Computer Network Security Research Articles

A novel MADM technique based on projection methods under q-rung orthopair fuzzy setting and its application to computer network security evaluation

Computer network security evaluation is a basic work to determine the security performance of the network system and implement the network security management. It involves organizational management, network technology, personnel psychology, social environment and other factors. In recent years, with the rapid development of information technology in China, the problem of computer network security has become increasingly prominent. Although domestic and foreign scholars have sought effective methods of network security evaluation from different aspects and using different methods, many factors involved in network security are difficult to quantify, so far, there is no relatively mature quantitative evaluation method of network security. The computer network security evaluation is classical multiple attribute decision making (MADM) problems. In this article, based on projection measure, we shall introduce the projection models with q-rung orthopair fuzzy information. First of all, the definition of q-rung orthopair fuzzy sets (q-ROFSs) is introduced. In addition, to fuse overall q-rung orthopair fuzzy evaluation information, two aggregation operators including q-ROFWA and q-ROFWG operators is introduced. Furthermore, combine projection with q-ROFSs, we develop the projection models with q-rung orthopair fuzzy information. Based on developed weighted projection models, the multiple attribute decision making model is established and all computing steps are simply depicted. Finally, a numerical example for computer network security evaluation is given to illustrate this new model and some comparisons between the new proposed models and q-ROFWA and q-ROFWG operators are also conducted to illustrate advantages of the new built method.

Unleashing the Power of Multi-Agent Deep Learning: Cyber-Attack Detection in IoT

Detecting botnet and malware cyber-attacks is a critical task in ensuring the security of computer networks. Traditional methods for identifying such attacks often involve static rules and signatures, which can be easily evaded by attackers. Dl is a subdivision of ML, has shown promise in enhancing the accuracy of detecting botnets and malware by analyzing large amounts of network traffic data and identifying patterns that are difficult to detect with traditional methods. In order to identify abnormal traffic patterns that can be a sign of botnet or malware activity, deep learning models can be taught to learn the intricate interactions and correlations between various network traffic parameters, such as packet size, time intervals, and protocol headers. The models can also be trained to detect anomalies in network traffic, which could indicate the presence of unknown malware. The threat of malware and botnet assaults has increased in frequency with the growth of the IoT. In this research, we offer a unique LSTM and GAN-based method for identifying such attacks. We utilise our model to categorise incoming traffic as either benign or malicious using a dataset of network traffic data from various IoT devices. Our findings show how well our method works by attaining high accuracy in identifying botnet and malware cyberattacks in IoT networks. This study makes a contribution to the creation of stronger and more effective security systems for shielding IoT devices from online dangers. One of the major advantages of using deep learning for botnet and malware detection is its ability to adapt to new and previously unknown attack patterns, making it a useful tool in the fight against constantly evolving cyber threats. However, DL models require large quantity of labeled data for training, and their performance can be affected by the quality and quantity of the data used. Deep learning holds great potential for improving the accuracy and effectiveness of botnet and malware detection, and its continued development and application could lead to significant advancements in the field of cybersecurity.

STUDI KASUS IMPLEMENTASI METODE BLOCKING PORT DALAM PENCEGAHAN MALWARE

Computer networks have become a staple in various fields. connected to the internet will increase the likelihood of threats or disruption to system security. Malicious Software (malware) is a program that is created with the aim of finding weaknesses or even damaging software and computer operating systems, they can enter through open ports that are not used in the network. After the author made observations at the XYZ School and conducted literature studies from various sources, the Router Mikrotik was needed to prevent this and the Dionaea Honeypot as a malware detection tool on the local network. This study aims to build a computer network security system with blocking port method at XYZ School by using observation, literature study and analysis methods. After the completion of this research, the security system of the XYZ School network can maximize system performance, prevent the entry of Malicious Software (malware).

Information security of hospital computer network based on SAE deep neural network

Abstract As the pace of hospital informatization construction continues to accelerate, information network technologies are being used more and more extensively in the medical industry. These advanced technologies make medical businesses more and more dependent on industry information and data, which brings about network system security issues that cannot be ignored. To strengthen the daily operation and management of hospitals, ensure the stable operation of computer systems, and do a good job in protecting the security of hospital computer system network information, this paper designs a risk assessment method for hospital computer network information security based on SAE deep neural network and analyzes the main factors affecting the security of hospital computer system network information. The experimental results prove that the proposed method can effectively improve the reliability of the evaluation results and ensure the accuracy of the evaluation results. According to the obtained information security model, it can effectively guide the construction and application of hospital computer network information systems, optimize the system network, and promote the development of hospital informatization.

Retracted: Computer Network Security Management of Data Encryption Technology

Retracted: Computer Network Security Management of Data Encryption Technology

Computer network security and information security technology optimization by using Bayesian classification algorithm

Computer network security and information security technology optimization by using Bayesian classification algorithm

AWC‐NIDS: Attack‐wise customized network intrusion detection system using machine learning, concurrency, and distributed systems

SummaryWith digitization and modern network applications, information security has gained a tremendous importance. Therefore, accurate and efficient detection systems are crucial for maintaining proactive security in computer networks. Machine learning (ML) has shown great potential as a promising solution since it can teach a machine to distinguish malicious and normal network activities. However, recently proposed methods are suffering from at least one of the following: detection accuracy, false alarm rate, and computational complexity issues. The main reason behind this problem is the complexity of the model in terms of attack types. From the ML perspective, intrusion detection is a classification problem where each attack type is identified by a set of different features, and features are used for classifying network activities. Thus, training an ML algorithm to detect more than one attack type leads to a more complex model; the increasing number of used features contributes positively to the model complexity, and may result in relatively lower detection accuracy or a higher false positive rate. To tackle this problem, this study proposes an attack‐wise customized network intrusion detection system (AWC‐NIDS) based on ML, concurrency, and distributed systems to achieve accurate and efficient network‐wide intrusion detection. Since CICIDS2017 contains many modern attacks, it was used for model development and performance evaluation. The experimental results showed that the proposed methodology achieved high classification performance for all datasets with a small number of features. However, it was observed that the lowest accuracy was achieved for the comprehensive dataset (which contains all attack types); for the single attack‐type datasets, the obtained accuracy was above 99%. This finding proves the concept of attack‐wise customization for intrusion detection and shows the significance of the proposed methodology. In conclusion, this framework is promising for implementing robust and accurate cybersecurity systems for traditional and modern networking.

Data security in smart devices: Advancement, constraints and future recommendations

AbstractNetwork security protocols are implemented to address network security challenges. Computer networks and applications have advanced and developed significantly in recent years, but consumers ‘excitement for network technology and high‐tech devices has been dampened by continual exposure to data security vulnerabilities. As of now, some individuals refuse to use smart devices due to concerns about the authenticity, confidentiality and integrity of data security leaks. This not only prompts Internet service providers to follow market protection mechanisms but also requires software developers to apply appropriate security protocols to protect computer network security. These applications’ dependability and integrity are dependent not just on the effectiveness of cryptographic algorithms, but also on key management protocols. Understanding network security protocols and implementing high‐quality standards to govern the transmission of data in the network are critical components of guaranteeing network security. The article explores data security, primarily at the application layer, various attack methods for different network security protocols and highlights the potential security implications. The study also looks at the corresponding, practical security measures and future research prospects for certain kinds of attacks. Finally, some technical challenges that remain unsolved at the time of writing are summarised, and future trends in cybersecurity are discussed.

Educational Scenario for Teaching Cyber Security Using low-cost Equipment and Open Source Software

This work presents a set of hands-on educational activities designed to teach some cyber security concepts in the classroom. The experimental configuration used an ad-hoc wireless and wired network, and a Raspberry Pi implementing a Web and an SSH server. Students were connected using their own devices (laptops or tablets). Initially the students tested DoS attacks to the Web server using various tools. Next, the students had to create SSH accounts to the server and a pair of RSA keys; using their SSH accounts, the students had to transfer their public keys to the server. Finally, students had to attack the SSH service from Kali Linux running on virtual machines in teams, each team using a different tool. The scenario was implemented in parts during a series of lessons and was positively accepted and evaluated by the students, who got familiar with a number of concepts and tools of computer networking and network security. In the end, the students informally assessed the Kali Linux SSH attack tools. Ways to assess the students qualitatively and quantitatively based on their participation are also presented. These lab exercises used a series of open source software, as well as low-cost equipment.

Deep learning technology of computer network security detection based on artificial intelligence

With the continuous progress of the information age and science and technology, how to combine complex and changeable data with self-protection ability has become an urgent problem that needs to be solved. In the computer system, the traditional technology for protection is somewhat passive. To this end, this paper analyzes computer network security monitoring under artificial intelligence (AI) deep learning technology and discusses its development trend, which monitors network security to find and address problems in time. The results show that the deep learning technology of computer network security detection can improve its security performance with high safety performance, high detection rate, and low false alarm rate. It can monitor the vulnerabilities in the computer network in time and realize effective security attack detection of the computer network.

Fog computing network security based on resources management

Fog computing paradigm is designed as an extension of cloud computing due to the need for a supporting platform that is capable of providing the requirements of the Internet of Things (IoT). However, due to its features, fog obviously confronts numerous security and privacy risks, such as huge scale geolocation, heterogeneity, and mobility. Indeed, there are many problems resulting from security violations and breaches. Thus, to exceed these problems, we propose an efficient access control system, ameliorated with appropriate monitoring function and risk estimation to detect abnormal user’s behavior and then deactivating illegitimate anomaly actions. Indeed, based on the risk value, we compute the trust level that will then be made into an access certificate, which would be provided to the user. This security certificate is used to authenticate and authorize users in case of re-connection in another time, without repeating the whole access control process from the beginning. Moreover, a comprehensive resource management mechanism is proposed to ameliorate the system performance and so to maintain low latency. Our aim is to further enhance data security, privacy and resource management for IoT users. To demonstrate the efficiency, feasibility, and security of our proposed scheme, we perform an extensive simulation using Network Security Simulator (Nessi2).

Research on Computer Network Security Protection Technology Incorporating Full Convolutional Networks

Abstract To strengthen networks’ security performance, here pose suggests a fused full convolutional approach to monitoring computer networks. This paper first analyzes the various performances of the full convolutional model for error problems rate, error reporting, and monitoring effects on various attack categories then proposes a network monitoring scheme for the full convolutional model and introduces the workflow of the full convolutional model in computer network security protection. In terms of accuracy, the full convolutional error rate of the blueprint meets the requirements rate of 96.8%, which is better than the classical network models of Lenet-5 and AlexNet, with 86.2% and 91.6%. The false alarm rate is only 2.37%, which is lower than the 5.74% MLP algorithm and 4.23% SVM algorithm. By comparison, the full convolutional calculation method is more efficient than other calculation methods in the detection rate of attack types such as Dos, Probe, U2R, and R2L. Therefore, the calculation method here is well adapted to computer network security protection requirements.

Analysis on Hacking the Secured Air-Gapped Computer and Possible Solution

Abstract The world today runs on data, every minuscule task to the large one requires data. All the data is stored in the various technologies that we use. And to keep data safe, air gaps are introduced. Air gaps are a network security measure where secure computer networks are physically isolated from unsecured networks. Yet, different methods to hack the air gap have come forth. The paper analyzes the problem of hacking an air gap via screen brightness modulations. The proposed solution is a software program used to alert the user of a change in the brightness level of the screen. The concept of Windows Management Instrumentation (WMI) has been used to put forth the software. Applied to an air-gapped computer, the program displays an alert box immediately, as the screen brightness changes. The solution is an easy and efficient way to counter the attack. The program can be further implemented in different testing environments and the WMI concept can be applied to various other cyber hacks.

Employing Deep Ensemble Learning for Improving the Security of Computer Networks Against Adversarial Attacks

In the past few years, Convolutional Neural Networks (CNN) have demonstrated promising performance in various real-world cybersecurity applications, such as network and multimedia security. However, the underlying fragility of CNN structures poses major security problems, making them inappropriate for use in security-oriented applications, including computer networks. Protecting these architectures from adversarial attacks necessitates using security-wise architectures that are challenging to attack. In this study, we present a novel architecture based on an ensemble classifier that combines the enhanced security of 1-Class classification (known as 1C) with the high performance of conventional 2-Class classification (known as 2C) in the absence of attacks. Our architecture is referred to as the 1.5-Class (cmb-classifier) classifier and is constructed using a final dense classifier, one 2C classifier (i.e., CNNs), and two parallel 1C classifiers (i.e., auto-encoders). In our experiments, we evaluated the robustness of our proposed architecture by considering eight possible adversarial attacks in various scenarios. We performed these attacks on the 2C and cmb-classifier architectures separately. The experimental results of our study showed that the Attack Success Rate (ASR) of the I-FGSM attack against a 2C classifier trained with the N-BaIoT dataset is 0.9900. In contrast, the ASR is 0.0000 for the cmb-classifier.

Detection Suspicious Activities on Network Package Traffic

One problem of computer network security was that unauthorized people had access to websites on the internet—the way to interject malicious programs that aim to send viruses and even commit data theft. Therefore, it was critical to understand the types of attacks in the different forms of crime that can not only harm organizational infrastructure but also affect financially. Detection of criminal activities was carried out with the help of Wireshark Software to view data packets, which indicated criminal programs carried out by intruders secretly on the official website. The capture packets on the running network had suspicious packets as evidence that led to malware infections. Therefore, both the owner and website users needed security protection by planning strategies to overcome criminal activities that infect websites.
 Keywords: malicious programs, malware, network package traffic, Wireshark

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.

Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment

Artificial neural networks (ANN), fuzzy inference systems, approximate reasoning, and derivative-free optimization approaches like evolutionary computation, among others, are all components of Soft Computing (SC), an innovative method for building computationally intelligent methods. Due to its widespread use and benefits, cloud computing is currently a major focus for researchers. The distributed nature of cloud computing and its complete reliance on the internet for service provision present security challenges, the most serious of which is insider Distributed Denial of Service (DDoS) which results in complete deactivation of services. Based on machine learning and the cloud, this study proposes a novel method for improving computing network security. Using a trust-based secure cloud environment and the Kernel principal component encoder architecture, the network monitoring is carried out. The soft computing environment-based detection of a cyberattack on the network is the purpose for the security enhancement. Throughput, QoS, latency, and packet delivery ratio for various monitored cyber security datasets are the focus of the experimental analysis. The proposed technique attained network security analysis of 89%, throughput of 98%, QoS of 66%, latency of 59%, packet delivery ratio of 83%.

Perbandingan Kriptography Klasik Caesar Cipher Dengan Kriptography Modern Aes Dalam Tingkat Keamanan Jaringan Komputer

Cryptography (cryptography) is the science and art of keeping messages safe. The sender of the message will encrypt it while the recipient of the message will decrypt it. The encryption and decryption process uses keywords that have been agreed upon by the sender and recipient of the message. To better understand the development of cryptography, this journal will introduce a comparison of classical cryptography and modern cryptography at the level of computer network security. The cryptography that will be compared is Caesar Cipher and AES. This study uses the method of literature review (library research), namely research based on expert opinion and the results of previous studies.
 

Information Security Problems of Computer Networks

The article deals with the problems of information security of computer networks and the main functions of the information security system.

Assessment methodology for security of an automated control system of critical information infrastructure against DDoS attacks based on Monte Carlo simulation

Objective. The purpose of the study is to develop a methodology for assessing the security of an automated control system of critical information infrastructure from DDoS attacks. The purpose of the methodology development is to provide the decision–maker with a scientifically sound tool for assessing the risk of implementing a DDoS attack.Method. To achieve the stated goal of the study, simulation modeling based on the Monte Carlo method was used.Result. The expediency of using Monte Carlo simulation to assess the probability of server failure under DDoS attacks is confirmed. It was concluded that the server can be considered as a queuing system, however, the flow of incoming applications under DDoS attacks is not Poisson, so the use of analytical expressions to assess the probability of failure is considered incorrect. The simulation results allow the decision-maker to assess the probability of server failure and make organizational and technical decisions to increase the level of security. Analysis of the simulation results showed the effectiveness of improving server performance by increasing service channels.Conclusion. Thus, the developed methodology will be useful in conducting an information security audit of an organization to justify the amount of its insurance premium in the framework of cyber risk insurance. A possible direction for further research is to study the issue of computer network security, taking into account the features of a specific topology.