International Journal of Network Management最新文献

{"title":"Out-of-band transaction pool sync for large dynamic blockchain networks","authors":"Novak Boskov,&nbsp;Xingyu Chen,&nbsp;Sevval Simsek,&nbsp;Ari Trachtenberg,&nbsp;David Starobinski","doi":"10.1002/nem.2265","DOIUrl":"10.1002/nem.2265","url":null,"abstract":"<p>Synchronization of transaction pools (<i>mempools</i>) has shown potential for improving the performance and block propagation delay of state-of-the-art blockchains. Indeed, various heuristics have been proposed in the literature to incorporate early exchanges of <i>unconfirmed</i> transactions into the block propagation protocol. In this work, we take a different approach, maintaining transaction synchronization externally (and independently) of the block propagation channel. In the process, we formalize the synchronization problem within a graph theoretic framework and introduce a novel algorithm (<i>SREP</i>—<i>set reconciliation-enhanced propagation</i>) with quantifiable guarantees. We analyze the algorithm's performance for various realistic network topologies and show that it converges on static connected graphs in a time bounded by the diameter of the graph. In graphs with dynamic edges, <i>SREP</i> converges in an expected time that is linear in the number of nodes. We confirm our analytical findings through extensive simulations that include comparisons with <i>MempoolSync</i>, a recent approach from the literature. Our simulations show that <i>SREP</i> incurs reasonable bandwidth overhead and scales gracefully with the size of the network (unlike <i>MempoolSync</i>).</p>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140300514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 security-enhanced equipment predictive maintenance solution for the ETO manufacturing","authors":"Xiangyu Cao,&nbsp;Zhengjun Jing,&nbsp;Xiaorong Zhao,&nbsp;Xiaolong Xu","doi":"10.1002/nem.2263","DOIUrl":"10.1002/nem.2263","url":null,"abstract":"<div>\u0000 \u0000 <p>With the rapid advancement of intelligent manufacturing, ensuring equipment safety has become a crucial prerequisite for enterprise production. In the engineer-to-order (ETO) production mode, characterized by diverse equipment types and frequent adjustments in production lines, equipment maintenance has become increasingly complex. Traditional maintenance plans are no longer adequate to meet the evolving demands of equipment maintenance. This paper proposes a security-enhanced predictive maintenance scheme specifically designed for ETO-type production equipment. The scheme utilizes industrial Internet of Things (IIoT) technology to monitor machines and equipment, constructs prediction models using machine learning methods, and reinforces the security of the prediction system through adoption of a decentralized architecture with blockchain distributed storage. In this experiment, six supervised learning models were compared, and it was found that the model based on the random forest algorithm achieved an outstanding accuracy rate of 98.88%. Furthermore, the average total response time for generating predictions within the system is 2.0 s, demonstrating a performance suitable for practical equipment maintenance applications.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"IGXSS: XSS payload detection model based on inductive GCN","authors":"Qiuhua Wang,&nbsp;Chuangchuang Li,&nbsp;Dong Wang,&nbsp;Lifeng Yuan,&nbsp;Gaoning Pan,&nbsp;Yanyu Cheng,&nbsp;Mingde Hu,&nbsp;Yizhi Ren","doi":"10.1002/nem.2264","DOIUrl":"10.1002/nem.2264","url":null,"abstract":"<p>To facilitate the management, Internet of Things (IoT) vendors usually apply remote ways such as HTTP services to uniformly manage IoT devices, leading to traditional web application vulnerabilities that also endanger the cloud interfaces of IoT, such as cross-site scripting (XSS), code injection, and Remote Command/Code Execute (RCE). XSS is one of the most common web application attacks, which allows the attacker to obtain private user information or attack IoT devices and IoT cloud platforms. Most of the existing XSS payload detection models are based on machine learning or deep learning, which usually require a lot of external resources, such as pretrained word vectors, to achieve a better performance on unknown samples. But in the field of XSS payload detection, high-quality vector representations of samples are often difficult to obtain. In addition, existing models all perform substantially worse when the distribution of XSS payloads and benign samples in the test dataset is extremely unbalanced (e.g., XSS payloads: benign samples = 1: 20). While in the real XSS attack scenario against IoT, an XSS payload is often hidden in a massive amount of normal user requests, indicating that these models are not practical. In response to the above issues, we propose an XSS payload detection model based on inductive graph neural networks, IGXSS (XSS payload detection model based on inductive GCN), to detect XSS payloads targeting IoT. Firstly, we treat the samples and words obtained from segmenting the samples as nodes and attach lines between them in order to form a graph. Then, we obtain the feature matrix of nodes and edges utilizing information between nodes only (instead of external resources such as pretrained word vectors). Finally, we feed the obtained feature matrix into a two-layer GCN for training and validate the performance of models in several datasets with different sample distributions. Extensive experiments on the real datasets show that IGXSS performs better compared to other models under various sample distributions. In particular, when the sample distribution is extremely unbalanced, the recall and F1 score of IGXSS still reach 1.000 and 0.846, demonstrating that IGXSS is more robust and more suitable for practical scenarios.</p>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139773683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Reducing the propagation delay of compact block in Bitcoin network","authors":"Aeri Kim,&nbsp;Meryam Essaid,&nbsp;Sejin Park,&nbsp;Hongtaek Ju","doi":"10.1002/nem.2262","DOIUrl":"10.1002/nem.2262","url":null,"abstract":"<div>\u0000 \u0000 <p>Bitcoin is a Blockchain-based network in which thousands of nodes are directly connected and communicate via a gossip-based flooding protocol. Mined blocks are propagated to all participating nodes in the network through a CBR (compact block relay) protocol developed to reduce the block propagation delay. However, propagation delay persists. The relay time between nodes must be measured and analyzed to determine the cause of the delay and provide solutions for reducing block propagation time. Previously, we measured the relay time and investigated the cause of the delay. According to the findings of the previous study, the delay of the relay time occurs when assembling compact blocks, depending on whether transactions are requested. In this paper, we find the reasons for requesting transactions. The reasons are due to the transaction propagation method and the characteristics of the transaction itself. We propose a solution based on this. It is a method of reducing probability of requesting transactions by using the compact block's “PREFILLEDTXN” to send the transactions expected to be requested with the block. The probability of requesting is reduced by up to 67% when transactions that have just entered the memory pool are propagated by PREFILLEDTXN. The block relay time is reduced by up to 44% as a result. Finally, this research reduces block relay time between nodes.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139590560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Deeper: A shared liquidity decentralized exchange design for low trading volume tokens to enhance average liquidity","authors":"Srisht Fateh Singh,&nbsp;Panagiotis Michalopoulos,&nbsp;Andreas Veneris","doi":"10.1002/nem.2261","DOIUrl":"10.1002/nem.2261","url":null,"abstract":"<p>This paper presents <span>Deeper</span>, a design for a decentralized exchange that enhances liquidity via reserve sharing. By doing this, it addresses the problem of shallow liquidity in low trading volume token pairs. Shallow liquidity impairs the functioning of on-chain markets by creating room for unwanted phenomena such as high slippage and sandwich attacks. <span>Deeper</span> solves this by allowing liquidity providers of multiple trading pairs against a common token to share liquidity. This is achieved by creating a common reserve pool for the shared token that is accessible by each trading pair. Independent from the shared liquidity, providers are free to add liquidity to individual token pairs without any restriction. The trading between one token pair does not affect the price of other token pairs even though the reserve of the shared token changes. The proposed design is an extension of concentrated liquidity automated market maker DEXs that is simple enough to be implemented on smart contracts. This is demonstrated by providing a template for a hook-based smart contract that adds our custom functionality to <span>Uniswap V4</span>. Experiments on historical prices show that for a batch consisting of eight trading pairs, <span>Deeper</span> enhances liquidity by over 2.6–\u0000<span></span><math>\u0000 <mn>5</mn>\u0000 <mo>.</mo>\u0000 <mn>9</mn>\u0000 <mo>×</mo></math>. The enhancement in liquidity can be increased further by increasing the participating tokens in the shared pool. While providing shared liquidity, liquidity providers should be cautious of certain risks and pitfalls, which are described. Overall, <span>Deeper</span> enables the creation of fair markets for low trading volume token pairs.</p>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 4","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/nem.2261","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139498570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"The next phase of identifying illicit activity in Bitcoin","authors":"Jack Nicholls,&nbsp;Aditya Kuppa,&nbsp;Nhien-An Le-Khac","doi":"10.1002/nem.2259","DOIUrl":"10.1002/nem.2259","url":null,"abstract":"<p>Identifying illicit behavior in the Bitcoin network is a well-explored topic. The methods proposed over time have generated great insights into the deanonymization of the Bitcoin user base through the clustering of inputs and outputs. With advanced techniques being deployed by Bitcoin users, these heuristics are now being challenged in their ability to aid in the detection of illicit activity. In this paper, we provide a comprehensive list of methods deployed by malicious actors on the network and illicit transaction mining methods. We detail the evolution of the heuristics that are used to deanonymize Bitcoin transactions. We highlight the issues associated with conducting law enforcement investigations and propose recommendations for the research community to address these issues. Our recommendations include the release of public data by exchanges to allow researchers and law enforcement to further protect the network from malicious users. We recommend the enhancement of current heuristics through machine learning methods and discuss how researchers can take the fight head-on against expert cybercriminals.</p>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/nem.2259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139499005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"TPAAD: Two-phase authentication system for denial of service attack detection and mitigation using machine learning in software-defined network","authors":"Najmun Nisa,&nbsp;Adnan Shahid Khan,&nbsp;Zeeshan Ahmad,&nbsp;Johari Abdullah","doi":"10.1002/nem.2258","DOIUrl":"10.1002/nem.2258","url":null,"abstract":"<p>Software-defined networking (SDN) has received considerable attention and adoption owing to its inherent advantages, such as enhanced scalability, increased adaptability, and the ability to exercise centralized control. However, the control plane of the system is vulnerable to denial-of-service (DoS) attacks, which are a primary focus for attackers. These attacks have the potential to result in substantial delays and packet loss. In this study, we present a novel system called Two-Phase Authentication for Attack Detection that aims to enhance the security of SDN by mitigating DoS attacks. The methodology utilized in our study involves the implementation of packet filtration and machine learning classification techniques, which are subsequently followed by the targeted restriction of malevolent network traffic. Instead of completely deactivating the host, the emphasis lies on preventing harmful communication. Support vector machine and K-nearest neighbours algorithms were utilized for efficient detection on the CICDoS 2017 dataset. The deployed model was utilized within an environment designed for the identification of threats in SDN. Based on the observations of the banned queue, our system allows a host to reconnect when it is no longer contributing to malicious traffic. The experiments were run on a VMware Ubuntu, and an SDN environment was created using Mininet and the RYU controller. The results of the tests demonstrated enhanced performance in various aspects, including the reduction of false positives, the minimization of central processing unit utilization and control channel bandwidth consumption, the improvement of packet delivery ratio, and the decrease in the number of flow requests submitted to the controller. These results confirm that our Two-Phase Authentication for Attack Detection architecture identifies and mitigates SDN DoS attacks with low overhead.</p>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/nem.2258","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139515576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Fractional non-fungible tokens: Overview, evaluation, marketplaces, and challenges","authors":"Wonseok Choi,&nbsp;Jongsoo Woo,&nbsp;James Won-Ki Hong","doi":"10.1002/nem.2260","DOIUrl":"10.1002/nem.2260","url":null,"abstract":"<p>Fractional non-fungible tokens (NFTs) have emerged at the forefront of blockchain innovation, merging tokenization, NFTs, and fractional ownership to democratize access to high-value digital assets. In this paper, we explore the fundamental concepts of blockchain technology, smart contracts, NFTs, and tokenization to lay the groundwork for understanding fractional NFTs. We investigate key ERC standards, including ERC-20, ERC-721, and ERC-1155, which are pivotal in enabling the creation and management of fractional NFTs on the Ethereum blockchain. Then, we present two major processes in fractional NFTs, minting and reconstitution. We develop fractional NFTs based on ERC standards and evaluate their gas consumption. Furthermore, through a comprehensive review of existing platforms, we analyze their minting and reconstitution processes and underlying ERC standards. Challenges, such as regulatory compliance and security, are also examined. We highlight the significance of robust security measures and transparency to build trust in fractional NFT ecosystems. While the field is still evolving, fractional NFTs have the potential to disrupt traditional ownership models and revolutionize industries. We envision fractional NFTs fostering a more inclusive and decentralized digital economy as technology advances and adoption grows.</p>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 4","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139462401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 novel eviction policy based on shortest remaining time for software defined networking flow tables","authors":"Kavi Priya Dhandapani,&nbsp;Mirnalinee Thanganadar Thangathai,&nbsp;Shahul Hamead Haja Moinudeen","doi":"10.1002/nem.2257","DOIUrl":"10.1002/nem.2257","url":null,"abstract":"<div>\u0000 \u0000 <p>Software defined networking is a modern paradigm that divides the control plane from the data plane for improved network manageability. A flow table in the data plane has limited and expensive memory called TCAM. The presence of unwanted flow rules would lead to flow bloat conditions and make the lookup operation inefficient. Eviction schemes based on LRU policy have been widely studied in the literature which preempts the life of the least recently used flow rule and reduces the occupancy of the flow table. LRU considers the past behavior for the eviction of a flow rule. This paper proposes a novel policy that preempts a flow rule by considering its future characteristic, the shortest remaining time (SRT). A rule with a higher probability of being used is avoided from eviction to mitigate the degradation of performance. The modeling of the SRT technique exhibits better utilization of a flow rule that has higher probability of being used. On the other hand, LRU does not guarantee that the evicted flow rule will not be used frequently in the future and it has been shown that an incorrectly evicted flow rule incurs controller delay. The experimental results show that for different traffic rates, SRT has reduced the delay by 15%, reinstallation count by 25%, and jitter by 40%. SRT has increased utilization by 22% compared to LRU.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138825536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Blockchain and crypto forensics: Investigating crypto frauds","authors":"Udit Agarwal,&nbsp;Vinay Rishiwal,&nbsp;Sudeep Tanwar,&nbsp;Mano Yadav","doi":"10.1002/nem.2255","DOIUrl":"10.1002/nem.2255","url":null,"abstract":"<p>In the past few years, cryptocurrency has gained widespread acceptance because of its decentralized nature, quick and secure transactions, and potential for investment and speculation. But the increased popularity has also led to increased cryptocurrency fraud, including scams, phishing attacks, Ponzi schemes, and other criminal activities. Although there is little documentation of cryptocurrency fraud, an in-depth study is essential to recognize various scams in different cryptocurrencies. To fill this gap, a study investigated cryptocurrency-related fraud in various cryptocurrencies and provided a taxonomy of crypto-forensics and forensic blockchain. In addition, we have introduced an architecture that integrates artificial intelligence (AI) and blockchain technologies to investigate and protect against instances of cryptocurrency fraud. The suggested design's effectiveness was evaluated using several machine learning (ML) classification algorithms. The conclusion of the evaluation confirmed that the random forest (RF) classifier performed the best, delivering the highest level of accuracy, that is, 97.5%. Once the ML classifiers detect cryptocurrency fraud, the information is securely stored in the InterPlanetary File System (IPFS); the document's hash is also stored in the blockchain using smart contracts. Law enforcement can leverage blockchain technology to secure access to fraudulent cryptographic transactions. The proposed architecture was tested for bandwidth utilization. Despite the potential benefits of blockchain and crypto-forensics, several issues and challenges remain, including privacy concerns, standardization, and difficulty identifying fraud between crypto-currencies. Finally, the paper discusses various problems and challenges in blockchain and crypto forensics to investigate cryptocurrency fraud.</p>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138561650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}