Computer Networks Research Articles

Paving the way towards safer and more efficient maritime industry with 5G and Beyond edge computing systems

The Maritime and Road Transport and Logistics (T&L) vertical industries account for 92.5% of the European total freight transport. Therefore, efficient T&L systems in busy port environments are critical for Europe’s global competitiveness. The emergence of 5G and Beyond, including Standalone (SA) with data rates up to 20 Gbps, latencies as low as 5 milliseconds (ms), and exceptional reliability (99.999%), offers a significant opportunity for innovation in the Maritime sector, in terms of smarter and safer operations. In this paper, we investigate the enhancements brought forth by Edge Network-aware Applications (EdgeApps) strategically positioned at the network edge, focusing on a practical maritime use case denominated as “Assisted Vessel Navigation”, all within the overarching framework of the edge-cloud/core continuum paradigm. We utilize the so-called edge network softwarization and intelligence that allows us to create EdgeApps, capable of running on 5G and Beyond-enabled edge and cloud infrastructure to increase the safety and efficiency of maritime operations. The EdgeApp framework simplifies the creation of complex 5G and Beyond vertical services and fosters collaboration between industry stakeholders, network experts, and EdgeApp developers. The contributions of this paper are threefold: i) this paper focuses on the significance of edge computing systems within the 5G ecosystem and their role in enhancing vertical industries such as transport and logistics (T&L), with a specific focus on maritime applications, ii) the core contribution of this paper lies in the design and implementation of EdgeApps tailored to vertical industries. We explore their deployment in a real-life maritime environment, and iii) we share valuable insights derived from our real-life experiments conducted in the Port of Antwerp-Bruges, analyzing network and service performance results based on the use case requirements. Therefore, we examine the deployment options of EdgeApps, comparing edge versus cloud environments to assess where they need to run in the edge-cloud continuum. The outcomes and lessons learned from this article hold important implications for vertical industries.

Integration of computer networks and artificial neural networks for an AI-based network operator

Integration of computer networks and artificial neural networks for an AI-based network operator

Multi-objective optimization for vector quantization via genetic algorithm

The issue of image compression continues to be a subject of ongoing research within the domain of image processing, particularly in the context of medical applications. The quality of the decompressed image can vary depending on the accuracy of the compression technique, resulting in either fine or distorted details. Therefore, the diagnostic procedure performed by medical professionals is contingent upon the precision of the compression and decompression process. In addition, the compression of medical images serves to decrease the amount of storage required, thereby enabling faster transmission over computer networks through the reduction of their bit size. This paper proposes a hybrid mix of the discrete wavelet transform (DWT) technique and vector quantization (VQ) to improve the compression technique of medical images. The aim of the proposed compression technique is to preserve diagnostic image information while achieving a high compression ratio. First, noise in medical images is caused by salt pepper noise. At the same time, the edges of the images are maintained in sharpness and context. Then, a lossless compression method is applied to the wavelet coefficients of the subband with the lowest frequency, while the thresholding method was used to efficiently construct coefficients for high-frequency sub-bands. This process will produce a traditional VQ, which is estimated via the Genetic Algorithm (GA) with fuzzy clustering. While Arithmetic encoded theory was being utilized to quantize coefficients, the proposed compression technique was evaluated by dividing the image into two levels and three levels of sub-bands, respectively, in two different scenarios. Comparing the decompressed image to the filtered image by means of different evaluation metrics, the proposed method can enhance compression performance and strike a balance between compression ratio and image visual quality.

Botnet detection based on Markov chain and Fuzzy rough set

Botnets now make up a wide range of cyber-attacks, which are a network of infected computers connected to the Internet, with remote control. So far, a lot of research has been done in this field, the proposed methods are based on the signatures of discovered botnets, anomalies, traffic behavior, and addresses. Each method has both advantages and disadvantages. This research proposes a structure for performing identification operations, which is presented in this research based on the Markov chain and is based on behavioral analysis. A disadvantage of the past methods is the inability to receive network information at a very high speed. In this research, it has tried using a solution to receive traffic at a very high speed of about 40 Gbps and analyze it. To be able to perform the analysis with a lower overhead. The proposed method can investigate the behavior of botnets by examining the area of behavior better than the previous solutions, and as a result, during the solutions used by botnets to hide their behavior, it can counter and identify suspicious flows. The accuracy of the proposed method was found to be 96.170%. DOI: https://doi.org/10.52783/pst.390

An improved lightweight and privacy preserving authentication scheme for smart grid communication

Smart grid is an efficient and reliable technical framework for controlling computers and automation equipment, and how to ensure the communication security in smart grid is an important issue. Cryptographic authentication scheme is a feasible solution, and the existing authentication schemes for smart grid seek to ensure better performance. Some existing authentication schemes lack comprehensive security considerations and have security or privacy vulnerabilities, which makes them vulnerable to specific attacks. The paper reviews a recent scheme ISG-SLAS (Yu and Park, 2022) and analyzes its potential insecure aspects in detail, including unable to resist ESL attack, cannot provide un-traceability and etc. To this end, the paper designs an improved authentication scheme for smart grid based on symmetric cryptography. Through informal security analyses and formal security analyses with real-or-random (ROR) model and Scyther platform, the security of the proposed scheme is demonstrated. From the perspective of performance, the proposed scheme is compared with ten advanced authentication schemes for smart grid, and the results show that the proposed scheme excels other recent schemes in computational overhead, communication overhead and storage overhead, reduced by 10.1%, 30.8% and 36.1% of ISG-SLAS and 58.9%, 64.1% and 24.3% of the average value of all alternatives respectively.

A Thermally Reconfigurable Photonic Switch Utilizing Drop Cast Vanadium Oxide Nanoparticles on Silicon Waveguides

Photonic switches play a vital role in optical communications and computer networks for establishing and releasing connections of optical signals. With the growing demand for ultra‐compact switches in high‐speed optical computing and communications, thermally reconfigurable optical switches have gained significant attention. These switches offer simplicity, ease of fabrication, and leverage a wide range of thermo‐optic materials. Silicon remains an ideal platform for making photonic devices including the switches due to its compatibility with complementary metal‐oxide‐semiconductor (CMOS) technology and cost‐effectiveness. The article presents a drop cast sub‐stoichiometric vanadium oxide (VO2−x) nanoparticles combined with a silicon ridge waveguide to make a compact thermally reconfigurable optical switch with low transition temperature and accelerated phase transition. Furthermore, the design achieves high modulation depth in addition to its scalability and simplicity. This study demonstrates the potential of solution‐based VO2−x nanoparticles in combination with silicon waveguides for efficient optical switch design for various applications.

An efficient intrusion detection technique for traffic pattern learning

Efficient intrusion detection algorithms are required for network traffic learning patterns in order to protect advanced network communication channels. These systems can be used to detect normal and unusual patterns, signatures, and rule violations. In recent years, conventional and deep machine learning algorithms have been utilized in the field of network intrusion detection for network traffic learning systems. The use of machine learning opens up new attack surfaces that are very intriguing to investigate. Attackers can introduce noisy data into training data to influence testing patterns in computer networks. The goal of this work is to create an efficient intrusion detection solution for network traffic learning patterns using a supervised and unsupervised technique. We developed an effective intrusion detection system (IDs) using an appropriate NSLKDD dataset for network traffic patterns. The model was trained and evaluated using the Genetic Optimization Algorithm (GOA) and the Niave Bayesian technique to recognize usual and unexpected network traffic patterns. We created a strategy that begins with a random population and subsequent iterates through the fitness function, returning the best parents with high detection accuracy. The best parents were determined using the n-parameters iterated by the crossover and mutation procedures. A cross over function was created to combine genes from two fitness parents by randomly selecting portions from each parent. The individual components of the crossover offsprings are randomly flipped to achieve the mutation. The fitness of the previous generation was obtained to generate a new generation, and this process was repeated n times. This was created to detect network intrusions using Nave Bayes' binary categorization problem and evolutionary algorithms. We accomplished this task by aggregating noise into training set before broadcasting the average number, and it is critical not to have that public average too frequently. The experimental results reveal that our proposed GA fared better than the NB technique, with a detection accuracy of 95.0% versus a recommendable detection accuracy of 53.0%.

Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network

The field of network security is highly concerned with intrusion detection, which safeguards the security of computer networks. The invention and application of intrusion detection technology play indispensable roles in network security, and it is crucial to investigate and comprehend this topic. Recently, with the continuous occurrence of intrusion incidents in virus propagation networks, traditional network detection algorithms for virus propagation have encountered limitations and have struggled to detect these incidents effectively and accurately. Therefore, updating the intrusion detection algorithm of the virus-spreading network is imperative. This paper introduces a novel system for virus propagation, whose core is a graph-based neural network. By organically combining two modules—a standardization module and a computation module—this system forms a powerful GNN model. The standardization module uses two methods, while the calculation module uses three methods. Through permutation and combination, we obtain six GNN models with different characteristics. To verify their performance, we conducted experiments on the selected datasets. The experimental results show that the proposed algorithm has excellent capabilities, high accuracy, reasonable complexity, and excellent stability in the intrusion detection of virus-spreading networks, making the network more secure and reliable.

Detection of Emergent Anomalous Structure in Functional Data

Motivated by an example arising from digital networks, we propose a novel approach for detecting the emergence of anomalies in functional data. In contrast to classical functional data approaches, which detect anomalies in completely observed curves, the proposed approach seeks to identify anomalies sequentially as each point on the curve is received. The new method, the Functional Anomaly Sequential Test (FAST), captures the common profile of the curves using Principal Differential Analysis and uses a form of CUSUM test to monitor a new functional observation as it emerges. Various theoretical properties of the procedure are derived. The performance of FAST is then assessed on both simulated and telecommunications data.

Intelligent Prevention of DDoS Attacks using Reinforcement Learning and Smart Contracts

(Distributed) Denial-of-Service (DoS/DDoS) attacks are among the most dangerous cybersecurity threats to computer networks. Lately, blockchain and artificial intelligence (AI) cyberdefense applications have successfully been implemented to identify attack patterns. This paper proposes a novel collaborative, blockchain-based multi-agent reinforcement learning (RL) cyberdefense method using smart contracts. Initial numerical experiments have shown that the agents quickly learn to predict attacks, which can lead to mitigating network-wide service disruptions.

The Applied Research of E-commerce Technology from the Perspective of Communication Studies

The Applied Research of E-commerce Technology from the Perspective of Communication Studies

Penerapan VPN Dalam Topologi Star Untuk Keamanan Pengiriman Data

Building a computer network needs significant care in selecting a configuration to ensure its efficacy and efficiency. It involves decisions about network topology, types, and device use. Security measures are essential for protecting networks with internet access from external attacks. A study conducted at an Islamic university in Indonesia indicated the use of a star network architecture and a client-server network model. The servers utilize Debian Linux. The university's security infrastructure includes a server authentication system and the setting up of a Virtual Private Network (VPN). If the server authentication difficulty is caused by a power outage, it is recommended that you use an UPS (uninterruptible power supply) to preserve power stability. Overall, security methods show up to be more effective when firewalls are installed.

Mobile Fog AI for Internet of BattleField Things (IoBT)

New mechanisms provide soldiers automation capabilities that include interactive, mobile, fast, scalable, secured, powerful computational communication in contested environments such as battle fields. Internet of BattleField Things (IoBT) is a technology that develops integrated Internet of Things (IoT) solutions for the war of the future. The IoBT connects soldiers with smart technology in armour, radios, weapons, munitions, other objects such as health sensors, GPS, and various IoT devices. These ``things'', objects, or constrained devices can communicate with each other or across existing network infrastructure such as the Internet or network meshes. They most of the time come with limited capacity in terms of memory storage, computational power and energy. IoT, networking (Device Communication (D2D), Peer to Peer Communication (P2P)), machine intelligence, multimodal sensing and fusion are some areas involved in developing interdisciplinary IoBT networks. Our aim is to equip soldiers with AI powered IoBT devices. Existing D2D/P2P/LPWAN based IoBT technology focuses on edge computing devices which are very limited, being slow, insecure, and lack sufficient scalability. We present a novel concept, i.e., Mobile Fog AI for IoBT to overcome existing IoBT limitations.

Enhancing adversarial robustness for deep metric learning via neural discrete adversarial training

Due to the security concerns arising from adversarial vulnerability in deep metric learning models, it is essential to enhance their adversarial robustness for secure neural network software development. Existing defense strategies utilize adversarial triplets to enhance adversarial robustness but sacrifice benign performance. This paper proposes a novel framework for enhancing adversarial robustness and maintaining benign performance by introducing the concept of Neural Discrete Adversarial Training (NDAT) for deep metric learning. NDAT employs VQGAN to transform the adversarial triplets into discrete inputs and then minimizes metric loss function on discrete adversarial triplets. NDAT aligns discrete adversarial examples more closely with clean samples, significantly reducing distribution deviation from their clean counterparts. Moreover, the visual explanations reveal that NDAT maintains consistent attention maps between benign and adversarial triplets and concentrates on structure details and object location perturbations. To demonstrate the effectiveness of our approach, we combine NDAT with popular adversarial methods under various perturbation iterations and intensities. Experiment evaluations on three benchmark databases illustrate that our proposed framework for deep metric learning significantly outperforms state-of-the-art defense approaches in terms of both adversarial robustness and benign performance.

Investigating Lexical and Syntactic Differences in Written and Spoken English Corpora

This paper presents an analysis of the differences between written text and the transcription of spoken text using current Natural Language Processing (NLP) methods. The purpose of the study is to investigate the long and rich history of attempts to differentiate spoken and written text in fields such as linguistics, communication, and rhetoric, which date back to the early 20th century. Given the availability of large quantities of machine-readable data and machine learning algorithms that can handle them, it is possible to use a large number of derived features. The research focuses on syntactic and lexical differences in written books and transcriptions of speeches by United States presidents. The analysis investigates morphological, lexical, syntactical, and text-level aspects. In this process, multiple features have been considered including lexical diversity, syllable count, frequency of parts of speech, and features relating to the parse tree, like the average length of noun phrases, and the use of interrogative sentences, among others. This study will enhance our understanding of the difference between written text and the transcription of spoken text in various disciplines including computer science, applied linguistics, communication, and similar fields.

A Review on Real-Time Network Traffic Monitoring and Anomaly Detection System : A Comprehensive Study with User-Friendly Interface and Historical Analysis Capabilities

Protecting the integrity and security of computer networks is crucial in an era of all-pervasive digital connectedness. The user-friendly interface and powerful historical analytical capabilities of the Real-Time Network Traffic Monitoring and Anomaly Detection System are highlighted in this review paper's thorough investigation of the system. This technology takes the stage in a setting where conventional security measures frequently fall short against developing threats that blend in with regular network traffic. It provides experienced administrators and newcomers with real-time insights into network traffic. Additionally, its skills for historical analysis come in handy for post-incident investigations and boosting general network security. This assessment emphasizes the system's crucial function in thwarting existing and new network threats, establishing it as a cornerstone of successful cybersecurity plans for both businesses and private citizens.

DETECTION OF WIRELESS NETWORK ATTACKS USING SUPERVISED MACHINE LEARNING TECHNIQUE

The security and integrity of computer networks are seriously threatened by network assaults. Keeping a safe network environment requires the capacity to anticipate and stop these threats. Supervised machine learning methods have become powerful instruments for attacking network traffic and spotting patterns that point to malicious behaviour. We provide an in-depth examination of supervised machine learning methods for network attack prediction. We gather the data, preprocess it, extract pertinent features, and structure it so that machine learning algorithms may use it.We assess these algorithms' performance. To understand the fundamental patterns and traits of network assaults, we look at how interpretability the trained models are. This enables network managers to comprehend the types of threats and create suitable. Keywords - Network Attacks, Supervised Machine Learning, Network security,Network traffic analysis, Malicious behavior detection, Feature extraction,Machine learning algorithm,Model interpretability,Network attack patterns,Network defense strategies,Naive Bayes Algorithm, Ridge Classfier ,Random Forest Classfier

Digital Strategies Supporting Social Network Approaches to HIV Testing: A Scoping Review.

This review captured how digital strategies support social network approaches to promote HIV testing. Overall, 29 studies were identified by searching PubMed and Embase for studies published up to June 2023. Existing studies revealed three types of digital strategies (social media (n = 28), online information channels (n = 4), and multifunctional digital platforms (n = 4)) split into four major modes of digital strategy-supported social-network-based HIV testing promotion: 1) Online outreach and recruiting, 2) gathering and identifying key populations for HIV testing, 3) communicating and disseminating online HIV testing health interventions, and 4) assisting and facilitating HIV testing uptake and distribution. Social network approaches supported by digital strategies yielded advantages in HIV testing education and distribution, which increases HIV testing coverage among key populations. Studies are needed on how to facilitate the use of digital strategies for social network-based HIV testing, as well as how to integrate them with existing HIV testing approaches.

Application of network data security based on wireless sensor technology in the development of digital sharing economy

Nowadays, when the existing algorithms are fused in the feature function network feature, they do not consider the different effects of different features on the saliency detection task. In order to solve this problem, this paper proposes a saliency feature fusion network based on multi-focus mechanism to complete the saliency detection task. While recognizing the important role of the network, it must also be realized that because most network users do not understand the details of network composition and network system analysis, the security of computer network data is relatively low. Unscrupulous hackers use their network technology and software to invade unauthorized networks and hosts without authorization. Therefore, network data security is very important. The competition and monopoly of the digital sharing economy platform has always been a hot topic of increasing attention from domestic and foreign economists. The platform economy is based on an efficient data collection and transmission system, a digital platform supported by continuously developing computing power and powerful data processing algorithms, which greatly promotes the development of social productivity. The core trend of the development of China's digital economy is the ecologicalization of the digital sharing economy. Small industrial platforms rely on massive user resources collected by a large platform to quickly connect the upper and lower channels of the industry to transform and form a new industry ecosystem.

Weak signal detection method based on nonlinear differential equations

With the rapid development of computer network technology, it is often necessary to collect weak signals to collect favorable information. The development of signal detection technology is ongoing; however, various issues arise during the detection process. These issues include low efficiency and a high signal noise threshold. However, many problems will be encountered in the process of detection. In order to solve these problems, the nonlinear chaos theory is introduced to detect signals, and the simulation experiments of weak pulse signals and weak partial discharge signals are carried out respectively. The experimental results showed that the detection effect was remarkable in the quasi periodic state, and it had a good detection effect for weak pulse signals. At a signal-to-noise ratio of -25 dB, double coupling system, two-way ring coupling system, and single ring coupling system displayed detection success rates exceeding 98%. Meanwhile, the detection success rate of the strong coupling system was only 12%. Even at a noise signal ratio as low as -40 dB, the dual coupling system still maintained a detection success rate above 80%. The simulation results of partial discharge signal detection showed that there was a high fluctuation only at 2 ms, and the rest was basically stable at about 0 V, indicating that the system had a strong suppression effect on Gaussian white noise. When comparing the simulation results of the detection of the new chaotic system and the double coupling system, it was found that the new chaotic system has a superior impact in detecting weakly attenuated partial discharge signals. Through analysis of the system’s dynamic behavior, the research confirms its rich dynamic characteristics and sheds light on the reasons for phase state mutation and missed detection. The noise system is utilized for comparing the performance of various systems, with the goal of enhancing the system’s detection capability.