Network Security Infrastructure Research Articles

Evaluating Sensitivity and Stability in Secure Deep Learning Based Channel Estimation for NextG Networks with Defensive Distillation

In the domain of wireless networks, including 5G and beyond, envisions a highly interconnected environment, enabling swift data transmission, expanded cell capacity, and minimal latency to support various applications and advanced manufacturing. Next Generation (NextG) networks seek to optimize and enhance network functionalities through Artificial intelligence (AI) integration, although concerns persist regarding the security of AI based models, particularly regarding model poisoning. Protecting these networks against cybersecurity threats, especially adversarial attacks, is vital. Hence, effective mitigation techniques and secure solutions employing AI-based methods are crucial. This study delves into the pivotal aspects of stability and sensitivity in AI-driven models tailored for NextG networks, focusing on Deep learning (DL) based channel estimation models. Utilizing data from MATLAB's 5G toolbox, we scrutinize these models' susceptibility to adversarial attacks and evaluate the efficacy of defensive distillation techniques in mitigating vulnerabilities. The research highlights the urgent necessity to fortify DL-based models against potential threats within NextG networks. Through a comprehensive evaluation of defensive distillation strategies, we showcase their ability to bolster both the stability and sensitivity of channel estimation models. By addressing these critical aspects, the study aims to enhance the resilience of AI-driven solutions within NextG environments, strengthening defenses against cybersecurity risks and safeguarding network integrity. Ultimately, the findings underscore the importance of prioritizing sensitivity and stability in the development and deployment of AI-based models for NextG networks. Through proactive measures and advanced mitigation methods, efforts are made to mitigate the risks posed by adversarial attacks and foster a more secure and resilient network infrastructure for the future.

Estimating Malware Impact on Network Traffic Analysis by Using Wireshark

With the increasing prevalence of advanced cyber threats, there is a growing need for effective cybersecurity measures that can detect and visualize malware attacks. This research introduces an integrated approach that combines malware detection techniques with geospatial visualization methods to enhance the identification and analysis of cyber-attacks. By analyzing packets in the HTTP protocol, we identify suspicious file transfers and compute their hash values for further examination. To assess the threat level of these transferred files, we utilize the Virus Total platform to conduct comprehensive scans for malware. At the same time, by utilizing geolocation data, we map out both the origins and destinations of these attacks, providing valuable spatial context for understanding global patterns in cyber threats. In addition to enabling the identification of potentially harmful files, the proposed approach also provides a comprehensive visualization of how these threats are spread geographically. Our findings contribute to advancing cybersecurity strategies by facilitating proactive threat mitigation and enhancing incident response capabilities. The integration of malware detection, hash analysis, and geospatial visualization emphasizes the importance of adopting a multidimensional approach in strengthening network security infrastructure.

Challenges and Outcomes of Combining Machine Learning with Software-Defined Networking for Network Security and management Purpose: A Review

Current research in data dissemination in Vehicular Ad Hoc Networks (VANETs) has examined different approaches and frameworks to enhance the effectiveness and dependability of information sharing between vehicles on the road. The integration of Machine Learning (ML) with Software-Defined Networking (SDN) has fundamentally transformed the field of network administration and security. This paper specifically addresses the challenges faced by traditional network architectures in effectively handling the increasing amount of data and complex applications. Software-Defined Networking (SDN), a cutting-edge framework, separates the control of network operations from the actual forwarding of data, offering a versatile and cost-effective solution. The combination of Software-Defined Networking (SDN) and Machine Learning (ML) allows for the extraction of valuable information from network data, leading to enhanced network management and the facilitation of predictive analytics. The aim of this study is to examine the feasibility and challenges of incorporating machine learning into software-defined networking (SDN), focusing particularly on practical applications. Integrating Machine Learning (ML) into Software-Defined Networking (SDN) presents challenges, including the requirement for robust algorithms to detect patterns and ensure security. It is crucial to carry out the tasks of developing and implementing machine learning models for real-time predictions and ensuring the robustness of the system. Research is essential to strike a balance between the transformative abilities of ML-SDN and the practical network environments. This helps to improve the resilience, security, and adaptability of network infrastructures in the digital age.

Contextual feature representation for image-based insider threat classification

With the rapid development of technology, insider threat incidents frequently occur in organizations. Detecting insider threats is an essential task in network infrastructure security. In this paper, we design an attention module to extract contextual features and augment abnormal features to generate high-quality images representing user behavior. Then, we use pre-trained ResNet and multi-source feature fusion on behavioral, psychological, and role features, intending to identify malicious insiders accurately. The proposed approaches are evaluated using the CMU-CERT Insider Threat Dataset. Experimental results show the effectiveness of methods and outperform other state-of-the-art methods.

Identifying network connection: Benign vs. DoS attack - A comparative analysis of binary classifiers

The escalating complexity of cyber-attacks coupled with the increasing sophistication of attackers have created an imperative need for robust and adaptable defense mechanisms to ensure the security of network infrastructure. Among these threats, Denial of Service (DoS) attacks stand out prominently. These attacks focus on inundating systems with high-frequency traffic to exhaust resources and disrupt the availability of services. Consequently, the accurate and timely identification of 'DoS Attacks' is of paramount importance in upholding the integrity and functionality of networks. In this context, the present paper aims to delve into the efficacy of various binary classifiers in effectively distinguishing normal network connections from instances of 'DoS Attacks.' By undertaking this exploration, the study aims to pinpoint the classifiers that exhibit the highest level of effectiveness in tackling this specific task. Ultimately, a comprehensive understanding of which classifiers perform best in discerning these types of cyber threats can significantly contribute to enhancing the overall security posture of network infrastructures.

Advancing Network Infrastructure: Integrating VXLAN Technology with Automated Circuit Operations and NOS Configurations

Enhancing network infrastructure is achieved through integrating VXLAN technology, Python-automated circuit operations, and Ansible-driven Network Operating System (NOS) configurations, complemented by GitHub for reliable configuration backups. VXLAN, a robust network virtualization protocol, addresses the challenges of managing extensive network segments. Python scripts facilitate the automated analysis, creation, and management of network circuits, significantly boosting efficiency and accuracy. Ansible, a powerful automation tool, is employed to streamline NOS configurations, ensuring consistency and reducing manual overhead in network settings. Concurrently, GitHub, working in tandem with crontab scheduling, offers a dependable platform for the automated, regular backup of configurations, thus enhancing network resilience and simplifying recovery processes. The collective implementation of VXLAN, Python, and Ansible automation, along with GitHub for configuration management, marks a notable advancement in operational efficiency, underscoring their importance as critical components in the modernization and security of network infrastructures.

Emerging Cyber Security Challenges: Implications for Iranian National Security

In the third decade of 21st century, cyber security has become a completely developed national security paradigm like other prominent security paradigms such as physical security, economic security, and food security. Unmatched pace of technological advancements in the fields of Information Technology, Artificial Intelligence, automation, and communication services have transformed key processes of governance, commerce, and trade from physical to cyber space. As human dependence on cyber technologies increased new questions about the prevailing security models has come to the fore during the same time. Concepts like network security, data security or infrastructure security are no longer isolated domains but have transformed as securitization of cyber domain has become an ongoing global phenomenon. This paper focuses on how this transformation of geopolitical conflicts, from physical to cyber arena, is taking place where traditional concepts of conventional war are changing. It also examines how cyber security threat matrix is diametrically different from all prevailing military doctrinal construct? These questions certainly demand a new theoretical framework that can put this rapidly emerging new security paradigm. This study attempts to address this important question as well while taking the international and regional developments with special focus on the emerging cyber security domain in Iran.

EPPTA: Efficient partially observable reinforcement learning agent for penetration testing applications

AbstractIn recent years, penetration testing (pen‐testing) has emerged as a crucial process for evaluating the security level of network infrastructures by simulating real‐world cyber‐attacks. Automating pen‐testing through reinforcement learning (RL) facilitates more frequent assessments, minimizes human effort, and enhances scalability. However, real‐world pen‐testing tasks often involve incomplete knowledge of the target network system. Effectively managing the intrinsic uncertainties via partially observable Markov decision processes (POMDPs) constitutes a persistent challenge within the realm of pen‐testing. Furthermore, RL agents are compelled to formulate intricate strategies to contend with the challenges posed by partially observable environments, thereby engendering augmented computational and temporal expenditures. To address these issues, this study introduces EPPTA (efficient POMDP‐driven penetration testing agent), an agent built on an asynchronous RL framework, designed for conducting pen‐testing tasks within partially observable environments. We incorporate an implicit belief module in EPPTA, grounded on the belief update formula of the traditional POMDP model, which represents the agent's probabilistic estimation of the current environment state. Furthermore, by integrating the algorithm with the high‐performance RL framework, sample factory, EPPTA significantly reduces convergence time compared to existing pen‐testing methods, resulting in an approximately 20‐fold acceleration. Empirical results across various pen‐testing scenarios validate EPPTA's superior task reward performance and enhanced scalability, providing substantial support for efficient and advanced evaluation of network infrastructure security.

A Method of Network Access Control for Ensuring Network Infrastructure Security Based on Severing Superfluous Network Connectivity

A Method of Network Access Control for Ensuring Network Infrastructure Security Based on Severing Superfluous Network Connectivity

A case analysis for Kubernetes network security of financial service industry in Indonesia using zero trust model

In this study, a case analysis of Kubernetes application in an enterprise providing financial services in Indonesia is presented. It is implemented to improve their digital services-based application, developed using micro-services architecture. Kubernetes, an application container technology, has been applied in the enterprise providing financial services in Indonesia to improve their digital services-based application, developed using micro-services architecture concepts. Without incorporating any additional hardware, the new technology and services have been adopted to existing virtualized resources of the enterprise. An infrastructure design for secure Kubernetes networking was built and has been studied using the data center provided by the enterprise. This study focuses on two important aspects: network and security. As a security guideline, the network recommendations from VMware, Cisco, and Forrester’s zero trust model were employed to design the infrastructure and were evaluated. The proposed secure network infrastructure design is successfully applied in the container networks using the zero trust requirement, the enterprise’s requirements, and constraints. And we hope this study about network design security can be used and adaptable with existing network and reduce risk and disruption to the business caused by the network.

Encrypted Model Predictive Control of a Nonlinear Chemical Process Network

This work focuses on developing and applying Encrypted Lyapunov-based Model Predictive Control (LMPC) in a nonlinear chemical process network for Ethylbenzene production. The network, governed by a nonlinear dynamic model, comprises two continuously stirred tank reactors that are connected in series and is simulated using Aspen Plus Dynamics. For enhancing system cybersecurity, the Paillier cryptosystem is employed for encryption–decryption operations in the communication channels between the sensor–controller and controller–actuator, establishing a secure network infrastructure. Cryptosystems generally require integer inputs, necessitating a quantization parameter d, for quantization of real-valued signals. We utilize the quantization parameter to quantize process measurements and control inputs before encryption. Through closed-loop simulations under the encrypted LMPC scheme, where the LMPC uses a first-principles nonlinear dynamical model, we examine the effect of the quantization parameter on the performance of the controller and the overall encryption to control the input calculation time. We illustrate that the impact of quantization can outweigh those of plant/model mismatch, showcasing this phenomenon through the implementation of a first-principles-based LMPC on an Aspen Plus Dynamics process model. Based on the findings, we propose a strategy to mitigate the quantization effect on controller performance while maintaining a manageable computational burden on the control input calculation time.

Deep learning model for intrusion detection system utilizing convolution neural network

Abstract An integral part of any reliable network security infrastructure is the intrusion detection system (IDS). Early attack detection can stop adversaries from further intruding on a network. Machine learning (ML) and deep learning (DL) techniques to automate intrusion threat detection at a scale never previously envisioned have snowballed during the past 10 years. Researchers, software engineers, and network professionals have been encouraged to reconsider the use of ML techniques, notably in cybersecurity. This article proposes a system for detecting intrusion with two approaches, the first utilizing a proposed hybrid convolutional neural network (CNN) and Dense layers. The second utilizes naïve Bayes (NB) ML techniques and compares the two approaches to determine the best detection accuracy. The preprocessing of network data is necessary. The suggested technique is evaluated using the UNSW-NB15 Dataset to create a reliable classifier and an effective IDS. The experimental results for the proposed CNN-dense classifier outperformed the ML and DL models. CNN has a 99.8% accuracy rate compared to previous studies. At the same time, the Gaussian naïve Bayes, which is considered the best among the ML-utilized classifiers, yielded an 83% accuracy rate.

Analisis Keamanan Infrastruktur Jaringan Berdasarkan Cyber Kill Chain Framework

This study concentrates on examining the security of network infrastructure using the cyber kill chain framework approach. The research is conducted within a company operating in network security services. In its operations, it offers a Virtual Private Cloud (VPC) containing various crucial information such as applications, internal data, client data, and product demos. Despite the attractive features of cloud computing, there are significant threats as well. Handling attacks cannot be swiftly and efficiently executed, resulting in temporary operational unavailability until the attacks are resolved. This research delves into the security of the company's infrastructure by testing several points of vulnerability exploited by malicious parties. The cyber kill chain framework approach is employed to systematically assess the company's infrastructure. The study reveals that certain issues have been adequately detected; however, security gaps persist, as evidenced by the testing conducted and the inadequate response from the company's security systems.

Feature Selection and Model Evaluation for Threat Detection in Smart Grids

The rising interest in the security of network infrastructure, including edge devices, the Internet of Things, and smart grids, has led to the development of numerous machine learning-based approaches that promise improvement to existing threat detection solutions. Among the popular methods to ensuring cybersecurity is the use of data science techniques and big data to analyse online threats and current trends. One important factor is that these techniques can identify trends, attacks, and events that are invisible or not easily detectable even to a network administrator. The goal of this paper is to suggest the optimal method for feature selection and to find the most suitable method to compare results between different studies in the context of imbalance datasets and threat detection in ICT. Furthermore, as part of this paper, the authors present the state of the data science discipline in the context of the ICT industry, in particular, its applications and the most frequently employed methods of data analysis. Based on these observations, the most common errors and shortcomings in adopting best practices in data analysis have been identified. The improper usage of imbalanced datasets is one of the most frequently occurring issues. This characteristic of data is an indispensable aspect in the case of the detection of infrequent events. The authors suggest several solutions that should be taken into account while conducting further studies related to the analysis of threats and trends in smart grids.

Identification of Network Traffic Intrusion Using Decision Tree

Network technology plays an increasingly important role in all aspects of social life. The Internet has brought a new round of industrial revolution and industrial upgrading. The arrival of the “Internet” era is accompanied by a large-scale increase in network applications and the number of netizens. At the same time, the number and severity of cyberattacks continue to increase. Therefore, intrusion detection systems (IDSs) have become an important part of the current network security infrastructure in various industries. Anomaly detection of network traffic data is an effective method for network protection. In order to better realize the detection of network traffic anomalies, several algorithms have been successfully applied. Most of them come from artificial intelligence (AI), but there is a general problem of excessive model execution processing time and low detection rates. And through a lot of research, it is found that most models do not pay enough attention to the data processing in the early stage. Therefore, in this paper, we optimize the data normalization process through a series of experiments and combine the PCA feature selection method to propose an optimized MaxAbs-DT classifier model. To train and measure the performance of the model, we used the NSL-KDD dataset, which is the benchmark dataset for most network anomaly detection models. The experimental results show that MaxAbs-DT outperforms other existing models and validates the effectiveness of the method. In addition, its execution time is greatly reduced compared to many models.

Proficient Intrusion Detection System using Machine Learning using Machine Learning

With the ever-growing dependence on computer networks for various purposes, network security has become a crucial aspect. Proficient Network Intrusion Detection System (PNIDS) is an essential component of network security infrastructure that helps to detect and prevent unauthorized access and malicious activities on the network. The primary objective of this project is to design and implement a Network Intrusion Detection System that can detect and prevent network attacks. The system will be built using various techniques such as rule-based detection, anomaly detection, and machine learning-based detection.

Development of a Machine-Learning Intrusion Detection System and Testing of Its Performance Using a Generative Adversarial Network.

Intrusion detection and prevention are two of the most important issues to solve in network security infrastructure. Intrusion detection systems (IDSs) protect networks by using patterns to detect malicious traffic. As attackers have tried to dissimulate traffic in order to evade the rules applied, several machine learning-based IDSs have been developed. In this study, we focused on one such model involving several algorithms and used the NSL-KDD dataset as a benchmark to train and evaluate its performance. We demonstrate a way to create adversarial instances of network traffic that can be used to evade detection by a machine learning-based IDS. Moreover, this traffic can be used for training in order to improve performance in the case of new attacks. Thus, a generative adversarial network (GAN)-i.e., an architecture based on a deep-learning algorithm capable of creating generative models-was implemented. Furthermore, we tested the IDS performance using the generated adversarial traffic. The results showed that, even in the case of the GAN-generated traffic (which could successfully evade IDS detection), by using the adversarial traffic in the testing process, we could improve the machine learning-based IDS performance.

Construction of Network Culture Security Indicator System Based on Deep Learning Algorithm

The Internet has become a key information infrastructure in China. However, in recent years, China's network security incidents have been frequent, thus posing a great threat to relevant software, hardware and other information infrastructure. It is very important to establish a large-scale network security situation analysis system based on existing network security infrastructure and technology to improve the processing capabilities of controlling, analyzing, and predicting network security situations. Network security situation assessment is an emerging research hotspot that has emerged in recent years. This article first defined a series of concepts such as the security indicator ring system, network culture, and deep learning algorithms, and then conducted comparative analysis through experiments. Compared with the original dataset, the classification accuracy after feature selection increased by 33.6%.

Infrastructure as Code for Security Automation and Network Infrastructure Monitoring

The Corona Virus (COVID-19) pandemic that has spread throughout the world has created a new work culture, namely working remotely by utilizing existing technology. This has the effect of increasing crime and cyber attacks as more and more devices are connected to the internet for work. Therefore, the priority on security and monitoring of network infrastructure should be increased. The security and monitoring of this infrastructure requires an administrator in its management and configuration. One administrator can manage multiple infrastructures, making the task more difficult and time-consuming. This research implements infrastructure as code for security automation and network infrastructure monitoring including IDS, honeypot, and SIEM. Automation is done using ansible tools to create virtual machines to security configuration and monitoring of network infrastructure automatically. The results obtained are automation processes and blackbox testing is carried out and validation is carried out using a User Acceptance Test to the computer apparatus of the IT Poltek SSN Unit to prove the ease of the automation carried out. Based on the results of the UAT, a score of 154 was obtained in the Agree area with an acceptance rate of 81.05% for the implementation of infrastructure as code for the automation carried out

Implementasi Metode K-Means, Dbscan, Dan Meanshift Untuk Analisis Jenis Ancaman Jaringan Pada Intrusion Detection System

The implementation of network security infrastructure has been carried out, including the Intrusion Detection System (IDS). However, in its implementation there are still many who have not combined with Data Technology (Data Science) to get a more comprehensive analysis. This study aims to analyze the types and characteristics of network threats using data science. As a computational method, the results of 3 algorithms in the unsupervised learning category will be implemented and compared, namely K-Means, Meanshift, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). From the experimental results as measured by the Silhouette Index (SI ) the best cluster of each implemented algorithm is DBSCAN which has the best SI value of 0.3424 with an Eps value of 0.2 and a MinPts value of 3. Meanwhile, from the results of clustering using K-Means, The best SI value was obtained by experiment k=4 with a value of 0.4531. The results of clustering using MeanShift, the best SI value was obtained by experiment bandwidth = 1 with a value of 0.5305.