Eclipse attacks isolate blockchain nodes by monopolizing their peer-to-peer connections. The attacks were extensively studied in Bitcoin (SP'15, SP'20, CCS'21, SP'23) and Monero (NDSS'25), but their practicality against Ethereum nodes remains underexplored, particularly in the post-Merge settings.

Cryptomining poses significant security risks, yet traditional detection methods like blacklists and Deep Packet Inspection (DPI) are often ineffective against encrypted mining traffic and suffer from high false positive rates. In this paper, we propose a practical encrypted cryptomining traffic detection mechanism. It consists of a two-stage detection framework, which can effectively provide fine-grained detection results by machine learning and reduce false positives from classifiers through active probing. Our system achieves an F1-score of 0.99 and identifies specific cryptocurrencies with a 99.39% accuracy rate. Extensive testing across various mining pools confirms the effectiveness of our approach, offering a more precise and reliable solution for identifying cryptomining activities.

Many tracking companies collect user data and sell it to data markets and advertisers. While they claim to protect user privacy by anonymizing the data, our research reveals that significant privacy risks persist even with anonymized data. Attackers can exploit this data to identify users' accounts on other websites and perform targeted identity alignment. In this paper, we propose an effective identity alignment scheme for accurately identifying targeted users. We develop a data collector to obtain the necessary datasets, an algorithm for identity alignment, and, based on this, construct two types of de-anonymization attacks: thepassive attack, which analyzes tracker data to align identities, and theactive attack, which induces users to interact online, leading to higher success rates. Furthermore, we introduce, for the first time, a novel evaluation framework for online tracking-based identity alignment. We investigate the key factors influencing the effectiveness of identity alignment. Additionally, we provide an independent assessment of our generated dataset and present a fully functional system prototype applied to a cryptocurrency use case.

Although many scalable event matching algorithms have been proposed to achieve scalability for publish/subscribe services, the content-based pub/sub system still suffer from performance deterioration when the system has large numbers of subscriptions, and cannot support the requirements of real-time pub/sub data services. In this paper, we model the event matching problem as an existence problem which only care about whether there is at least one matching subscription in the given subscription set, differing from existing works that try to speed up the time-consuming search operation to find all matching subscriptions. To solve this existence problem efficiently, we propose DLS (Discrete Label Set), a novel subscription and event representation model. Based on the DLS model, we propose an event matching algorithm withO(Nd)time complexity to support real-time event matching for a large volume of subscriptions and high event arrival speed, whereNdis the node degree in overlay network. Experimental results show that the event matching performance can be improved by several orders of magnitude compared with traditional algorithms.

Cryptomining behaviours pose severe security threats to campus network. However, existing blacklist and DPI-based techniques suffer from delayed blacklist updates and inability to identify encrypted cryptomining traffic. Furthermore, existing encrypted cryptomining traffic detection schemes usually fail to provide detailed information about cryptomining behaviours and do not have a solution to deal with false positives caused by detection models. To meet the needs of campus networks and solve the problems of existing work, this paper proposes an effective and practical encrypted cryptomining traffic detection mechanism in campus network. It consists of a two-stage detection framework, which can effectively provide fine-grained detection results by machine learning and reduce false positives from classifiers through active probing. Based on our collected dataset and extracted time series features, our classifiers detect mining traffic with an 0.99 F 1 score and identify the cryptocurrency being mined with $99.39 \%$ correct recognition rate. Unlike existing schemes, we perform active probing after the traffic classification to reduce false positives. Futhermore, we have extensively evaluated the active probing scheme to verify its effectiveness for different mining pools.

This work investigates the application of an Iron-Nitride (FeN) Permanent Magnet (PM) material to variable flux motors (VFMs). Two existing VFMs are used as references, a rareearth-free inverted saliency VFM and a series hybrid VFM. Initially, the inverted saliency VFM performance is evaluated for equal usage of PM material. Then, the PMs are resized for achieving similar electromagnetic performance to the reference VFM. It is noted that although the new PM has 25% higher residual flux density than AlNiCo9 PM, it requires high current for remagnetization. However, adopting the FeN magnet to the inverted saliency machine can allow reducing the PM usage by 42% and hence reduced rotor weight would be expected. By replacing the rare-earth magnet of the series hybrid VFM with FeN, comparable constant torque performance is attained with the advantage of avoiding rare-earth magnet demagnetization risks. This shows that for the investigated torque density levels, the FeN magnet has a high potential for designing existing/upcoming regular and hybrid magnet VFMs.

This paper presents a new approach for measuring the magnetic core loss in the stator of an electric machine aimed for electric vehicle applications. Using this method, the effect of mechanical stress on the magnetic characteristics of a fully assembled stator core is studied. Later core loss is calculated based on the measurement data. It is shown that the BH curve of the steel lamination changes due to the mechanical stress. The core loss also increases by more than fifty percent for the sample under test. This increase in the loss can be crucial for the performance of the electric motor in the highly competitive field of the electric vehicle market.

Abstract Publish/subscribe(pub/sub) systems are widely used in large-scale messaging systems due to their asynchronous and decoupled nature. With the population of pub/sub cloud services, the privacy protection problem of pub/sub systems has started to emerge, and events and subscriptions are exposed when executing event matching on untrustworthy cloud brokers. However, as the number of subscriptions increases, the effectiveness of the previous confidentiality protection approaches declines drastically. In this paper, we propose SBM (scalable blind matching), an effective confidentiality protection scheme for pub/sub systems. To the best of our knowledge, SBM is the first scheme that applies order-preserving encryption algorithm to protect the system’s confidentiality and ensure its scalability. In this scheme, SBM-I is highly effective in subscription matching but is unable to achieve ideal security IND-OCPA, whereas SBM-II is suggested to ensure system security and SGX is used to reduce interaction and boost ciphertext matching performance. The experiment demonstrates that this method has better matching performance compared to others: the average matching time of SBM-I is 3–4 orders of magnitude faster than the matching algorithm MP and SGX-based algorithm SCBR when the number of subscriptions is 500,000, and the average matching time of SBM-II is 40 times faster than MP and 24 times than SCBR.

Abstract Bitcoin is a decentralized P2P cryptocurrency. It supports users to use pseudonyms instead of network addresses to send and receive transactions at the data layer, hiding users’ real network identities. Traditional transaction tracing attack cuts through the network layer to directly associate each transaction with the network address that issued it, thus revealing the sender’s network identity. But this attack can be mitigated by Bitcoin’s network layer privacy protections. Since Bitcoin protects the unlinkability of Bitcoin addresses and there may be a many-to-one relationship between addresses and nodes, transactions sent from the same node via different addresses are seen as coming from different nodes because attackers can only use addresses as node identifiers. In this paper, we proposed the evicting and filling attack to expose the correlations between addresses and cluster transactions sent from different addresses of the same node. The attack exploited the unisolation of Bitcoin’s incoming connection processing mechanism. In particular, an attacker can utilize the shared connection pool and deterministic connection eviction strategy to infer the correlation between incoming and evicting connections, as well as the correlation between releasing and filling connections. Based on inferred results, different addresses of the same node with these connections can be linked together, whether they are of the same or different network types. We designed a multi-step attack procedure, and set reasonable attack parameters through analyzing the factors that affect the attack efficiency and accuracy. We mounted this attack on both our self-run nodes and multi-address nodes in real Bitcoin network, achieving an average accuracy of 96.9% and 82%, respectively. Furthermore, we found that the attack is also applicable to Zcash, Litecoin, Dogecoin, Bitcoin Cash, and Dash. We analyzed the cost of network-wide attacks, the application scenario, and proposed countermeasures of this attack.

Data privacy and security issues are preventing a lot of potential on-cloud machine learning as services from happening. In the recent past, secure multi-party computation (MPC) has been used to achieve the secure neural network predictions, guaranteeing the privacy of data. However, the cost of the existing two-party solutions is expensive and they are impractical in real-world setting.

In this paper a PMSM model is proposed as a flexible and accurate tool for machine designers to conveniently evaluate the performances of a new PMSM design or a specific modification on an existing design base on the stator and rotor geometries. A step-by-step procedure is discussed, and informed assumptions are proposed to keep the model adaptable to different geometries. A reference IPM motor is used as an example of an application of the model. The model results are compared with FEA-based simulation and experimental validation.

Content-based publish/subscribe cloud services are prevailing recently. Confidentiality in publish/subscribe cloud services has become a major concern, especially for applications with sensitive data. Many methods have been proposed to achieve the confidentiality of events and subscriptions. Unfortunately, all these approaches suffer significant performance deterioration while matching on large-scale subscription set. In this paper, we propose an efficient ciphertext matching scheme called SBM. To the best of our knowledge, SBM is the first approach which can preserve confidentiality while at the same time provide scalable event matching. Furthermore, we have integrated SBM with an open source publish/subscribe middleware, PADRES and conducted extensive experiments to evaluate our scheme. The experimental results demonstrate that the matching speed of our solution is faster by two orders of magnitude than its counterparts.

This paper discusses the relation between dorm arrangement and student performance. One of the unsupervised learning algorithms, k-means algorithm, is mainly used in the process of analysis. Students are clustered into several clusters according to their similarity of performance scores. This paper analyzes the result of clustering by comparing it with actual dorm arrangement. In the end, drawbacks of k-means and reliability of this student dorm-performance relation are evaluated. Finally, this paper draws a conclusion that student performances are influenced by dorm arrangement.

Many factors affect the time cost of Cloud computing tasks. One of the most serious factors is data transmission latency, which reduces the efficiency of Cloud computing. Existing notable schemes ignore the communication cost among virtual machines (VMs) in the MapReduce environment. In this paper, we propose a VM placement approach to reduce data transmission latency with the communication cost among VMs. We first construct bipartite graph and classify VMs as two groups according to their transmission latency with data nodes. Then we propose two VM placement optimization algorithms to minimize the total data transmission latency (TDTL) and the maximum data transmission latency (MDTL) in the MapReduce environment. Finally, we place VMs for Reduce phase. The evaluation results show that our approach reduces the average data transmission latency by 26.3% compared with other approaches.

IoT (Internet of Things) bridges the physical world and information space. IoT services are environment sensitive and event-driven. The new IoT service architecture should adapt to these features. This paper analyses IoT sensing service characteristics and proposes the future services architecture. It is focused on the middleware architecture and the interface presentation technology. In the middleware layer, the traditional SOA architecture is insufficient in the real-time response and parallel process of services execution, this paper proposes that the new sensing service system based on EDSOA (Event Driven SOA) architecture to support real-time, event-driven, and active service execution. At presentation layer, this paper presents the new IoT browser features including using augmented reality technology to input and output, and realize the superposition presentation of the physical world and abstract information. Through a use case and proof-of-concept implementation-road manhole covers monitoring system - we verify the feasibility of the proposed ideas and frameworks.

Due to complex environment of the coal mine, it's necessary to monitor the information of underground environment, device and miner instantly in order to ensure the safety of coal mine production. However, the exiting coal mine can not meet the requirements of coverage without blind spots as it is developed by the wired network. This paper proposes a RESTful Web services mashup augmented coal mine safety monitoring and control automation using ZigBee wireless sensor network, which can collect the underground temperature, humidity methane values and personal position through sensor nodes in the coal mine, and also collects the personnel position information inside the mine, and then implement a RESTful Application Programming Interface (API) on sensor nodes to provide access to sensors and actuators, allowing for them to be easily combined with other enterprise information resources based on the success of mashup applications. We also illustrated three different of scenarios for RESTful Web service mashups representing for coal mine safety monitoring and control automation. Finally, we give the conclusions.