Network Security Challenges Research Articles

Cutting-Edge Challenges in Communication Technology and Computer Network Security

The rapid development of communication technology and computer networks has brought a lot of convenience to production and life, but it also increases the security problem. Information security has become one of the severe challenges faced by people in the digital age. Currently, the security problems facing the field of communication technology and computer networks in China mainly include the evolution of offensive technology, the risk of large-scale data transmission, the potential vulnerabilities introduced by emerging technology, and the dilemma of user identity verification. This paper analyzes the frontier challenges of communication technology and computer network security, and puts forward corresponding solutions, hoping to provide ideas for coping with the security challenges of communication technology and computer networks.

Opportunities and Challenges of Intelligent Computer Network Configuration and Security with its Technologies

This paper explores the dual aspects of opportunities and challenges posed by intelligent computer network configuration and security, highlighting the role of emerging technologies while emphasizing the need for robust strategies to address the accompanying risks. Intelligent network configuration utilizes technologies like artificial intelligence (AI), machine learning (ML) and software-defined networking (SDN) to automate and optimize network management. This automation enhances efficiency, scalability and adaptability, allowing networks to respond dynamically to traffic patterns and emerging threats. AI-powered security mechanisms further bolster defenses against sophisticated cyberattacks by enabling real-time monitoring, threat detection and proactive mitigation strategies. However, alongside these opportunities come challenges. Implementing intelligent technologies requires addressing issues like system complexity, interoperability and the need for continuous updates to handle evolving cyber threats. Additionally, privacy concerns, the integration of legacy systems and ensuring compliance with regulatory standards are key hurdles in adopting these technologies

A Hybrid Approach for Efficient DDoS Detection in Network Traffic Using CBLOF-Based Feature Engineering and XGBoost

This is one of the greatest challenges in computer network security and cannot be dealt with without a set of most recent detection techniques. This paper lays down a new hybrid technique that combines Clustering-Based Local Outlier Factor (CBLOF) and Extreme Gradient Boosting (XGBoost) to enhance accuracy while detecting Distributed Denial of Service (DDoS) from network traffic. The proposed hybrid model utilizes a CBLOF for outlier detection as feature engineering. Over the detected anomalies, classification is to be done using XGBoost classification to attain the objective. The proposed hybrid model was tested extensively on CICIDS 2017 and CICIDS 2018 datasets Compared with traditional ones, the proposed model outperformed the traditional ones with an accuracy rate of 99.99%, precision of 100%, and F1 score reflecting perfection. These results confirm this model's efficiency in terms of known and novel attack patterns and introduce a further reliable framework for the timely detection of DDoS attacks. Even if it is computation-heavy, optimization could be made towards real-time large-scale data.

VAE-WACGAN: An Improved Data Augmentation Method Based on VAEGAN for Intrusion Detection.

To address the class imbalance issue in network intrusion detection, which degrades performance of intrusion detection models, this paper proposes a novel generative model called VAE-WACGAN to generate minority class samples and balance the dataset. This model extends the Variational Autoencoder Generative Adversarial Network (VAEGAN) by integrating key features from the Auxiliary Classifier Generative Adversarial Network (ACGAN) and the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). These enhancements significantly improve both the quality of generated samples and the stability of the training process. By utilizing the VAE-WACGAN model to oversample anomalous data, more realistic synthetic anomalies that closely mirror the actual network traffic distribution can be generated. This approach effectively balances the network traffic dataset and enhances the overall performance of the intrusion detection model. Experimental validation was conducted using two widely utilized intrusion detection datasets, UNSW-NB15 and CIC-IDS2017. The results demonstrate that the VAE-WACGAN method effectively enhances the performance metrics of the intrusion detection model. Furthermore, the VAE-WACGAN-based intrusion detection approach surpasses several other advanced methods, underscoring its effectiveness in tackling network security challenges.

Internet of Things, critical infrastructures, and the governance of cybersecurity in 5G network slicing

Over the last decade, cybersecurity has become a disruptive challenge driven by the shift to 5G networks. 5G is an application-agnostic general-purpose technology (GPT) characterized by its major role as multidimensional upstream technology for a large and open set of downstream Internet of Things (IoT) applications and use cases in various network industries and, more generally, within the economy as a whole. The focus of this paper is on the network economics of cybersecurity governance, considering the interplay between the recent EU cybersecurity regulatory framework, the extensive standardization efforts of international standardization organizations, and the economic incentives to tackle 5G network performance and security challenges. Based on the economic concept of 5G-based network slicing, the entrepreneurial challenges driven by the Internet of Things (IoT) are analyzed with an eye towards the complementarity between the physical side and virtual side of IoT applications. Network slicing gains particular relevance in the context of different types of logically separated, QoS-differentiated 5G bandwidth capacities, which are combined with other virtual resources such as sensor networks, data storage and processing, together with end-to-end slice security. It is shown in this paper that economic incentives for the implementation of 5G cybersecurity cannot be considered in isolation but must be viewed in combination with other performance requirements of 5G network slices. This is the driver for implementing network slicing architectures within 5G networks that feature a multitude of heterogeneous network slices with different QoS differentiation and various cybersecurity requirements.

Campus Network Traffic Prediction and Anomaly Detection Based on Deep Learning

This paper proposes an intelligent solution for network traffic prediction and anomaly detection in campus networks, addressing the increasingly severe network security challenges. The proposed approach innovatively integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) to simultaneously extract local features and capture dynamic temporal dependencies of network traffic, significantly improving prediction accuracy. Based on this, an adaptive threshold anomaly detection algorithm is designed to automatically adjust detection sensitivity according to traffic variations, achieving a better balance between accuracy and recall rates. Additionally, an anomaly visualization scheme is presented, intuitively displaying the spatiotemporal distribution of network anomalies through heatmaps, assisting administrators in decision-making. Large-scale experiments demonstrate that this approach can effectively identify various security threats such as DDoS attacks, scanning probes, and botnets, with an overall detection rate exceeding 90% while maintaining a low false positive rate. Compared to traditional statistical and machine learning methods, the proposed approach exhibits stronger adaptability and generalization capabilities, providing crucial support for building an intelligent, precise, and reliable campus network security protection system. Future work will focus on further improving the real-time performance and robustness of the solution, expanding its application in new network scenarios such as IoT and edge computing.

An improved intrusion detection method for IIoT using attention mechanisms, BiGRU, and Inception-CNN

In the field of Industrial Internet of Things (IIoT), existing intrusion detection models face challenges in three main areas: low accuracy in detecting attack traffic, feature redundancy when dealing with high-dimensional and complex attack traffic, making it difficult to capture critical information, and a tendency to favor learning common categories while neglecting rare categories when handling imbalanced data. To tackle these challenges, this study introduces an intrusion detection method that combines an attention mechanism, Bidirectional Gated Recurrent Units (BiGRU), and Inception Convolutional Neural Network (Inception-CNN) to enhance the model’s detection rate. Simultaneously, the method employs a mixed sampling strategy for data resampling to address the bias learning issue caused by data imbalance. Additionally, the method employs a hybrid sampling strategy for data resampling to address the bias learning issue caused by data imbalance. It also incorporates denoising techniques to handle potential dataset noise introduced by hybrid sampling. Furthermore, a feature selection method combining Pearson correlation coefficient and Random Forest is applied to eliminate feature redundancy, enhancing the model’s ability to capture crucial information from high-dimensional attack traffic. Experimental validation on internationally recognized datasets (Edge-IIoTset, CIC-IDS2017, and CIC IoT 2023) affirms the reliability of the proposed intrusion detection method. This approach underscores the significance of intrusion detection in the security of Industrial IoT and showcases its potential in addressing pertinent challenges in network security.

Network Security Challenges and Countermeasures for Software-Defined Smart Grids: A Survey

The rise of grid modernization has been prompted by the escalating demand for power, the deteriorating state of infrastructure, and the growing concern regarding the reliability of electric utilities. The smart grid encompasses recent advancements in electronics, technology, telecommunications, and computer capabilities. Smart grid telecommunication frameworks provide bidirectional communication to facilitate grid operations. Software-defined networking (SDN) is a proposed approach for monitoring and regulating telecommunication networks, which allows for enhanced visibility, control, and security in smart grid systems. Nevertheless, the integration of telecommunications infrastructure exposes smart grid networks to potential cyberattacks. Unauthorized individuals may exploit unauthorized access to intercept communications, introduce fabricated data into system measurements, overwhelm communication channels with false data packets, or attack centralized controllers to disable network control. An ongoing, thorough examination of cyber attacks and protection strategies for smart grid networks is essential due to the ever-changing nature of these threats. Previous surveys on smart grid security lack modern methodologies and, to the best of our knowledge, most, if not all, focus on only one sort of attack or protection. This survey examines the most recent security techniques, simultaneous multi-pronged cyber attacks, and defense utilities in order to address the challenges of future SDN smart grid research. The objective is to identify future research requirements, describe the existing security challenges, and highlight emerging threats and their potential impact on the deployment of software-defined smart grid (SD-SG).

Подход к настройке CatBoost для продвинутого обнаружения атак DoS и DDoS в сетевом трафике

In the ever-evolving landscape of network security, the sophistication of cyber-attacks, especially Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, poses a formidable challenge to intrusion detection systems. Recognizing the longstanding application of CatBoost in various domains, this study explores its novel optimization for network intrusion detection, a critical area in need of advanced solutions. Leveraging the strengths of CatBoost in handling categorical data and imbalanced datasets, we meticulously adapt the classifier to meet the complex demands of distinguishing between DoS, DDoS, and benign traffic within the comprehensive CICIDS2017 and CSE-CIC-IDS2018 datasets. This research is an attempt to refine the learning efficiency and detection capabilities of CatBoost through the implementation of advanced feature selection and data preparation, contributing to the field by improving detection accuracy within real-time intrusion detection systems. The results show a notable improvement in performance, underscoring the classifier's role in advancing cybersecurity measures. Furthermore, the study paves the way for future exploration into adversarial machine learning and automated feature engineering, fortifying the resilience and adaptability of intrusion detection systems against the backdrop of a rapidly changing cyber threat landscape. These efforts provide solid approaches to address the current challenges in network security, signaling a move towards more refined and dependable intrusion detection methods.

Research on Communication Network Security Based on Narrowband Internet of Things and Encryption Algorithms

With the rapid development of Internet of Things (IoT) technology, Narrowband IoT (NB-IoT) has been widely adopted in smart cities, smart homes, and industrial automation due to its low power consumption, wide coverage, low cost, and high reliability. However, the associated security issues have become a critical factor hindering its further development. This paper aims to explore the security challenges in NB-IoT communication networks using encryption algorithms. Through theoretical analysis and model construction, a comprehensive security solution is proposed. Initially, the paper reviews the fundamental principles and application scenarios of NB-IoT, analyzing the current challenges in network security, including data leakage, device tampering, and man-in-the-middle attacks. Subsequently, a security framework based on lightweight encryption algorithms is introduced, leveraging the strengths of both symmetric and asymmetric encryption to ensure security while reducing computational and communication overheads. Additionally, the application of blockchain technology in NB-IoT security is discussed, enhancing data credibility and system resilience against attacks through its decentralized nature. Theoretical derivations and simulation analyses confirm the effectiveness and feasibility of the proposed security framework. The results demonstrate that this framework significantly enhances the security performance of NB-IoT networks, providing theoretical support and practical guidance for the secure development of IoT.

DeepRoughNetID: A Robust Framework for Network Anomaly Intrusion Detection with High Detection Rates

Network security faces challenges, including reduced true positives, increased false positives, and inadequate anomaly detection efficacy. This paper introduces the DeepRoughNetID (DRNID) approach to address these issues and confront the increasing cyberattack threat in modern communication systems. DRNID presents an innovative framework for network intrusion detection, incorporating kNNImputer, GreedyRoughSelector, DeepVAEEnsembler, and IntrusionNet components. kNNImputer enables adaptable data preprocessing by leveraging instance-based learning, facilitating efficient handling of evolving datasets without necessitating full retraining. GreedyRoughSelector enhances classifier performance through systematic attribute selection, focusing on relevant features while eliminating redundancies. DeepVAEEnsembler leverages Variational Autoencoders (VAEs) to learn underlying data distributions, enabling robust anomaly detection. IntrusionNet utilizes VAEs to classify intrusions, providing a comprehensive solution to network security challenges. By integrating these components, DRNID offers a refined approach that promises significant advancements in network intrusion detection. The experimental results demonstrate the effectiveness of DRNID, with an impressive accuracy of 98.8% achieved through 10-fold cross-validation. DRNID exhibits superior performance compared to existing methods across various datasets, showcasing its robust anomaly detection capabilities. This methodology empowers organizations to proactively prevent security violations and safeguard sensitive data from malicious entities. Beyond its theoretical contributions, our research carries tangible implications for strengthening cybersecurity defenses across diverse sectors, including telecommunications, finance, and healthcare.

Enhanced Network Security Protection through Data Analysis and Machine Learning: An Application of GraphSAGE for Anomaly Detection and Operational Intelligence

With the Internet's rapid expansion, network security challenges have become increasingly complex and prominent. Traditional protection methods, largely dependent on predefined rules and patterns, demonstrate limited effectiveness against sophisticated and unknown network attacks, failing to harness the full potential of extensive network data. This study addresses the challenges faced by modern cybersecurity, particularly the limitations of traditional defense methods in countering unknown and complex attacks, by proposing a solution that integrates data analysis and machine learning technologies. The focus of this research is placed on network security anomaly detection as well as on intelligent network operations and maintenance exception management based on graph network algorithms, aiming to enhance security defense capabilities and operational efficiency. Specifically, the main contributions and innovations of this paper include: 1. Innovations in sampling, aggregation, and loss functions within the Graph Sample and Aggregation (GraphSAGE) model to improve the accuracy and robustness of the model for network anomaly detection; 2. The introduction of a novel network anomaly root cause analysis and localization model, which, combined with an optimized root cause likelihood assessment method and search scheme, significantly enhances the speed and accuracy of anomaly localization; 3. The design of an integrated decision support system that can automatically adjust protection strategies as network conditions change, achieving a high level of automation and intelligence in cybersecurity management. This work not only provides effective technical support for network security protection but also opens new avenues for future cybersecurity research.

Detecting and mitigating security anomalies in Software-Defined Networking (SDN) using Gradient-Boosted Trees and Floodlight Controller characteristics

Cutting-edge and innovative software solutions are provided to address network security, network virtualization, and other network-related challenges in highly congested SDN-powered networks. However, these networks are susceptible to the same security issues as traditional networks. For instance, SDNs are significantly vulnerable to distributed denial of service (DDoS) attacks. Previous studies have suggested various anomaly detection techniques based on machine learning, statistical analysis, or entropy measurement to combat DDoS attacks and other security threats in SDN networks. However, these techniques face challenges such as collecting sufficient and relevant flow data, extracting and selecting the most informative features, and choosing the best model for identifying and preventing anomalies. This paper introduces a new and advanced multi-stage modular approach for anomaly detection and mitigation in SDN networks. The approach consists of four modules: data collection, feature selection, anomaly classification, and anomaly response. The approach utilizes the NetFlow standard to gather data and generate a dataset, employs the Information Gain Ratio (IGR) to select the most valuable features, uses gradient-boosted trees (GBT), and leverages Representational State Transfer Application Programming Interfaces (REST API) and Static Entry Pusher within the floodlight controller to construct an exceptionally efficient structure for detecting and mitigating anomalies in SDN design. We conducted experiments on a synthetic dataset containing 15 types of anomalies, such as DDoS attacks, port scans, worms, etc. We compared our model with four existing techniques: SVM, KNN, DT, and RF. Experimental results demonstrate that our model outperforms the existing techniques in terms of enhancing Accuracy (AC) and Detection Rate (DR) while simultaneously reducing Classification Error (CE) and False Alarm Rate (FAR) to 98.80 %, 97.44 %, 1.2 %, and 0.38 %, respectively.

Research on Huawei's Digital Transformation and Market Adaptation in the Post-epidemic Era

With the spread of global epidemic, Huawei, as a leading enterprise in the field of information and communication technology, is facing many challenges, such as supply chain uncertainty, market demand changes, network security challenges and so on. This paper makes an in-depth analysis of Huawei's response measures in product innovation, marketing strategy adjustment, supply chain management, employee security and so on, and summarizes Huawei's achievements and experiences in digital transformation and market adaptation. It is found that Huawei has successfully achieved stable development during the epidemic period by increasing investment in key technical fields, flexibly adjusting marketing strategies, optimizing supply chain management, and paying attention to the health and safety of employees. However, with the advent of the post-epidemic era, it is still necessary to further strengthen the innovation ability and enhance the level of digital transformation in order to better meet the market demand and maintain the competitive advantage. The purpose of this study is to provide reference and enlightenment for other enterprises and promote the sustainable development and transformation and upgrading of the industry.

Challenges and Solutions in Network Security for Serverless Computing

This research study explores the challenges and solutions related to serverless computing so that the computer systems connected to the network can be protected. Serverless computing can be defined as a method of managing computer services without the need to have fixed servers. The qualitative research method is used by this research study, which does not include any numerical data and involves the examination of non-number data so that the network security challenges can be identified in detail. In the literature review, the past studies from 2019 to 2023 are reviewed to identify study gaps so that the foundation for investigating serverless network security. The literature review is based on thematic analysis, so all the data can be organized into meaningful themes. The findings of this research study include the solutions to the challenges like data privacy, insecure dependencies and limited control. The strategies to overcome these challenges include encryption, strong monitoring and other relevant strategies. This research study also suggests the use of blockchain technology and Artificial Intelligence. In short, this research study provides insights to improve serverless computing security and also guides future researchers to innovate creative solutions for developing security challenges.

Artificial Intelligence: Real Challenge or Boon for Network Operation Center and Network security

Artificial intelligence has emerged as a game-changer in every domain, and network operation center and network security are no exception. This paper critically evaluates the dual impact of AI as a daunting challenge and a blissful boon in the context of NOCs, network security, and their overlap. As firms increasingly rely on ever more sophisticated and complex networks to run their business, the need for effective network management and security has become a top priority. Correspondingly, controlling such complex networks, an analyzing their performance and status, and safeguarding them from rapidly evolving threats have grown more difficult than ever. AI-powered solutions have the potential to considerably improve NOC efficiency and effectiveness. Introduce automated automation of routine processes, facilitate network performance optimization, and help in forecasting of most network abnormalities. Consequently, these can drastically cut NOC operational costs and network uninsured losses and enhance network dependability. However, and exposure of AI technologies to an entire new category of challenges, such as the potential of AI-based cyberattacks, algorithmic discrimination, and the ethical insinuations of fully autonomous NOCs, also imply that many possible risks and risks are taking into account.

The Role of Mining and Detection of Big Data Processing Techniques in Cybersecurity

Abstract The need for advanced detection methods has become more critical in light of the increasing prevalence of network security incidents. This study proposes a novel approach to network security detection using a fuzzy data mining algorithm, addressing the rising challenges in big data processing and network security. The paper outlines the evolution of big data analytics by exploring the integration of network security detection, data mining, and structural feature analysis. Data for this research was collected using a sniffer device and underwent extensive preprocessing to ensure diversity and applicability. To overcome the limitations of traditional data mining, such as the issue of sharp boundaries, this method combines fuzzy logic with data mining techniques, enhancing conventional network security protocols. Simulation experiments demonstrate the efficacy of this fuzzy mining-based approach, with results showing 987,238 predicted positive cases, 93,951 of which were accurate. The method achieves an impressive 93.65% accuracy and 92.55% recall rate, proving its capability to promptly identify and mitigate suspicious network activities.

Comprehensive Analysis of Implementation and Evaluation IoT based Techniques in Networked Security Systems

This research introduces an advanced network security methodology based on IoT, combining five innovative algorithms: Dynamic Threat Detection (DTD), Adaptive Intrusion Prevention System (AIPS), Anomaly-Based Security Metrics (ABSM), Context-Aware Firewall (CAF), and Cognitive Security Assessment (CSA). Each algorithm contributes specific functionalities, ranging from real-time threat detection and adaptive policy adjustments to anomaly quantification, contextual rule modifications, and holistic security risk assessments. The ablation study conducted on each algorithm reveals critical components driving their performance, ensuring a deep understanding of their inner workings. The proposed method demonstrates superior performance in accuracy, scalability, usability, and adaptability compared to existing network security methods. Visual representations and a comprehensive evaluation further validate the proposed method's effectiveness, positioning it as an advanced and efficient solution for addressing evolving network security challenges.

Encountering social engineering activities with a novel honeypot mechanism

<span lang="EN-US">Communication and conducting businesses have eventually transformed to be performed through information and communication technology (ICT). While computer network security challenges have become increasingly significant, the world is facing a new era of crimes that can be conducted easily, quickly, and, on top of all, anonymously. Because system penetration is primarily dependent on human psychology and awareness, 80% of network cyberattacks use some form of social engineering tactics to deceive the target, exposing systems at risk, regardless of the security system's robustness. This study highlights the significance of technological solutions in making users more safe and secure. Throughout this paper, a novel approach to detecting and preventing social engineering attacks will be proposed, combining multiple security systems, and utilizing the concept of Honeypots to provide an automated prevention mechanism employing artificial intelligence (AI). This study aims to merge AI and honeypot with intrusion prevention system (IPS) to detect social engineering attacks, threaten the attacker, and restrict his session to keep users away from these manipulation tactics.</span>

Analysis and Countermeasures of Computer Network Security in the Age of Artificial Intelligence

With the widespread adoption of artificial intelligence technology, the landscape of network threats is continually evolving. Malicious attackers are increasingly leveraging intelligent technology to enhance their attacks, resulting in more intricate network security challenges. Among these challenges, antagonistic attacks and intelligent threats stand out, while the risk of large-scale data breaches and privacy infringements looms over both individuals and organizations. This paper proposes a set of strategies to address these pressing issues. These strategies encompass the deployment of deep learning technology, safeguarding data privacy, the dissemination of automation solutions, and the advancement of network user education. Furthermore, the importance of research into antagonistic attacks and emerging technologies, as well as the significance of international cooperation and information sharing, are underscored to ensure the resilience of network security in the face of evolving threats. This comprehensive approach serves as a valuable resource for safeguarding the information security of network users and fostering the prosperity and sustainable development of our digital society.