Journal of Network and Computer Applications最新文献

{"title":"Blockchain-inspired intelligent framework for logistic theft control","authors":"Abed Alanazi ,&nbsp;Abdullah Alqahtani ,&nbsp;Shtwai Alsubai ,&nbsp;Munish Bhatia","doi":"10.1016/j.jnca.2024.104055","DOIUrl":"10.1016/j.jnca.2024.104055","url":null,"abstract":"<div><div>The smart logistics industry utilizes advanced software and hardware technologies to enhance efficient transmission. By integrating smart components, it identifies vulnerabilities within the logistics sector, making it more susceptible to physical attacks aimed at theft and control. The main goal is to propose an effective logistics monitoring system that automates theft prevention. Specifically, the suggested model analyzes logistics transmission patterns through secure surveillance enabled by IoT-based blockchain technology. Additionally, a bi-directional convolutional neural network is employed to evaluate real-time theft vulnerabilities, aiding optimal decision-making. The proposed method has been shown to provide accurate real-time analysis of risky behaviors. Experimental simulations indicate that the proposed solution significantly improves logistics monitoring. The system’s performance is assessed using various statistical metrics, including latency rate (7.44 s), a data processing cost (<span><math><mrow><mi>O</mi><mrow><mo>(</mo><mrow><mo>(</mo><mi>n</mi><mo>−</mo><mn>1</mn><mo>)</mo></mrow><mo>log</mo><mi>n</mi><mo>)</mo></mrow></mrow></math></span>), and model training and testing results (precision (94.60%), recall (95.67%), and F-Measure (96.64%)), statistical performance (error reduction (48%)) and reliability (94.48%).</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104055"},"PeriodicalIF":7.7,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FRRL: A reinforcement learning approach for link failure recovery in a hybrid SDN","authors":"Yulong Ma ,&nbsp;Yingya Guo ,&nbsp;Ruiyu Yang ,&nbsp;Huan Luo","doi":"10.1016/j.jnca.2024.104054","DOIUrl":"10.1016/j.jnca.2024.104054","url":null,"abstract":"<div><div>Network failures, especially link failures, happen frequently in Internet Service Provider (ISP) networks. When link failures occur, the routing policies need to be re-computed and failure recovery usually takes a few minutes, which degrades the network performance to a great extent. Therefore, a proper failure recovery scheme that can realize a fast and timely routing policy computation needs to be designed. In this paper, we propose FRRL, a Reinforcement Learning (RL) approach to intelligently perceive network failures and timely compute the routing policy for improving the network performance when link failure happens. Specifically, to perceive the link failures, we design a Topology Difference Vector (TDV) encoder module in FRRL for encoding the topology structure with link failures. To efficiently compute the routing policy when link failures happen, we integrate the TDV in the agent training for learning the map between the encoded failure topology structure and routing policies. To evaluate the performance of our proposed method, we conduct experiments on three network topologies and the experimental results demonstrate that our proposed method has superior performance when link failures happen compared to other methods.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104054"},"PeriodicalIF":7.7,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAT-Net: A staggered attention network using graph neural networks for encrypted traffic classification","authors":"Zhiyuan Li,&nbsp;Hongyi Zhao,&nbsp;Jingyu Zhao,&nbsp;Yuqi Jiang,&nbsp;Fanliang Bu","doi":"10.1016/j.jnca.2024.104069","DOIUrl":"10.1016/j.jnca.2024.104069","url":null,"abstract":"<div><div>With the increasing complexity of network protocol traffic in the modern network environment, the task of traffic classification is facing significant challenges. Existing methods lack research on the characteristics of traffic byte data and suffer from insufficient model generalization, leading to decreased classification accuracy. In response, we propose a method for encrypted traffic classification based on a Staggered Attention Network using Graph Neural Networks (SAT-Net), which takes into consideration both computer network topology and user interaction processes. Firstly, we design a Packet Byte Graph (PBG) to efficiently capture the byte features of flow and their relationships, thereby transforming the encrypted traffic classification problem into a graph classification problem. Secondly, we meticulously construct a GNN-based PBG learner, where the feature remapping layer and staggered attention layer are respectively used for feature propagation and fusion, enhancing the robustness of the model. Experiments on multiple different types of encrypted traffic datasets demonstrate that SAT-Net outperforms various advanced methods in identifying VPN traffic, Tor traffic, and malicious traffic, showing strong generalization capability.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"233 ","pages":"Article 104069"},"PeriodicalIF":7.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RLL-SWE: A Robust Linked List Steganography Without Embedding for intelligence networks in smart environments","authors":"Pengbiao Zhao ,&nbsp;Yuanjian Zhou ,&nbsp;Salman Ijaz ,&nbsp;Fazlullah Khan ,&nbsp;Jingxue Chen ,&nbsp;Bandar Alshawi ,&nbsp;Zhen Qin ,&nbsp;Md Arafatur Rahman","doi":"10.1016/j.jnca.2024.104053","DOIUrl":"10.1016/j.jnca.2024.104053","url":null,"abstract":"<div><div>With the rapid development of technology, smart environments utilizing the Internet of Things, artificial intelligence, and big data are improving the quality of life and work efficiency through connected devices. However, these advances present significant security challenges. The data generated by these smart devices contains many private and sensitive information. In data transmission, crime and terrorism may intercept this sensitive information and use it for secret communications and illegal activities. Steganography hides information in media files and prevents information leakage and interception by criminal and terrorist networks in an intelligent environment. It is an important technology to protect data integrity and security. Traditional steganography techniques often cause detectable distortions, whereas Steganography Without Embedding (SWE) avoids direct modification of cover media, thereby minimizing detection risks. This paper introduces an innovative and robust technique called Robust Linked List (RLL)-SWE, which improves resistance to attacks compared to traditional methods. Using multiple median downsampling and gradient calculations, this method extracts stable features. It restructures them into a multi-head unidirectional linked list, ensuring accurate message retrieval and high resistance to adversarial attacks. Comprehensive analysis and simulation experiments confirm the technique’s exceptional effectiveness and steganographic capacity.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104053"},"PeriodicalIF":7.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FCG-MFD: Benchmark function call graph-based dataset for malware family detection","authors":"Hassan Jalil Hadi ,&nbsp;Yue Cao ,&nbsp;Sifan Li ,&nbsp;Naveed Ahmad ,&nbsp;Mohammed Ali Alshara","doi":"10.1016/j.jnca.2024.104050","DOIUrl":"10.1016/j.jnca.2024.104050","url":null,"abstract":"<div><div>Cyber crimes related to malware families are on the rise. This growth persists despite the prevalence of various antivirus software and approaches for malware detection and classification. Security experts have implemented Machine Learning (ML) techniques to identify these cyber-crimes. However, these approaches demand updated malware datasets for continuous improvements amid the evolving sophistication of malware strains. Thus, we present the FCG-MFD, a benchmark dataset with extensive Function Call Graphs (FCG) for malware family detection. This dataset guarantees resistance against emerging malware families by enabling security systems. Our dataset has two sub-datasets (FCG &amp; Metadata) (1,00,000 samples) from VirusSamples, Virusshare, VirusSign, theZoo, Vx-underground, and MalwareBazaar curated using FCGs and metadata to optimize the efficacy of ML algorithms. We suggest a new malware analysis technique using FCGs and graph embedding networks, offering a solution to the complexity of feature engineering in ML-based malware analysis. Our approach to extracting semantic features via the Natural Language Processing (NLP) method is inspired by tasks involving sentences and words, respectively, for functions and instructions. We leverage a node2vec mechanism-based graph embedding network to generate malware embedding vectors. These vectors enable automated and efficient malware analysis by combining structural and semantic features. We use two datasets (FCG &amp; Metadata) to assess FCG-MFD performance. F1-Scores of 99.14% and 99.28% are competitive with State-of-the-art (SOTA) methods.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"233 ","pages":"Article 104050"},"PeriodicalIF":7.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Particle swarm optimization tuned multi-headed long short-term memory networks approach for fuel prices forecasting","authors":"Andjela Jovanovic ,&nbsp;Luka Jovanovic ,&nbsp;Miodrag Zivkovic ,&nbsp;Nebojsa Bacanin ,&nbsp;Vladimir Simic ,&nbsp;Dragan Pamucar ,&nbsp;Milos Antonijevic","doi":"10.1016/j.jnca.2024.104048","DOIUrl":"10.1016/j.jnca.2024.104048","url":null,"abstract":"<div><div>Increasing global energy demands and decreasing stocks of fossil fuels have led to a resurgence of research into energy forecasting. Artificial intelligence, explicitly time series forecasting holds great potential to improve predictions of cost and demand with many lucrative applications across several fields. Many factors influence prices on a global scale, from socio-economic factors to distribution, availability, and international policy. Also, various factors need to be considered in order to make an accurate forecast. By analyzing the current literature, a gap for improvements within this domain exists. Therefore, this work suggests and explores the potential of multi-headed long short-term memory models for gasoline price forecasting, since this issue was not tackled with multi-headed models before. Additionally, since the computational requirements for such models are relatively high, work focuses on lightweight approaches that consist of a relatively low number of neurons per layer, trained in a small number of epochs. However, as algorithm performance can be heavily dependent on appropriate hyper-parameter selections, a modified variant of the particle swarm optimization algorithm is also set forth to help in optimizing the model’s architecture and training parameters. A comparative analysis is conducted using energy data collected from multiple public sources between several contemporary optimizers. The outcomes are put through a meticulous statistical validation to ascertain the significance of the findings. The best-constructed models attained a mean square error of just 0.044025 with an R-squared of 0.911797, suggesting potential for real-world use.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"233 ","pages":"Article 104048"},"PeriodicalIF":7.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A blockchain based secure authentication technique for ensuring user privacy in edge based smart city networks","authors":"Abeer Iftikhar ,&nbsp;Kashif Naseer Qureshi ,&nbsp;Faisal Bashir Hussain ,&nbsp;Muhammad Shiraz ,&nbsp;Mehdi Sookhak","doi":"10.1016/j.jnca.2024.104052","DOIUrl":"10.1016/j.jnca.2024.104052","url":null,"abstract":"<div><div>In the past decade, modernization of Information and Communication Technology (ICT), Edge Computing (EC), and Smart Cities has attracted significant academic interest due to its diverse applications in the fields of healthcare, transportation, agriculture, and defense. EC offers numerous advantages, including faster and more efficient services, lower latency, improved data processing, managed bandwidth consumption, scalable, real-time decision-making, security, reduced network congestion, and increased resilience. Despite these benefits, EC networks face persistent challenges, particularly related to security and privacy concerns. Addressing these security challenges requires strong authentication mechanisms, which demand extra resources like processing power and memory, often surpassing the limited capabilities of lightweight edge devices compared to cloud systems. This highlights the critical need for securing edge nodes and ensuring user privacy before real-world deployment and data transfer. User and edge device authentication is vital to prevent external and internal Impersonation and Reflection attacks that threaten system integrity and confidentiality. This paper presents a BlockChain based Authentication technique for Edge Networks (BCAuthEN) that utilizes a Consortium Blockchain (CB) with key agreements for biometric authentication, incorporating a Fuzzy Extractor (FE) to secure user biometrics and passwords. In addition, BCAuthEN offers multifactor and continuous authentication by monitoring user behavior and biometrics. BCAuthEN has been formally verified through Real-Or-Random (RoR) modeling and AVISPA tool, proving its effectiveness in enhancing privacy, and security. The proposed technique ensures robust security by preventing attackers at the potential entry points (edge nodes). In addition, BCAuthEN reduces computation cost, communication overhead and improves throughput. BCAuthEN provides strong resilience by achieving high detection accuracy and reduces false positives against impersonation and reflection attacks. Results have shown that BCAuthEN improves communication costs and reduces overhead by 10% and 7%, respectively, as compared to the recent biometric and key-based user authentication techniques.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"233 ","pages":"Article 104052"},"PeriodicalIF":7.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning frameworks for cognitive radio networks: Review and open research challenges","authors":"Senthil Kumar Jagatheesaperumal ,&nbsp;Ijaz Ahmad ,&nbsp;Marko Höyhtyä ,&nbsp;Suleman Khan ,&nbsp;Andrei Gurtov","doi":"10.1016/j.jnca.2024.104051","DOIUrl":"10.1016/j.jnca.2024.104051","url":null,"abstract":"<div><div>Deep learning has been proven to be a powerful tool for addressing the most significant issues in cognitive radio networks, such as spectrum sensing, spectrum sharing, resource allocation, and security attacks. The utilization of deep learning techniques in cognitive radio networks can significantly enhance the network’s capability to adapt to changing environments and improve the overall system’s efficiency and reliability. As the demand for higher data rates and connectivity increases, B5G/6G wireless networks are expected to enable new services and applications significantly. Therefore, the significance of deep learning in addressing cognitive radio network challenges cannot be overstated. This review article provides valuable insights into potential solutions that can serve as a foundation for the development of future B5G/6G services. By leveraging the power of deep learning, cognitive radio networks can pave the way for the next generation of wireless networks capable of meeting the ever-increasing demands for higher data rates, improved reliability, and security.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"233 ","pages":"Article 104051"},"PeriodicalIF":7.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint VM and container consolidation with auto-encoder based contribution extraction of decision criteria in Edge-Cloud environment","authors":"Farkhondeh Kiaee ,&nbsp;Ehsan Arianyan","doi":"10.1016/j.jnca.2024.104049","DOIUrl":"10.1016/j.jnca.2024.104049","url":null,"abstract":"<div><div>In the recent years, emergence huge Edge-Cloud environments faces great challenges like the ever-increasing energy demand, the extensive Internet of Things (IoT) devices adaptation, and the goals of efficiency and reliability. Containers has become increasingly popular to encapsulate various services and container migration among Edge-Cloud nodes may enable new use cases in various IoT domains. In this study, an efficient joint VM and container consolidation solution is proposed for Edge-Cloud environment. The proposed method uses the Auto-Encoder (AE) and TOPSIS modules for two stages of consolidation subproblems, namely, Joint VM and Container Multi-criteria Migration Decision (AE-TOPSIS-JVCMMD) and Edge-Cloud Power SLA Aware (AE-TOPSIS-ECPSA) for VM placement. The module extracts the contribution of different criteria and computes the scores of all the alternatives. Combining the non-linear contribution learning ability of the AE algorithm and the intelligent ranking of the TOPSIS algorithm, the proposed method successfully avoids the bias of conventional multi-criteria approaches toward alternatives that have good evaluations in two or more dependent criteria. The simulations conducted using the Cloudsim simulator confirm the effectiveness of the proposed policies, demonstrating to 41.5%, 30.13%, 12.9%, 10.3%, 58.2% and 56.1% reductions in energy consumption, SLA violation, response time, running cost, number of VM migrations, and number of container migrations, respectively in comparison with state of the arts.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"233 ","pages":"Article 104049"},"PeriodicalIF":7.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Third layer blockchains are being rapidly developed: Addressing state-of-the-art paradigms and future horizons","authors":"Saeed Banaeian Far,&nbsp;Seyed Mojtaba Hosseini Bamakan","doi":"10.1016/j.jnca.2024.104044","DOIUrl":"10.1016/j.jnca.2024.104044","url":null,"abstract":"<div><div>Undoubtedly, blockchain technology has emerged as one of the most fascinating advancements in recent decades. Its rapid development has attracted a diverse range of experts from various fields. Over the past five years, numerous blockchains have been launched, hosting a multitude of applications with varying objectives. However, a key limitation of blockchain-based services and applications is their isolation within their respective host blockchains, preventing them from recording or accessing data from other blockchains. This limitation has spurred developers to explore solutions for connecting different blockchains without relying on centralized intermediaries. This new wave of projects, officially called Layer 3 projects (L3) initiatives, has introduced innovative concepts like cross-chain transactions, multi-chain frameworks, hyper-chains, and more. This study provides an overview of these significant concepts and L3 projects while categorizing them into interoperability and scalability solutions. We then discuss opportunities, challenges, and future horizons of L3 solutions and present a SWOT (Strengths–Weaknesses–Opportunities–Threats) analysis of the two groups of L3 solutions and all other proposals. As an important part, we introduce the concept of Universal decentralized finance (DeFi) as one the most exciting applications of L3s which decreases transaction costs, enhances the security of crowdfunding, and provides many improvements in distributed lending-borrowing processes. The final part of this study maps the blockchain’s triangle problem on L3s and identifies current challenges from the L3’s perspective. Ultimately, the future directions of L3 for both academic and industry sectors are discussed.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"233 ","pages":"Article 104044"},"PeriodicalIF":7.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}