Network Security Research Articles

Examining The VTP Protocol, Identifying the Risks of Its Use and Providing Risk Reduction Solutions in The Computer Networks of Offices and Large Organizations

Today, one of the challenges of computer networks implemented in large offices and organizations is maintaining the stability and security of networks, as well as the ease and automation of their configuration to save the time and energy of network administrators. The VTP protocol is a tool to ease and reduce the burden of manually configuring changes to VLANs on Cisco switches. However, the use of this protocol, despite having advantages such as automatic configuration and centralized management of VLANs, has great risks such as the complexity of implementation in large networks and the loss of settings in case of wrong configuration. The problem studied in this article is to investigate the characteristics and function of this protocol and to recognize the possible risks and dangers associated with its use. In the following, solutions are provided to reduce these risks so that the benefits of this protocol can be used and at the same time protect the stability and security of the network implemented in the organization. By using this evaluation, network administrators can have an optimal and more informed use of this protocol.

Enhancing network security using unsupervised learning approach to combat zero-day attack

Enhancing network security using unsupervised learning approach to combat zero-day attack

Securing DNS over HTTPS traffic: a real-time analysis tool

DNS over HTTPS (DoH) is a developing protocol that uses encryption to secure domain name system (DNS) queries within hypertext transfer protocol secure (HTTPS) connections, thereby improving privacy and security while browsing the web. This study involved the development of a live tool that captures and analyzes DoH traffic in order to classify it as either benign or malicious. We employed machine learning (ML) algorithms such as K-Nearest Neighbors (K-NN), random forest (RF), decision tree (DT), deep neural network (DNN), and support vector machine (SVM) to categorize the data. All of the algorithms, namely KNN, RF, and DT, achieved exceptional performance, with F1 scores of 1.0 or above for both precision and recall. The SVM and DNN both achieved exceptionally high scores, with only slight differences in accuracy. This tool employs a voting mechanism to arrive at a definitive classification decision. By integrating with the Mallory tool, it becomes possible to locally resolve DNS, which in turn allows for more accurate simulation of DoH queries. The evaluation results clearly indicate outstanding performance, confirming the tool's effectiveness in analyzing DoH traffic for network security and threat detection purposes.

Harnessing AI for Network Security and DDoS Attack Detection

This paper investigates the utilization of artificial intelligence (AI) techniques for network security and detection of Distributed Denial of Service (DDoS) attacks. It explores the role of AI algorithms, machine learning models, and anomaly detection techniques in enhancing network security, mitigating cyber threats, and improving incident response in the face of DDoS attacks. The paper discusses real-world implementations and case studies showcasing the effectiveness of AI in network security and DDoS attack detection.

Wireless Network Security Prediction Using Machine Learning and Graph Neural Networks

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

Metaheuristic optimized complex-valued dilated recurrent neural network for attack detection in internet of vehicular communications

The Internet of Vehicles (IoV) is a specialized iteration of the Internet of Things (IoT) tailored to facilitate communication and connectivity among vehicles and their environment. It harnesses the power of advanced technologies such as cloud computing, wireless communication, and data analytics to seamlessly exchange real-time data among vehicles, road-side infrastructure, traffic management systems, and other entities. The primary objectives of this real-time data exchange include enhancing road safety, reducing traffic congestion, boosting traffic flow efficiency, and enriching the driving experience. Through the IoV, vehicles can share information about traffic conditions, weather forecasts, road hazards, and other relevant data, fostering smarter, safer, and more efficient transportation networks. Developing, implementing and maintaining sophisticated techniques for detecting attacks present significant challenges and costs, which might limit their deployment, especially in smaller settings or those with constrained resources. To overcome these drawbacks, this article outlines developing an innovative attack detection model for the IoV using advanced deep learning techniques. The model aims to enhance security in vehicular networks by efficiently identifying attacks. Initially, data is collected from online databases and subjected to an optimal feature extraction process. During this phase, the Enhanced Exploitation in Hybrid Leader-based Optimization (EEHLO) method is employed to select the optimal features. These features are utilized by a Complex-Valued Dilated Recurrent Neural Network (CV-DRNN) to detect attacks within vehicle networks accurately. The performance of this novel attack detection model is rigorously evaluated and compared with that of traditional models using a variety of metrics.

Attachment, Security, and Relational Networks

Attachment, Security, and Relational Networks

Experiences With mHealth Use Among Patient-Caregiver Dyads With Chronic Heart Failure: Qualitative Study.

Chronic heart failure has become a serious threat to the health of the global population, and self-management is key to treating chronic heart failure. The emergence of mobile health (mHealth) provides new ideas for the self-management of chronic heart failure in which the informal caregiver plays an important role. Current research has mainly studied the experiences with using mHealth among patients with chronic heart failure from the perspective of individual patients, and there is a lack of research from the dichotomous perspective. The aim of this study was to explore the experiences with mHealth use among patients with chronic heart failure and their informal caregivers from a dichotomous perspective. This descriptive phenomenological study from a post-positivist perspective used a dyadic interview method, and face-to-face semistructured interviews were conducted with patients with chronic heart failure and their informal caregivers. Data were collected and managed using NVivo 12 software, and data analysis used thematic analysis to identify and interpret participants' experiences and perspectives. The thematic analysis included familiarizing ourselves with the data, generating initial codes, searching for themes, reviewing themes, defining and naming themes, and producing the report. A total of 14 dyads of patients with chronic heart failure and their informal caregivers (13 men and 15 women) participated in this study, including 3 couples and 11 parent-child pairs. We constructed 4 key themes and their subthemes related to the experiences with mHealth use: (1) opposing experiences with mHealth as human interaction or trauma (great experience with mHealth use; trauma), (2) supplement instead of replacement (it is useful but better as a reference; offline is unavoidable sometimes), (3) both agreement and disagreement over who should be the adopter of mHealth (achieving consensus regarding who should adopt mHealth; conflict occurs when considering patients as the adopter of mHealth), (4) for better mHealth (applying mHealth with caution; suggestions for improved mHealth). This study reported that the experiences with mHealth use among patients with chronic heart failure and their informal caregivers were mixed, and it highlighted the human touch of mHealth and the importance of network security. These results featured mHealth as a complement to offline hospitals rather than a replacement. In the context of modern or changing Chinese culture, we encourage patients to use mHealth by themselves and their informal caregivers to provide help when necessary. In addition, we need to use mHealth carefully, and future mHealth designs should focus more on ease of use and be oriented more toward older adults.

Deep learning approaches for protecting IoT devices in smart homes from MitM attacks

The primary objective of this paper is to enhance the security of IoT devices in Software-Defined Networking (SDN) environments against Man-in-the-Middle (MitM) attacks in smart homes using Artificial Intelligence (AI) methods as part of an Intrusion Detection and Prevention System (IDPS) framework. This framework aims to authenticate communication parties, ensure overall system and network security within SDN environments, and foster trust among users and stakeholders. The experimental analysis focuses on machine learning (ML) and deep learning (DL) algorithms, particularly those employed in Intrusion Detection Systems (IDS), such as Naive Bayes (NB), k-Nearest Neighbors (kNN), Random Forest (RF), and Convolutional Neural Networks (CNN). The CNN algorithm demonstrates exceptional performance on the training dataset, achieving 99.96% accuracy with minimal training time. It also shows favorable results in terms of detection speed, requiring only 1 s, and maintains a low False Alarm Rate (FAR) of 0.02%. Subsequently, the proposed framework was deployed in a testbed SDN environment to evaluate its detection capabilities across diverse network topologies, showcasing its efficiency compared to existing approaches.

Analysis of Big Data Network Security Defense Mechanism Application of Artificial Intelligence

In the context of the close integration of computer network technology and daily life, big data network security faces many challenges. With its powerful information processing ability, this AI can effectively identify and handle potential threats in the network, thereby improving network security. This paper discusses the application of artificial intelligence (AI) in enhancing big data network security defense, especially in the threat detection link. This paper proposes a detection algorithm that combines support vector machine (SVM) and K nearest neighbor (KNN), namely SVM-KNN algorithm, for network attack detection and defense. When the hazard data is 60 under the test sample, the detection accuracy of KNN, SVM, and SVM-KNN is 86.88%, 89.51%, and 96.75% respectively; when the hazard data is 60 under the experimental sample, the detection accuracy of KNN, SVM, and SVM-KNN is 82.70%, 83.88%, and 89.26% respectively.response.

AI techniques applied to network intrusion detection system by using genetic approaches

The ability to identify and assess potential security risks to a communication network is a critical function of network intrusion detection systems. The data it provides regarding the frequency and type of assaults is a valuable addition to other network security measures, including firewalls. Typically, an NIDS will have a sensor that scans all incoming and outgoing packets on the monitored network, flags those that seem suspicious, and then sends the flagged packets and an alert message to a server system, which will then store them and analyse them in conjunction with other events. First, we standardised the protocol structure. Then, we used a genetic approach to generate mutation and cross-over values for device identification. Finally, we used a modified J48 decision tree algorithm to conduct our search. The developed algorithms/ones outperform the existing methods in terms of performance. As an initial step in detecting an intrusion, the 64-byte structured protocol standardisation technique is created. The wire shark tool is used to monitor the communication process network, taking into account all potential transactions. An array is created from the recorded packets. Included in the array are details about the frames, as well as the protocol type, hardware device type, source and destination IP addresses, MAC addresses, and data. This information is transformed by the 64-byte protocol structured standardisation. Because all of the necessary attributes are determined in the same place by this common protocol structure procedure, finding the MAC and IP addresses in packets should take as little time as possible. As a second step, the product and device details are supplied by the least and most important 16 bits of the MAC address. Using the cross-over and mutation functions, the genetic iv method compares the invader device to the Current Active Directory List (CADL).

AI in Cyber Defense: Tools and Techniques for Network Security

AI in Cyber Defense: Tools and Techniques for Network Security

Sine Cosine Algorithm Based on Optimal Convolutional Autoencoder for Intrusion Detection and Classification Models

Network security comprises a multifaceted method that aims to protect computer networks from malicious activities, unauthorized access, and data breaches. The security mechanism is Intrusion Detection which is an important constituent that is employed to monitor and analyse the network traffic for recognizing and responding to intrusive or suspicious behavior. Innovative methods such as deep learning (DL) are employed to enhance the effectiveness of Intrusion Detection Systems (IDSs). DL is extremely implemented for IDS owing to its proficiency for automatically learning and extracting complex patterns and features from massive and multifaceted network datasets. Neural network (NN) models, permit the system to distinguish between anomalous patterns and normal network behaviors, increasing the accuracy of intrusion detection. The flexibility of DL methods to emerging cyberattacks with their adeptness to handle large - scale and various data, positions them as a strong and efficient tool for proactive and intelligent intrusion detection in existing cybersecurity settings. This article presents a Sine Cosine Algorithm with Optimal Convolutional Autoencoder for Intrusion Detection and Classification (SCAOCAE - IDC) method. The developed SCAOCAE - IDC system presents a wide - ranging strategy to improve the precision and effectiveness of IDSs. The method combines diverse advanced mechanisms like Min - Max scalar normalization for data preprocessing, Sine Cosine Algorithm (SCA) for feature selection (FS), Convolutional Autoencoder (CAE) for better feature extraction and classification, and Heap - Based Optimization (HBO) for hyperparameter tuning. The Min - Max scalar makes sure of robust data normalization, SCA increasingly chooses main features, CAE capably captures complex patterns in the data, and HBO fine - tunes hyperparameters for improved system performance. By employing the synergistic combination of such modules, the presented SCAOCAE - IDC algorithm indicates considerable outcomes for increasing the reliability and accuracy of IDSs and classification systems.

A Robust Hybrid Approach for Malware Detection: Leveraging CNN and LSTM for Encrypted Traffic Analysis

The rapid growth in Internet usage and advancements in network technologies have escalated the risk of network attacks. As the adoption of encryption protocols increases, so does the difficulty in identifying malware within encrypted traffic. Malware represents a significant danger in cyberspace, as it compromises personal data and harms computer systems. Network attacks involve unauthorized access to networks, often aiming to disrupt or damage them, with potentially severe consequences. To counter these threats, researchers, developers, and security experts are constantly innovating new malware detection techniques. Recently, deep learning has gained traction in network security and intrusion detection systems (IDSs), with models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) showing promise in detecting malicious traffic. Despite these advancements, extracting relevant features from diverse malware types remains a challenge. Current solutions demand substantial computational resources and are often inefficient for large datasets. Additionally, existing image-based feature extraction methods consume significant resources. This study tackles these issues by employing a 1D CNN alongside LSTM for the detection and classification of encrypted malicious traffic. Using the Malware Analysis benchmark dataset, which consists of 42,797 malware and 1,079 goodware API call sequences, the proposed model achieved an accuracy of 99.2%, surpassing other state-of-the-art models

The role of cybersecurity in women’s involvement in online sexual violence and its response strategies

With the rapid development of information technology, cyberspace has become an indispensable part of people’s daily lives. However, cyberspace is not a pure land, especially with the increasingly prominent issue of online sexual violence against women. This article explores the role of cybersecurity in women’s involvement in online sexual violence and its response strategies. The article first analyzes how cyberspace has become an important platform for people’s lives and work in the background of the popularization of the Internet and social media. Still, it also brings about network security problems, especially the increasingly serious phenomenon of online sexual violence against women. These behaviors seriously violate women’s privacy and reputation rights and cause significant harm to their mental health. The article demonstrates the widespread and severe nature of online sexual violence through specific cases and statistical data. At the same time, the article also analyzed the causes of online sexual violence, including insufficient regulation of social media platforms, information overload of personalized algorithm recommendations, and the impact of user emotional intelligence on online behavior. Regarding response strategies, the article proposes various suggestions, such as improving network security regulations and protection systems, enhancing public awareness and education, and promoting social cooperation. In particular, it emphasizes the importance of formulating and improving laws and regulations against online sexual violence, strengthening cross-departmental collaboration, enhancing digital literacy programs, and promoting gender equality through school education and media publicity to raise awareness and respect for women’s rights.

Research on the Influencing Factors of High-quality Development of Export Enterprises

The Internet and big data have a profound impact on export enterprises, and their high-quality development is also facing unprecedented opportunities and challenges. This paper takes small and medium-sized enterprises of sales oriented export trade as the research sample, and uses the BP neural network model to conduct empirical research on the factors affecting the high-quality development of enterprises. Research conclusions: (1) A influencing factor system for high-quality development of export enterprises has been constructed, which includes 26 specific factors; (2) The factors in order of impacts: convenience of shopping operations, website content, comprehensive quality of employees, product awareness, consumer penetration rate, internationalization level, enterprise scale and reputation; (3) Factors such as overall employee quality, internationalization level, employee training system and effectiveness, network security, internationalization style of website design, convenience of shopping operations, sales growth rate of the enterprise, and profit growth rate do not have a direct, absolute positive relationship with the high-quality development of the enterprise.

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

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

Network Attack Classification using Neural Network-Based Imputation Technique

Rapid technological developments have changed access to information significantly, especially in telecommunications. This growth creates new threats, such as network attacks, so detection becomes critical for network security. Leveraging machine learning algorithms to detect threats is promising, with effectiveness largely dependent on selecting relevant features optimized by the bat algorithm. Data imputation is critical in preparing data sets, and neural network-based imputation techniques demonstrate outstanding performance, achieving accuracy rates of 99.4% on validation data and 99.3% on test data. This method consistently maintains precision, recall, and scores around 98%. Models using this method also approach perfection in classifying normal and neptune labels. This imputation method can also be applied to other model architectures using autoML. Alternative models such as Light GBM, XGBoost, Random Forest, Extra Trees, and Weighted Ensemble L2 also exhibit exceptional accuracy, exceeding 99.8%.

Identification of key areas for Territorial Ecological Restoration: focusing on ecological security and restoration potential

Implementing Territorial Ecological Restoration is a crucial approach to achieving ecological stability and regional sustainable development, and it also serves as an important measure for promoting the coordinated development of socioeconomic and ecological environments in the Yellow River Basin. However, effective identification of key areas for Territorial Ecological Restoration remains challenging in planning ecological restoration projects. This study focuses on ecological security and restoration potential, taking Linxia Prefecture in the ecologically fragile area of the upper Yellow River as an example. By evaluating six types of ecosystem services in this area, utilizing morphological spatial pattern analysis models and circuit theory, we identified the structural elements of ecological security patterns and assessed the potential for ecological restoration, identifying key areas for regional ecological restoration.The findings indicated that (1) 13 ecological patches were identified in total, predominantly distributed in the southwest and central regions of Linxia Prefecture. 25 ecological corridors were identified, demonstrating a high level of spatial coherence in terms of significance and connectivity, establishing a closely integrated ecological security network primarily in the southwest of Linxia Prefecture. (2) Thirteen ecological pinch points and seventeen ecological barrier points have been identified, concentrated in the central and southwestern regions of Linxia Prefecture. (3) Based on the characteristics and urgency of key areas for Territorial Ecological Restoration, they are classified and graded as “point-line-surface” and “primary-very important-important.” The primary restoration areas of the point type are mainly located in the central part of Linxia Prefecture, while the primary restoration areas of the line and plane types are mainly in the southwestern part of Linxia Prefecture. This paper emphasizing a holistic approach that prioritizes ecosystem integrity and social support to guide targeted restoration strategies across various ecological features, ultimately aiming for sustainable regional development and maximizing restoration benefits in ecologically fragile areas.

Implementation Strategy Analysis of Network Security using dalo RADIUS and Pi-hole DNS Server to enhance Computer Network Security, Case Study: XYZ as a Fintech Company

According to the annual report by the National Encryption Agency in 2023, Indonesia had the highest number of cyber-attack sources, with over 1 million attacks, which has increased quite rapidly compared to the last 3 years. Several online media platforms have reported incidents of this nature over the past three years. Among the 10 institutions implicated in the present incident, it has been confirmed that 6 of them are Fintech institutions. The incident's factors are various, including the user's lack of awareness who accessed an unofficial website outside the company's production website. This act ultimately proved to be detrimental to the company. Therefore, this study highlights the importance of RADIUS (Remote Authentication Dial-In User Service) as a comprehensive security tool that contributes to mitigating unauthorized access and strengthening network defenses against emerging threats. This research focuses on XYZ, a Fintech Company, using it as a case study. The main discussion in this paper focuses on utilizing the daloRADIUS server to resolve authorization concerns regarding network security. Pi-hole DNS Server is also used in this research to block access to illegal sites such as pornography and online gambling. The results of this research are proof of the success of a combination of daloRADIUS server components, RADIUS router, and Pi-hole DNS Server in blocking users who are detected accessing illegal sites and are also observed regarding the use of daloRADIUS Server resources in a usage range ranging from 10 active users to 300 active users.