International Journal of Network Management最新文献

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.

This paper presents Deeper, 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. Deeper 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 Uniswap V4. Experiments on historical prices show that for a batch consisting of eight trading pairs, Deeper enhances liquidity by over 2.6–$�5�.�9�\times$. 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, Deeper enables the creation of fair markets for low trading volume token pairs.

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.

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.

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.

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.

Software defined network (SDN) integrated vehicular ad hoc network (VANET) is a magnificent technique for smart transportation as it raises the efficiency, safety, manageability, and comfort of traffic. SDN-integrated VANET (SDN-int-VANET) has numerous benefits, but it is susceptible to threats like distributed denial of service (DDoS). Several methods were suggested for DDoS attack detection (AD), but the existing approaches to optimization have given a base for enhancing the parameters. An incorrect selection of parameters results in a poor performance and poor fit to the data. To overcome these issues, residual-based temporal attention red fox-convolutional neural network (RTARF-CNN) for detecting DDoS attacks in SDN-int-VANET is introduced in this manuscript. The input data is taken from the SDN DDoS attack dataset. For restoring redundancy and missing value, developed random forest and local least squares (DRFLLS) are applied. Then the important features are selected from the pre-processed data with the help of stacked contractive autoencoders (St-CAE), which reduces the processing time of the introduced method. The selected features are classified by residual-based temporal attention-convolutional neural network (RTA-CNN). The weight parameter of RTA-CNN is optimized with the help of red fox optimization (RFO) for better classification. The introduced method is implemented in the PYTHON platform. The RTARF-CNN attains 99.8% accuracy, 99.5% sensitivity, 99.80% precision, and 99.8% specificity. The effectiveness of the introduced technique is compared with the existing approaches.