how financial institutions handle transactions worldwide. This in-depth article examines the operational needs, security procedures, and technical difficulties of setting up and managing real-time payment networks. The article examines advanced fraud prevention techniques, such as rule-based systems and machine learning applications, while examining the crucial trade-off between transaction speed and security. This article illustrates the development of payment infrastructure and security measures through in-depth case studies of European and Asian payment networks. To help financial institutions, regulators, and technology suppliers navigate this quickly changing industry, the essay delves deeper into new technologies, regulatory factors, and upcoming advancements influencing the real-time payments market.

Enhancing Security in Low-power Wide-area (LPWA) IoT Environments: The Role of HSM, Tamper-proof Technology, and Quantum Cryptography

Low-power wide-area (LPWA) networks are integral to expanding Internet of Things (IoT) applications, offering extensive coverage with low power consumption. However, these networks face significant security challenges due to their widespread deployment and inherent constraints. In order to provide secure services in an LPWA IoT environment, important information stored in IoT devices (encryption keys, device unique numbers, etc.) must be safely protected from external hacking or theft by physical access, and it is necessary to develop tamper-proof technology to enhance physical security. Meanwhile, with so many ruggedized IoT devices processing and transmitting sensitive information, security systems are essential to protect the integrity and privacy of IoT data. This paper explores the critical role of hardware security modules (HSMs), tamper-proof technology, and quantum cryptography in enhancing the physical, network, and data security of LPWA IoT environments. We propose operational strategies for HSMs, tamper-proof technology in ruggedized LPWA IoT settings, and a quantum key distribution (QKD)-based IPsec solution for robust network and data security.

Adaptive Security Solutions for NOMA Networks: The Role of DDPG and RIS-Equipped UAVs

Adaptive Security Solutions for NOMA Networks: The Role of DDPG and RIS-Equipped UAVs

The Use of PFSense and Suricata as a Network Security Attack Detection and Prevention Tool on Web servers

This research explores the effectiveness of PFSense and Suricata integration in detecting and preventing network security attacks on web servers. The experimental method was conducted by testing the performance of these two open-source tools in the face of four types of attacks: Ping of Death, SYN Flood, SQL Injection, and Brute Force Attack on SSH. The tests were conducted in an environment with hardware specifications such as [specify specifications, e.g. CPU, RAM, and device type], and software including [specify operating system and version of PFSense and Suricata]. The results showed that Suricata was able to detect threats with an accuracy rate of 92% and successfully blocked 85% of the attacks. The average response time of the system to detect attacks was 250 ms. The integration between PFSense and Suricata proved effective in identifying attack patterns and preventing further potential damage. With proper configuration, this combination not only keeps the web server network secure, but also provides a quick response to complex cyber threats. This research contributes to the development of more reliable network security solutions by demonstrating how the integration of PFSense and Suricata can be effectively used to protect web servers. The findings provide practical guidance for practitioners and academics in implementing more innovative approaches to enhance network security in the digital era.

Data Mining Approach for Evil Twin Attack Identification in Wi-Fi Networks

Recent cyber security solutions for wireless networks during internet open access have become critically important for personal data security. The newest WPA3 network security protocol has been used to maximize this protection; however, attackers can use an Evil Twin attack to replace a legitimate access point. The article is devoted to solving the problem of intrusion detection at the OSI model’s physical layers. To solve this, a hardware–software complex has been developed to collect information about the signal strength from Wi-Fi access points using wireless sensor networks. The collected data were supplemented with a generative algorithm considering all possible combinations of signal strength. The k-nearest neighbor model was trained on the obtained data to distinguish the signal strength of legitimate from illegitimate access points. To verify the authenticity of the data, an Evil Twin attack was physically simulated, and a machine learning model analyzed the data from the sensors. As a result, the Evil Twin attack was successfully identified based on the signal strength in the radio spectrum. The proposed model can be used in open access points as well as in large corporate and home Wi-Fi networks to detect intrusions aimed at substituting devices in the radio spectrum where IEEE 802.11 networking equipment operates.

A comprehensive review of combating EDoS attacks in cloud services with deep learning and advanced network security technologies including DDoS protection and intrusion prevention systems

This comprehensive review examines the current state of research and practice in combating Economic Denial of Sustainability (EDoS) attacks in cloud services, with a focus on deep learning approaches and advanced network security technologies. The paper provides an in-depth analysis of EDoS attack characteristics, their impact on cloud economics, and the challenges faced in mitigation efforts. It explores the application of deep learning techniques in EDoS detection and prevention, highlighting recent advancements in neural network architectures and feature extraction methods. The review also covers the integration of advanced network security technologies, including next-generation firewalls, software-defined networking, and cloud-native security solutions, in the context of EDoS protection. Furthermore, it discusses the adaptation of Distributed Denial of Service (DDoS) mitigation strategies for EDoS attacks, emphasizing traffic analysis and anomaly detection techniques. The role of Intrusion Prevention Systems (IPS) in EDoS mitigation is examined, comparing signature-based and behavior-based approaches and exploring their integration with other security components. The paper concludes by identifying emerging threats, regulatory considerations, and open research problems in EDoS protection, providing valuable insights for researchers and practitioners in the field of cloud security. This review aims to serve as a comprehensive resource for understanding the current landscape of EDoS attacks and defense mechanisms, while also highlighting future directions for research and development in this critical area of cloud computing security.

Deep Packet Inspection Model Based on Support Vector Machine for Anomaly Detection in Local Area Networks

Deep packet inspection is a network security solution that identifies and flags anomalous network traffic patterns in a local network environment. Traditional signature-based techniques for intrusion detection are limited in identifying different attacks or completely new kinds, which makes them unsuitable in some situations. In addition, most previous methods for anomaly detection have low detection rate and high false alarm. In this study, a deep packet inspection model based on support vector machine (SVM) for anomaly detection in local area networks was proposed. The proposed method combined the SelectKBest method and SVM for the categorization of anomaly in a local network environment. Results showed that the proposed method outperformed other related machine learning methods with accuracy, precision, recall, and F1-score of 94.81%, 94.03%, 94.13%, and 94.0799%, respectively. The accuracy result shows that most network traffic can be correctly identified by the SVM using the SelectKBest approach, with minimal false positives or negatives.

Prioritizing cybersecurity initiatives in aviation: A dematel-QSFS methodology

The aviation industry's growing dependence on digital technologies necessitates robust cybersecurity measures to counter advanced threats. This study integrates the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method with Quantum Spherical Fuzzy Sets (QSFS) to enable precise and reliable decision-making under uncertainty. Key criteria, identified through expert evaluations, include Threat Detection Systems (TDS), Data Encryption Protocols (DEP), Regulatory Compliance (RC), Incident Response Plans (IRP), User Training (UT), Access Control Mechanisms (ACM), and Network Security Solutions (NSS). Analysis using the proposed method revealed that “Regulatory Compliance” and “Threat Detection Systems” are the most influential factors, emphasizing the need for strict adherence to standards and advanced threat detection capabilities. Additionally, the significance of “User Training” and “Data Encryption Protocols” underscores the importance of comprehensive training programs and strong encryption measures. By incorporating strategic management theories such as the Resource-Based View (RBV), Contingency Theory, and Risk Management Theory, this study presents a strategic framework to assist aviation organizations, policymakers, and researchers in developing effective cybersecurity strategies, ensuring the safety and security of global air travel.

JAPPI: An unsupervised endpoint application identification methodology for improved Zero Trust models, risk score calculations and threat detection

The surge in global digitalization triggered by COVID-19 has led to a significant increase in internet traffic and has precipitated a rapid transformation of the network security landscape. Despite being increasingly difficult, accurate traffic inspection is vital for ensuring productivity while reliably protecting internal assets. Endpoint application identification enables high accuracy inspection and detection by providing network security solutions with specific context on individual connections. However, achieving it in real-time with standard fingerprinting methods based only on client-side traffic has proven to be a challenging problem with no comprehensive solution thus far. In this article, we present a new methodology for identifying endpoint applications from network traffic, utilizing machine learning. Our methodology leverages similarities in the pre-hash string of the JA3 algorithm for fingerprinting application specific TLS Client Hello messages. By utilizing well-known clustering algorithms it is possible to identify the underlying TLS libraries and the application from the traffic remarkably better than with simple string-based matching. Our model can categorize 99,5% of the traffic in a controlled network, and 93,8% in an uncontrolled network, compared to 0,1% and 0,2% using simple string matching. Our methodology is especially effective for enhancing Zero Trust models, calculating a risk score for network events, and improving threat detection accuracy in network security solutions.

Pioneering advanced security solutions for reinforcement learning-based adaptive key rotation in Zigbee networks

In the rapidly evolving landscape of Internet of Things (IoT), Zigbee networks have emerged as a critical component for enabling wireless communication in a variety of applications. Despite their widespread adoption, Zigbee networks face significant security challenges, particularly in key management and network resilience against cyber attacks like distributed denial of service (DDoS). Traditional key rotation strategies often fall short in dynamically adapting to the ever-changing network conditions, leading to vulnerabilities in network security and efficiency. To address these challenges, this paper proposes a novel approach by implementing a reinforcement learning (RL) model for adaptive key rotation in Zigbee networks. We developed and tested this model against traditional periodic, anomaly detection-based, heuristic-based, and static key rotation methods in a simulated Zigbee network environment. Our comprehensive evaluation over a 30-day period focused on key performance metrics such as network efficiency, response to DDoS attacks, network resilience under various simulated attacks, latency, and packet loss in fluctuating traffic conditions. The results indicate that the RL model significantly outperforms traditional methods, demonstrating improved network efficiency, higher intrusion detection rates, faster response times, and superior resource management. The study underscores the potential of using artificial intelligence (AI)-driven, adaptive strategies for enhancing network security in IoT environments, paving the way for more robust and intelligent Zigbee network security solutions.

A novel approach of botnet detection using hybrid deep learning for enhancing security in IoT networks

In an era dominated by the Internet of Things (IoT), protecting interconnected devices from botnets has become essential. This study introduces an innovative hybrid deep learning model that synergizes LSTM Auto Encoders and Multilayer Perceptrons in detecting botnets in IoTs. The fusion of these technologies facilitates the analysis of sequential data and pattern recognition, enabling the model to detect intricate botnet activities within IoT networks. The proposed model's performance was carefully evaluated on two large IoT traffic datasets, N-BAIoT2018 and UNSW-NB15, where it demonstrated exceptional accuracy of 99.77 % and 99.67 % respectively for botnet detection. These results not only demonstrate the model's superior performance over existing botnet detection systems but also highlight its potential as a robust solution for IoT network security.

FLOLSTM: Fuzzy logic‐driven optimized LSTM for improved malicious traffic detection in hypervisor environments

SummaryIn the ever‐evolving realm of cloud computing, the challenge of intrusion detection has grown increasingly intricate and vital. With the proliferation of cyber‐attacks and the widespread use of virtualized environments, there is a pressing need for network security solutions that are not only innovative and robust but also easily comprehensible. These solutions must possess the ability to effectively detect malicious activities, provide visibility into network operations, adapt to changing requirements, and promptly alert stakeholders to any suspicious behavior. In this study, we introduce a groundbreaking approach known as fuzzy logic‐driven optimized long short‐term memory (LSTM)—FLOLSTM, specifically designed for hypervisor‐based environments. By integrating fuzzy logic with an optimized LSTM neural network, FLOLSTM aims to significantly enhance the detection of attack traffic within hypervisor networks by combining the interpretability and uncertainty management capabilities of fuzzy logic with the temporal pattern recognition prowess of LSTM. The research methodology involves meticulous data collection from hypervisor monitors, followed by rigorous cleaning and preprocessing to enhance data reliability. Subsequently, the preprocessed data is input into the FLOLSTM classifier to identify malicious activities. Furthermore, the performance of the LSTM is fine‐tuned using the waterwheel plant optimization (WPO) algorithm. Experimental evaluations compare the efficiency of the proposed FLOLSTM with existing techniques across various metrics including accuracy, recall, precision, F‐measure, specificity, false‐positive rate (FPR), and false‐negative rate (FNR). Overall, the FLOLSTM model represents a significant advancement in intrusion detection for cloud environments, offering a potent blend of interpretability, accuracy, and efficiency. Its superior performance underscores its capacity to enhance network security and effectively mitigate cyber threats in dynamic and virtualized settings, thereby making a substantial impact on the field of cyber security.

Exploration on human resource management and prediction model of data-driven information security in Internet of Things

The advent of the Internet of Things (IoT) has accelerated the pace of economic development across all sectors. However, it has also brought significant challenges to traditional human resource management, revealing an increasing number of problems and making it unable to meet the needs of contemporary enterprise management. The IoT has brought numerous conveniences to human society, but it has also led to security issues in communication networks. To ensure the security of these networks, it is necessary to integrate data-driven technologies to address this issue. In response to the current state of human resource management, this paper proposes the application of IoT technology in enterprise human resource management and combines it with radial basis function neural networks to construct a model for predicting enterprise human resource needs. The model was also experimentally analyzed. The results show that under this algorithm, the average prediction accuracy for the number of employees over five years is 90.2 %, and the average prediction accuracy for sales revenue is 93.9 %. These data indicate that the prediction accuracy of the model under this study's algorithm has significantly improved. This paper also conducted evaluation experiments on a wireless communication network security risk prediction model. The average prediction accuracy of four tests is 91.21 %, indicating that the model has high prediction accuracy. By introducing data-driven technology and IoT applications, this study provides new solutions for human resource management and communication network security, promoting technological innovation in the fields of traditional human resource management and information security management. The research not only improves the accuracy of the prediction models but also provides strong support for decision-making and risk management in related fields, demonstrating the great potential of big data and artificial intelligence technology in the future of enterprise management and security.

A Review and Comparative Analysis of Relevant Approaches of Zero Trust Network Model.

The Zero Trust safety architecture emerged as an intriguing approach for overcoming the shortcomings of standard network security solutions. This extensive survey study provides a meticulous explanation of the underlying principles of Zero Trust, as well as an assessment of the many strategies and possibilities for effective implementation. The survey begins by examining the role of authentication and access control within Zero Trust Architectures, and subsequently investigates innovative authentication, as well as access control solutions across different scenarios. It more deeply explores traditional techniques for encryption, micro-segmentation, and security automation, emphasizing their importance in achieving a secure Zero Trust environment. Zero Trust Architecture is explained in brief, along with the Taxonomy of Zero Trust Network Features. This review article provides useful insights into the Zero Trust paradigm, its approaches, problems, and future research objectives for scholars, practitioners, and policymakers. This survey contributes to the growth and implementation of secure network architectures in critical infrastructures by developing a deeper knowledge of Zero Trust.

China's Universities Network Security Current Situation and Solutions

With the development of new technologies such as the Internet, artificial intelligence and big data, more convenience has been brought to Chinese colleges and universities, but it is followed by threats in network security. Due to the maturity of big data algorithms, it is easy to leak personal information and cause campus network security accidents while bringing convenience to college students. China's university network has experienced great development, but the current situation is not optimistic. There are many problems such as not paying attention to the ideology, heavy construction and light application. This paper focuses on the system construction, safety education and professional construction of these problems.

Research on Substation Network Security Situational Awareness Strategy and Equipment Remote Operation and Maintenance

Abstract Substation network security is the key to maintaining the stable operation of power systems. In the face of growing threats of network attacks, traditional security protection measures have been brutal to meet the needs of modern power systems. Research on substation network security, situational awareness strategies, and remote operation and maintenance of equipment is essential to improve network defense capability and ensure the continuity and reliability of power supply. This study explores effective security situational awareness methods and remote operation and maintenance techniques to provide new solutions for substation network security. This paper builds an efficient network attack detection model by introducing linear discriminant analysis (LDA) and radial basis function (RBF) neural networks. The experiment uses the KDD Cup99 dataset, which is preprocessed to provide the model training and testing data. The LDA-RBF model in this paper outperforms the traditional RNF neural and BP neural networks regarding recognition rate. Specifically, the recognition rate reaches 90.2% for the Smurf attack and 100% for the Ipsweep attack. The proposed model of the study also performs well in terms of leakage and false alarm rates, with an overall recognition rate of 97.00%. This study proposes a network security situational awareness strategy and equipment remote operation and maintenance method that can effectively enhance substation networks’ security and operation and maintenance efficiency.

Research on the network security protection architecture and solutions for distributed power dispatch automation systems

To enhance the network security of distributed power dispatching and control systems in the context of the “30·60” dual carbon goals, this study focuses on typical architectures for business networking of distributed power interface devices. We propose a layered and graded network security protection architecture, informed by in-depth examination of leading manufacturers’ architectures. This architecture considers communication patterns, horizontal and vertical data flow paths, and grid security planning. It culminates in a grid-like security protection framework. The research aims to bolster network security for distributed power systems, ensuring stable operation. This effort contributes significantly to the sustainable development of the power industry.

Network intrusion detection system by learning jointly from tabular and text‐based features

AbstractNetwork intrusion detection systems (NIDS) play a critical role in maintaining the security and integrity of computer networks. These systems are designed to detect and respond to anomalous activities that may indicate malicious intent or unauthorized access. The need for robust NIDS solutions has never been more pressing in today's digital landscape, characterized by constantly evolving cyber threats. Deploying effective NIDS can be challenging, particularly in accurately identifying network anomalies amid the ever‐increasing sophisticated and difficult‐to‐detect cyber threats. The motivation for our research stems from the recognition that while NIDS studies have made significant strides, there remains a crucial need for more effective and accurate methods to detect network anomalies. Commonly used features in NIDS studies include network logs, with some studies exploring text‐based features such as payload. However, traditional machine and deep learning models may need to be improved in learning jointly from tabular and text‐based features. Here, we present a new approach that integrates both tabular and text‐based features to improve the performance of NIDS. Our research aims to address the existing limitations of NIDS and contribute to the development of more reliable and efficient network security solutions by introducing more effective and accurate methods for detecting network anomalies. Our internal experiments have revealed that the deep learning approach utilizing tabular features produces favourable results, whereas the pre‐trained transformer approach needs to perform sufficiently. Hence, our proposed approach, which integrates both feature types using deep learning and pre‐trained transformer approaches, achieves superior performance. These findings indicate that integrating both feature types using deep learning and pre‐trained transformer approaches can significantly improve the accuracy of network anomaly detection. Moreover, our proposed approach outperforms the state‐of‐the‐art methods in terms of accuracy, F1‐score, and recall on commonly used NIDS datasets consisting of ISCX‐IDS2012, UNSW‐NB15, and CIC‐IDS2017, with F1‐scores of 99.80%, 92.37%, and 99.69%, respectively, indicating its effectiveness in detecting network anomalies.

Neuroguard:Unveiling the Strength of Lightfooted Anomaly Detection with Swift-Net Neural Networks in Countering Network Threats

In the dominion of cybersecurity, the prime tasks revolve around recognizing and moderating network breaches. This research paper impacts the widely recognized CICIDS2017 dataset to conduct a complete evaluation and comparison of numerous deep learning and machine learning representations designed for Anomaly-detection by the analysis of a diverse array of algorithms, spanning from traditional methodologies like logistic regression to more modern advances such as K-Nearest Neighbors (KNN) and state-of-the-art Swift-Net neural networks. The research also delves into the realism of employing dimensionality reduction and feature selection procedures, remarkably Principal Component Analysis (PCA) in addition with Gaussian Mixture Models (GMM). The implications of this consideration are substantial for the enhancement of network security with an emphasis of the efficiency of PCA and GMM in facilitating data visualization, enabling a deeper understanding of network behavior. Moreover, the paper highlights the potential of Swift-Net for real-time threat detection, signifying its relevance in the evolving cybersecurity environment. As the cybersecurity domain undergoes constant transformation, this research serves as a valuable reserve, paving the way for more effective Anomaly detection techniques and the employment of efficient network security solutions. These outcomes offer acute insights to reinforce network safety.

Let model keep evolving: Incremental learning for encrypted traffic classification

Encrypted Traffic Classification (ETC) is valuable for many network management and security solutions as it provides insights into applications active on the network. However, the network environment constantly evolves, and new applications emerge in an endless stream daily, which gradually makes well-trained ETC models ineffective. The conventional approach to adapting new applications is to re-train the models on a re-formed dataset with both pre-existing and new application samples. The major limitation is that requiring redundant computing resources and sufficient storage spaces. In this work, we propose an Incremental Learning (IL) framework based on multi-view sequences fusion, MISS, to keep ETC models evolving with new applications. The key novelty of MISS is three-fold: extract cross-view information from multi-view sequences to capture sufficient knowledge; propose an exemplar selection algorithm from communication patterns to reduce redundant consumption; design a pair of branches from the learnability of parameters to mitigate accuracy loss during evolution. MISS outperforms the existing IL methods of ETC, and the state-of-the-art ETC models using the classic IL framework, on the real-world network traffic datasets, which achieves satisfactory improvements of 11.37%↑ and 1.58%↑. Furthermore, we comprehensively perform incremental experiments to evaluate the evolution ability of MISS, which is able to select representative exemplars of old applications, counteract the adverse effects of homogeneous applications, and keep evolving with unknown applications.