Network Security Vulnerabilities Research Articles

Deep Learning-Based Information Security

Abstract. With the rapid development of network technology, information security is facing increasingly complex challenges. Deep learning technology, due to its strong capabilities in data processing and pattern recognition, has become a key technology to improve the detection efficiency and accuracy in the field of information security. This paper delves into the application of deep learning in various aspects such as malware detection, network intrusion identification, User and Entity Behavior Analytics (UEBA), privacy protection technology, model explainability, and network security vulnerability detection, and proposes deep learning-based information security methods. Through experimental validation, our methods have outperformed traditional machine learning models in multiple evaluation metrics, providing new solutions for the field of information security. In the future, we will continue to explore new applications of deep learning in the field of information security to cope with the ever-changing network security threats.

Mitigating Security Vulnerabilities in 6G Networks: A Comprehensive Analysis of the DMRN Protocol Using SVO Logic and ProVerif

Mitigating Security Vulnerabilities in 6G Networks: A Comprehensive Analysis of the DMRN Protocol Using SVO Logic and ProVerif

Beyond Bombs: The Growing Threat of Cyberterrorism

Cyberterrorism, which once was a virtual version of physical attacks, has now moved to a digital platform that targets the critical infrastructure. These activities pursue to introduce fear, make a disruption in essential services, and influence politics. On the other hand, a comprehensive strategy should be applied to deal with this dynamic danger. The importance of understanding cyber terrorism lies in the fact that it creates breach of trust, collapse of the system, and sufferings among the population mentally. The economic impacts comprise businesses losses, decline in productivity, and discouragement of foreign investments. Efficient tools such as network security monitoring, vulnerability scanning and threat intelligence can locate anomalous activity as well as loopholes in systems. Strong authentication, employee training, data encryption, and regular backups all lead to a digital environment that is less vulnerable to attack. International cooperation, public education, and constant development are the key aspects for having a preparedness against cyberterrorism. We can accomplish this through collaboration and thereby ensure the safety and security of the digital world. Keywords—Cyberterrorism, digital threats, international cooperation, public awareness, critical infrastructure.

WIFI-SLAX (WIFI-CRACKING & SECURITY)

This research paper delves into WiFi network security with a focus on WiFi-SLAX, a potent tool designed for WiFi-cracking and security assessment. Exploring common WiFi-cracking techniques, security vulnerabilities in WiFi networks, and WiFi-SLAX's features, the paper assesses its impact on enhancing WiFi network security. Real-world case studies demonstrate WiFi-SLAX's efficacy in security assessments, while recommendations address its deployment in evolving network environments. By offering insights into WiFi-SLAX's capabilities and contributions to network security, this paper aids network professionals in fortifying wireless networks against potential threats. Research Paper Title: Exploring WiFi-SLAX: A Comprehensive Study on WiFi-Cracking and Security

An Evaluation of the Proposed Security Access Control for BYOD Devices with Mobile Device Management (MDM)

Bring Your Own Device (BYOD) at Work is a growing practice that has significantly increased network security vulnerabilities. This development has tremendous implications for both businesses and individuals in every organization. As a result of the extensive spreading of viruses, spyware, and other problematic downloads onto personal devices, the government has been forced to examine its data protection legislation. Dangerous apps are downloaded into personal devices without the user's awareness. As a result, both people and governments may suffer disastrous repercussions. In this research, proposed BYODs are troublesome since they can change policies without consent and expose private information. This type of privacy violation has a domino effect, resulting in substantial legal and financial consequences as well as decreased productivity for enterprises and governments. Governments have a daunting problem since they must protect networks from these threats while simultaneously considering user rights and privacy legislation. The framework of this paper that decreases the number of system limits and access control methods that are established for BYODs and cloud environments has been presented by the researchers of the study. They also attempted to protect user privacy by implementing Mobile Device Management (MDM) technology. The study's preliminary findings were optimistic, implying that the framework might reduce access control difficulties.

Network Security Threats and Vulnerabilities

The communication of confidential data over the Internet increases every day. Individuals and organizations submit their sensitive data electronically. It is also common for hackers to target these networks. Nowadays, protecting your data, software, and hardware from viruses is more of a necessity than ever, not just a concern. Cyber security is traditionally defined by the three attributes of confidentiality, integrity and availability. Confidentiality is the prevention of unauthorized disclosure of information. Integrity is the prevention of unauthorized modification of information, and availability is the prevention of unauthorized retention of information or resources. This article studies the different types of security threats, resources and vulnerabilities of computer systems.

Novel Machine Learning Approach for DDoS Cloud Detection: Bayesian-Based CNN and Data Fusion Enhancements.

Cloud computing has revolutionized the information technology landscape, offering businesses the flexibility to adapt to diverse business models without the need for costly on-site servers and network infrastructure. A recent survey reveals that 95% of enterprises have already embraced cloud technology, with 79% of their workloads migrating to cloud environments. However, the deployment of cloud technology introduces significant cybersecurity risks, including network security vulnerabilities, data access control challenges, and the ever-looming threat of cyber-attacks such as Distributed Denial of Service (DDoS) attacks, which pose substantial risks to both cloud and network security. While Intrusion Detection Systems (IDS) have traditionally been employed for DDoS attack detection, prior studies have been constrained by various limitations. In response to these challenges, we present an innovative machine learning approach for DDoS cloud detection, known as the Bayesian-based Convolutional Neural Network (BaysCNN) model. Leveraging the CICDDoS2019 dataset, which encompasses 88 features, we employ Principal Component Analysis (PCA) for dimensionality reduction. Our BaysCNN model comprises 19 layers of analysis, forming the basis for training and validation. Our experimental findings conclusively demonstrate that the BaysCNN model significantly enhances the accuracy of DDoS cloud detection, achieving an impressive average accuracy rate of 99.66% across 13 multi-class attacks. To further elevate the model's performance, we introduce the Data Fusion BaysFusCNN approach, encompassing 27 layers. By leveraging Bayesian methods to estimate uncertainties and integrating features from multiple sources, this approach attains an even higher average accuracy of 99.79% across the same 13 multi-class attacks. Our proposed methodology not only offers valuable insights for the development of robust machine learning-based intrusion detection systems but also enhances the reliability and scalability of IDS in cloud computing environments. This empowers organizations to proactively mitigate security risks and fortify their defenses against malicious cyber-attacks.

Infrastructural insecurity: Geopolitics in the standardization of telecommunications networks

This article argues that the production and maintenance of “infrastructural insecurity” is an inherent part of the process of the standardization of telecommunication networks. Infrastructural insecurity is the outcome of intentional practices during the production, standardization, and maintenance of communication infrastructures that leave end-users vulnerable to attacks that benefit particular actors. We ground this analysis in the qualitative and quantitative exploration of the responses to the disclosure of three fundamental security vulnerabilities in telecommunications networks. To research the shaping of communication and infrastructure architectures in the face of insecurities, we develop a novel approach to the study of Internet governance and standard-setting processes that leverages web scraping and computer-assisted document set discovery software tools combined with document analysis. This is an important contribution because it problematizes the process of standardization and asks fundamental questions about the adequacy and legitimacy of the process and procedures of standardization, its participants, and its institutions.

Network Security with Virtual Reality based Antivirus Protection and Reduced Detection Delays

Addressing the persistent delay problem in traditional network security antivirus protection systems, this paper introduces an innovative approach utilizing virtual reality (VR) technology. The primary objective is to significantly reduce detection delays and enhance the efficiency of network security measures. An enhanced decision-making algorithm is proposed to identify relevant features associated with network security. These features are then weighted and optimized to improve the overall detection process. An injection list is generated through web crawling techniques to strengthen security measures. A virtual protection block is also developed to serve as a barrier against potential threats. The proposed method claims a detection delay of only 75.33 milliseconds, significantly outperforming two traditional methods that recorded 290.11 milliseconds and 337.30 milliseconds, respectively. This substantial decrease in detection delay emphasizes the effectiveness of automatic detection within the context of VR technology. Practical implementation and empirical evidence further validate the success of this approach. The automatic detection of network security vulnerabilities within the VR technology framework is efficient and exhibits considerable progress. As such, this research offers a promising solution to the delay problem in network security antivirus protection. Embracing VR technology achieves shorter detection delays, ultimately improving the security posture of network systems.

Security Vulnerabilities in 5G Non-Stand-Alone Networks: A Systematic Analysis and Attack Taxonomy

5G networks, pivotal for our digital mobile societies, are transitioning from 4G to 5G Stand-Alone (SA) networks. However, during this transition, 5G Non-Stand-Alone (NSA) networks are widely used. This paper examines potential security vulnerabilities in 5G NSA networks. Through an extensive literature review, we identify known 4G attacks that can theoretically be applied to 5G NSA. We organize these attacks into a structured taxonomy. Our findings reveal that 5G NSA networks may offer a false sense of security, as most security and privacy improvements are concentrated in 5G SA networks. To underscore this concern, we implement three attacks with severe consequences and successfully validate them on various commercially available smartphones. Notably, one of these attacks, the IMSI Leak, consistently exposes user information with no apparent security mitigation in 5G NSA networks. This highlights the ease of tracking individuals on current 5G networks.

Research on Computer Network Security Vulnerabilities and Encryption Technology in Cloud Computing Environment

Abstract Inadequate network security defense measures threaten the information and property security of the state and the public, and how to safeguard network security is of vital practical significance. This paper proposes a dynamic security threat assessment model and a robust optimal control strategy to improve the efficiency of detecting network vulnerabilities and the accuracy of detecting network threats in a cloud computing environment. It also protects the privacy of the user's identity through a multi-factor continuous authentication method, encrypts and protects the user's data using a homomorphic encryption algorithm, and strengthens the ability of computers to resist intrusion. Three case studies are conducted to verify the effectiveness of the proposed technical approach: dynamic assessment of security threats, control policy, authentication, and network encryption. The results show that in the network vulnerability control policy and authentication case study, when adding the control policy Tk = 40, the percentage of network normal nodes, malicious nodes and restorer nodes are 0.98, 0.02, and 0.009, respectively, and the encrypted plaintext ASCII value is distributed in [0,60] in an unordered manner, and the network is in a very desirable security state. Real-time accurate assessments of network security state can be provided by cloud computing-based network security vulnerability and encryption technology.

A machine learning based approach for 5G network security monitoring

Abstract This paper constructs a 5G network security detection system based on the functional requirements of network security detection and the P2DR process model. The structure can be broken down into three layers from the bottom to the top: acquisition layer, analysis layer, and display layer. The design focuses on writing the SVM algorithm into the software of the communication network security vulnerability monitoring system, and in order to solve the defect of the long training time of the model of this machine learning algorithm, the incremental learning vector machine model is used, which is combined into the CSV-KKT-ISVM model. Test datasets that cover system performance, effectiveness, and leakage are used to test the system after it is completed. The test data was analyzed to prove that the system’s memory usage was maintained at 46M, CPU usage was 5% to 10%, and the response time was no later than 1 s. The monitoring accuracy was 98.5% at the highest but decreased with the increase of the percentage of vulnerability data, and the accuracy dropped to 93.9% at 50%, the minimum was not lower than 90%, and the error rate was no less than 0.8%. To achieve the best outcome, the system threshold should be set to 5, and there should be no false alarms or misreporting.

INFRASTRUCTURAL INSECURITY: GEOPOLITICS IN THE STANDARDIZATION OF TELECOMMUNICATIONS NETWORKS

This paper argues that the production of ‘infrastructural insecurity’ is an inherent part of the standardization of information networks. Infrastructural insecurity is the outcome of an intentional process within infrastructural production, standardization, and maintenance that leaves end-users of the infrastructure vulnerable to attacks that benefit a particular actor. We ground this analysis in an interrogation of the responses to the disclosure of three security vulnerabilities in telecommunications networks, namely (1) a security flaw in Signaling System No. 7 (SS7) that allows for the data interception and surveillance, SMS interception and location tracking by third parties, (2) the lack of encryption of permanent identifiers that allowed for the deployment of rogue base stations, which allowed for man-in-the-middle attacks, resulting in interception of all voice and data traffic in a physical signal vicinity, and (3) the lack of forward secrecy between user-equipment and the home network, which allows for the decryption of current encrypted data stream if credentials were obtained in the past. To research the shaping of communication and infrastructure architectures in the face of insecurities, we develop a novel approach to the study of Internet governance and standard-setting processes that leverages web scraping and computer-assisted document set discovery software tools combined with document analysis. We bring these methods into conversation with theoretical approaches from material media studies, science and technology studies, and critical security studies. This is an important contribution because it asks fundamental questions about the adequacy and legitimacy of standardization processes.

SECURITY CONSIDERATIONS AND GUIDELINES FOR AUGMENTED REALITY IMPLEMENTATION IN CORPORATE ENVIRONMENTS

This research paper explores the intricate tapestry of security considerations in integrating augmented reality (AR) within corporate landscapes. The journey begins with an in-depth literature review, providing insights into authentication, data privacy, network security, and device vulnerabilities specific to AR systems. A conceptual framework, synthesizing the augmented reality security framework with legal, ethical, and human-centric dimensions, serves as a foundational guide. The guidelines proposed outline a strategic roadmap, emphasizing policy formulation, employee training, security audits, integration with existing infrastructures, legal compliance, and device security. The conclusion underscores the dynamic nature of AR technology, advocating for ongoing vigilance and collaboration to secure the evolving frontier of augmented reality in corporate environments.
 Keywords: Augmented Reality Security, Corporate Environments, Authentication, Data Privacy, Network Security, Conceptual Framework, Security Guidelines.

Digital Communication Forensics in 6G and beyond Networks

6G networks can be truly intelligent wireless systems that have the flexibility for continuous technological improvements. However, security events affecting information systems have grown increasingly complicated and sophisticated. Intruders may be able to operate due to a lack of evidence authenticity. Using network forensics methods can solve many cybercrime instances. These methods can be used to locate the intruder and to determine the intrusion’s origin and how it can be avoided in the future. Existing network forensics techniques confront security threats and vulnerabilities in several networks, such as personal area networks, local area networks, wide area networks, and wireless wide area networks under telecom networks, in practice, but cannot operate in 6G and beyond telecom networks, which are critical at this moment. Thus, a comprehensive literature analysis of telecom networks is conducted to identify existing concerns and challenges in conducting digital communication forensics. Hence, a digital forensics system for 6G and beyond networks is proposed as an initial idea on the basis of the highlighted issues, thereby assessing security incidents and providing justified evidence that explains the footprints of the attackers.

Vulnerability Detection in Computer Networks using Virtual Reality Technology

This paper challenges the time-related challenges inherent in conventional network security detection methodologies. It is achieved by incorporating virtual reality technology into the domain of computer network security detection. The research methodology employs optimization calculations to extract attributes that characterize network security vulnerabilities. Concurrently, the weighting of diverse vulnerability attributes is adjusted using a web crawler, a comprehensive list of injection points, and meticulous analyses of the attacks’ genetic characteristics. This collective approach facilitates the exploration of automated network security vulnerability detection within a virtual reality framework. The study’s empirical results demonstrate that the detection method proposed within this investigation exhibits a notably reduced delay of 75.33 milliseconds. The respective delays observed in the two conventional methods stand at 290.11 milliseconds and 337.30 milliseconds. The substantial decrease in detection delay validates the effectiveness and efficiency of the devised automated network vulnerability detection approach grounded in virtual reality technology.

Machine Learning: Research on Detection of Network Security Vulnerabilities by Extracting and Matching Features

The existence of vulnerabilities is a serious threat to the security of networks, which needs to be detected timely. In this paper, machine learning methods were mainly studied. Firstly, network security vulnerabilities were briefly introduced, and then a Convolutional Neural Network (CNN) + Long Short-Term Memory (LSTM) method was designed to extract and match vulnerability features by preprocessing vulnerability data based on National Vulnerability Database. It was found that the CNN-LSTM method had high training accuracy, and its recall rate, precision, F1, and Mathews correlation coefficient (MCC) values were better than those of support vector machine and other methods in detecting the test set; its F1 and MCC values reached 0.8807 and 0.9738, respectively; the F1 value was above 0.85 in detecting different categories of vulnerabilities. The results demonstrate the reliability of the CNN-LSTM method for vulnerability detection. The CNN-LSTM method can be applied to real networks.

A Survey on Packet Capture: Tool Introduction and Security Vulnerability Description

This work involves discovering network security vulnerabilities based on existing packet capture software. By learning the operation principle of packet capture software, we discuss the hidden network security problem among them. There are two basic packet capture software mentioned Wireshark and Sniffer. Analyse the SSL (Secure Socket Layer) protocol and HTTPS (Hypertext Transfer Protocol Secure) protocol to observe where causes a vulnerability in those protocols that allows the thief to obtain information through packet capture software. Finding an attack mode of the thief, we further study and discuss how to reduce the risk of information leakage caused by vulnerability existing. We hope the perspective proposed is not only to avoid workstation chaos caused by packet capture software as far as possible, but also to further encrypt the plaintext data to make it more difficult for packet capture tools to parse the packet.

A Cyber-Physical Power System Risk Assessment Model Against Cyberattacks

With the integration of cyber devices into power systems, such as information communication technology devices and intelligent electronic devices, there are currently a large number of software vulnerabilities (SVs) in cyber devices, which are probably used to obtain the authority of cyber devices for malicious attackers and be carried out subsequently severe attacks on power systems. Thus, it is inappropriate to only consider the power risks as the risk assessment for the system with the strong coupling between cyber systems and power systems. In this article, we investigate a dynamic risk assessment model for the cyber-physical power system (CPPS) against cyberattacks, combining the network security vulnerability of the supervisory control and data acquisition (SCADA) system in a substation and physical consequences in power systems caused by it under malicious control. By construction, a dynamic network security vulnerability assessment method for the SCADA system is well studied by considering SVs and deployed network security defense measures, which is also corrected by the propagation characteristics. Moreover, we estimate physical consequences by minimum shedding loads in N-1 contingencies caused by the SCADA system in a substation under malicious control. Finally, simulations on modified IEEE 14-bus and IEEE 118-bus systems verify the proposed model's effectiveness, highlighting the importance of network security in CPPS.

Neural network based single index evaluation for SQL injection attack detection in health care data

In recent years, there are a lot of security risks in the network, and there is the combination intersection between the computer network security problems and security evaluation. The scale of computer network is very large with vast information, so there are many loopholes in the system network. At present, many have established a computer network security evaluation system to monitor the network security vulnerabilities, viruses, and defects in healthcare. However, many places simply analyzed the risk assessment of network security, but there is no assessment of network security situation. For the network security evaluation system, there is no complete evaluation system of network information security. Therefore, a network security evaluation system must be constructed to develop an effective and practical simulation model of computer network security evaluation. Through the simulation model, the effect of network security can be improved. Since the reform and opening up, the simulation of computer network security evaluation in our country is a new subject. It can directly study the network security evaluation, build a network security evaluation model, and study the network security in detail. In the computer network simulation system, it can analyze, study, design, and plan various stages, so as to play an important role.