Journal of Computer Networks and Communications

In today’s security landscape, advanced threats are becoming increasingly difficult to detect as the pattern of attacks expands. Classical approaches that rely heavily on static matching, such as blacklisting or regular expression patterns, may be limited in flexibility or uncertainty in detecting malicious data in system data. This is where machine learning techniques can show their value and provide new insights and higher detection rates. The behavior of botnets that use domain-flux techniques to hide command and control channels was investigated in this research. The machine learning algorithm and text mining used to analyze the network DNS protocol and identify botnets were also described. For this purpose, extracted and labeled domain name datasets containing healthy and infected DGA botnet data were used. Data preprocessing techniques based on a text-mining approach were applied to explore domain name strings with n-gram analysis and PCA. Its performance is improved by extracting statistical features by principal component analysis. The performance of the proposed model has been evaluated using different classifiers of machine learning algorithms such as decision tree, support vector machine, random forest, and logistic regression. Experimental results show that the random forest algorithm can be used effectively in botnet detection and has the best botnet detection accuracy.

Ali Soleymani

Fatemeh Arabgol

A Novel Approach for Detecting DGA-Based Botnets in DNS Queries Using Machine Learning Techniques

一种利用机器学习技术检测基于DGA的僵尸网络DNS查询的新方法

With the growing elderly population, ensuring consistent and reliable healthcare monitoring has become increasingly important. This research introduces an innovative solution in the form of a smart glove designed specifically for elderly care. The glove is equipped with advanced features such as fall detection, emergency messaging via hand gestures, and vital signs monitoring, including oxygen saturation (SpO 2 ) and heart rate. The smart glove integrates the MPU6050 and MAX30100 sensors with Arduino and ESP8266 modules to achieve these functionalities. Fall detection is accurately identified by the MPU6050 sensor, while the MAX30100 sensor monitors SpO 2 and heart rate. Hand gestures are recognized and interpreted for sending emergency messages. Data from these sensors are transmitted through IFTTT, enabling alerts and notifications via SMS, email, and a secured website, thus facilitating real‐time remote monitoring and communication. This setup provides a comprehensive and reliable method for monitoring the health and safety of elderly individuals, ensuring prompt response and care in emergencies. The proposed system demonstrates significant potential in enhancing the quality of life for elderly individuals by offering a smart, integrated, and user‐friendly healthcare solution.

Nowshad Hasan

Mohammad Faisal Ahmed

Wearable Technology for Elderly Care: Integrating Health Monitoring and Emergency Alerts

老年人护理中的可穿戴技术：整合健康监测与紧急警报

Human fertility monitoring, whether for natural conception or artificial insemination and fertilization, presents a significant challenge for both physicians and patients. Detecting the presence of eggs in the fallopian tube and timely communication of this information to the healthcare team and the patient remains an elusive goal; in this paper, we propose a novel, hybrid communication architecture for this purpose. Our architecture combines an intrabody nano‐sensor network for detecting the presence of eggs in the female fallopian tube with a body area network (BAN) to receive information from the intrabody network and transmit it over‐the‐air to the required point (physician, nurse, or patient). To investigate the system’s performance, we conducted preliminary simulations using a particle‐based stochastic simulator. The results revealed interesting relationships between information rates achievable, signal‐to‐noise ratio (SNR), distance, number of sensors, detection delay, viscosity, and temperature. Specifically, we found that increasing viscosity led to an increase in detection delay, while increasing temperature had the opposite effect. With data rates of up to 300 Mbps achievable at an SNR of 45, our proposed architecture ensures near‐zero latency, minimum energy consumption, and high throughput for efficient fertility monitoring. By integrating advanced communication technologies and sensor networks, our approach paves the way for improved fertility monitoring, enabling timely interventions and personalized care. These findings contribute to the development of a cooperative intra‐ to extra‐body network that enhances accuracy and enables real‐time monitoring for effective fertility management.

Shama Siddiqui

Anwar Ahmed Khan

Muhammad Shoaib Siddiqui

Indrakshi Dey

Enhanced Accuracy and Real‐Time Monitoring: A Hybrid Communication Architecture for Fertility Monitoring

提高准确性和实时监测：一种用于生育监测的混合通信架构

Vehicular ad hoc networks (VANET) are revolutionizing intelligent transportation systems (ITS), and as a result, research on their security is becoming increasingly important. As the primary security concern for VANET, authentication security is still quite difficult to achieve. Consequently, the prior knowledge of VANET is covered in this survey before outlining the primary security concerns. To set itself apart from previous surveys, this study suggests security properties and challenges among VANET. Next, the essential and significant features of a secure VANET system, such as confidentiality and integrity of data, and the availability of network systems have been reported, the authenticity of nodes and messages, and the refusal to deny data once it has been transmitted is detailed. Later, it outlined the requirement of the ITS which makes the survey unique. More importantly, the report on the most recent developments in VANET concentrates on the authentication schemes that have been proposed recently. The security features and authentication resistance against attacks, along with the overhead and efficiency of these schemes, are thoroughly examined and contrasted. A detailed analysis of V2V, V2I, and V2X authentication is been reported. Various cryptographic schemes have been discussed along with some advanced techniques such as Blockchain and hybrid schemes. An overview of the integration of 5G/6G networks is documented. Applications of VANET have been discussed in detail along with some open challenges in VANET. In summary, this work reviews a few lessons learned and explores different possibilities for future research.

Soujanya B K

Farooque Azam

Ensuring Security and Privacy in VANET: A Comprehensive Survey of Authentication Approaches

确保VANET中的安全与隐私：认证方法的综合调查

Recently, deep learning algorithms and software‐defined networking technologies enabled traffic management to be more controllable in IP networking and mobile Internet to yield quality services to subscribers. Quality of service (QoS) needs more effort to optimize QoS performance. More specifically, elephant flow management is a critical task that needs further research since its heavy hit behavior leads to high CPU utilization, packet drops, high latency, packet overflow, and network congestion problems. For this purpose, we focused on elephant flow management since elephant flows are big flows that hinder good service delivery (QoS) on demand. Hence, elephant flow detection and early prediction techniques optimize QoS. In this regard, we employed DNN and CNN deep learning models to detect elephant flows, and the random forest model predicts elephant flows in the SDN. As a result of our experiments, the findings reveal that deep learning algorithms within the Ryu controller significantly outperform in detecting and predicting performance in order to yield good network throughput. This solution proves to be significant for QoS optimization in data centers.

Getahun Wassie

Jianguo Ding

Yihenew Wondie

Detecting and Predicting Models for QoS Optimization in SDN

检测与预测模型在软件定义网络中服务质量优化的应用

Leaf blight spot disease, caused by bacteria and fungi, poses a considerable threat to commercial plants, manifesting as yellow to brown color spots on the leaves and potentially leading to plant mortality and reduced agricultural productivity. The susceptibility of jasmine plants to this disease emphasizes the necessity for effective detection methods. In this study, we harness the power of a deep convolutional generative adversarial network (DCGAN) to generate a dataset of jasmine plant leaf disease images. Leveraging the capabilities of DCGAN, we curate a dataset comprising 10,000 images with two distinct classes specifically designed for segmentation applications. To evaluate the effectiveness of DCGAN-based generation, we propose and assess a novel loss function. For accurate segmentation of the leaf disease, we utilize a UNet architecture with a custom backbone based on the MobileNetV4 CNN. The proposed segmentation model yields an average pixel accuracy of 0.91 and an mIoU (mean intersection over union) of 0.95. Furthermore, we explore different UNet-based segmentation approaches and evaluate the performance of various backbones to assess their effectiveness. By leveraging deep learning techniques, including DCGAN for dataset generation and the UNet framework for precise segmentation, we significantly contribute to the development of effective methods for detecting and segmenting leaf diseases in jasmine plants.

Shwetha V.

Arnav Bhagwat

Vijaya Laxmi

Sakshi Shrivastava

A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant

一种自定义骨干UNet框架与DCGAN增强用于高效分割茉莉花植物叶斑病

This article explores the growing importance of the QoE (Quality of Experience) metric as a fundamental criterion in the optimization of heterogeneous multimedia networks. We explore the benefits of using QoE, such as improving user experience, efficient resource management, and adaptation to network conditions. However, this paradigm presents technical challenges, including accurately measuring QoE and managing the complexity of heterogeneous networks. To address these challenges, we highlight promising research prospects, such as the development of advanced algorithms, real-time measurement of QoE, and the integration of machine learning. Ultimately, the use of QoE in the optimization of heterogeneous multimedia networks contributes to a substantial improvement in the quality of multimedia services offered to end users.

M. Hamidou Harouna Omar

Kouraogo Justin P.

Kabre Windmi Jonathan M.

Tapsoba Stanislas David Wendkouni

Sie Oumarou Pr

Using QoE Metric as a Decision Criterion in Multimedia Heterogeneous Network Optimization: Challenges and Research Perspectives

在多媒体异构网络优化中使用QoE指标作为决策标准：挑战与研究展望

Blockchain is a very secure, authentic, and distributed technology and is very prominent in areas such as edge computation, cloud computation, and Internet-of-things. Artificial intelligence assists in the completion of activities efficiently and effectively by providing intelligence, analytics, and predicting capabilities. There is an obvious convergence between the two technologies. Artificial intelligence systems can utilize blockchain to establish trust in communication channels, ensuring that messages are securely transmitted and received without the need for a centralized intermediary. By leveraging blockchain, artificial intelligence systems can maintain an immutable record of communications, ensuring transparency and preventing unauthorized modifications. The integration of blockchain and artificial intelligence technologies can enhance the security, transparency, and privacy of communication systems. By leveraging blockchain’s decentralized nature and artificial intelligence’s analytical capabilities, secure and trustworthy communication channels can be established, benefiting various domains such as finance, healthcare, and supply chain. Overall, the integration of blockchain and artificial intelligence has the potential to offer several benefits, and as these technologies continue to evolve, new and innovative applications will continue to emerge.

Kamal Kumar

Vinod Kumar

None Seema

Mukesh Kumar Sharma

Akber Ali Khan

M. Javed Idrisi

A Systematic Review of Blockchain Technology Assisted with Artificial Intelligence Technology for Networks and Communication Systems

区块链技术与人工智能技术辅助网络与通信系统的系统综述

Data offloading is considered as a potential candidate for alleviating congestion on wireless networks and for improving user experience. However, due to the stochastic nature of the wireless networks, it is important to take optimal actions under different conditions such that the user experience is enhanced and congestion on heavy-loaded radio access technologies (RATs) is reduced by offloading data through lower loaded RATs. Since artificial intelligence (AI)-based techniques can learn optimal actions and adapt to different conditions, in this work, we develop an AI-enabled Q-agent for making data offloading decisions in a multi-RAT wireless network. We employ a model-free Q-learning algorithm for training of the Q-agent. We use stochastic geometry as a tool for estimating the average data rate offered by the network in a given region by considering the effect of interference. We use the Markov process for modeling users’ mobility, that is, estimating the probability that a user is currently located in a region given its previous location. The user equipment (UE) plays the role of a Q-agent responsible for taking sequence of actions such that the long-term discounted cost for using network service is minimized. Q-agent performance has been evaluated and compared with the existing data offloading policies. The results suggest that the existing policies offer the best performance under specific situations. However, the Q-agent has learned to take near-optimal actions under different conditions. Thus, the Q-agent offers performance which is close to the best under different conditions.

Murk Marvi

Adnan Aijaz

Anam Qureshi

Muhammad Khurram

Development of an AI-Enabled Q-Agent for Making Data Offloading Decisions in a Multi-RAT Wireless Network

多无线接入技术网络中基于AI的Q-Agent数据卸载决策开发

Abbreviation	J. Comput. Networks Commun.
Journal Impact	2.14
Quartiles(Global)	TELECOMMUNICATIONS(Q3)

ISSN	2090-7141, 2090-715X
h-index	31

Publication Information	Publisher: Hindawi Limited，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2011，Proportion of original research papers: 77.78%，Self Citation Rate:0.00%， Gold OA Rate: 97.30%
Journal Fees	$1490Details
Average review cycle	网友分享经验：15 Weeks

Journal of Computer Networks and Communications

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A Novel Approach for Detecting DGA-Based Botnets in DNS Queries Using Machine Learning Techniques

Wearable Technology for Elderly Care: Integrating Health Monitoring and Emergency Alerts

Enhanced Accuracy and Real‐Time Monitoring: A Hybrid Communication Architecture for Fertility Monitoring

Ensuring Security and Privacy in VANET: A Comprehensive Survey of Authentication Approaches

Detecting and Predicting Models for QoS Optimization in SDN

A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant

Using QoE Metric as a Decision Criterion in Multimedia Heterogeneous Network Optimization: Challenges and Research Perspectives

A Systematic Review of Blockchain Technology Assisted with Artificial Intelligence Technology for Networks and Communication Systems

Development of an AI-Enabled Q-Agent for Making Data Offloading Decisions in a Multi-RAT Wireless Network

Wearable Technology for Elderly Care: Integrating Health Monitoring and Emergency Alerts