Android has stood at a predominant position in mobile operating systems for many years. However, its popularity and openness make it a desirable target of malicious attackers. There is an increasing need for mobile malware detection. Existing analysis methods fall into two categories, i.e., static analysis and dynamic analysis. The dynamic analysis is more effective and timely than the static one, but it incurs a high computational overhead, thus cannot be deployed in resource-constrained mobile devices. Existing studies solve this issue by outsourcing malware detection to the cloud. However, the privacy of mobile app runtime data uploaded to the cloud is not well preserved during both detection model training and malware detection. Numerous efforts have been made to preserve privacy with cryptography, which suffers from high computational overhead and low flexibility. To address these issues, in this paper, we propose an Intel SGX-empowered mobile malware detection scheme called EPMDroid. We also design a probabilistic data structure based on cuckoo filters, named CuckooTable, to effectively fuse features for detection and achieve high space efficiency. We conduct both theoretical analysis and real-world data based tests on EPMDroid performance. Experimental results show that EPMDroid can speed up malware detection by up to 43.8 times and save memory space by up to 3.7 times with the same accuracy, as compared to a baseline method.
According to the features of open and distance education examination in RTVU,aiming at difficulties and problems encountered in the test administration,this paper introduces the use of information technology,network and database to develop Guangdong Radio TV University Examination Command System,which improves the effectiveness of the examination management,and realizes real-time,synchronous,interactive management during TVU examination in the entire province,which can ensure the authority,timeliness and security of the examination administration,and thoroughly raises the command capacity,management and processing capabilities in case of emergency during TVU examinations.
In an active noise-reducing headrest with virtual microphones, the noise attenuation achieved at the ears of the listener usually decreases significantly as the head moves away from the central seat position. This paper presents a study on designing an active headrest with robust performance against head movement. To solve this problem, a minimax optimization problem is presented to design appropriate plant models for the system. Experiments are carried out on an active headrest system with the remote microphone technique. Experimental results show that the proposed method can extend the lateral head movement range from 2 cm to about 6 cm, within which the active headrest provides noise attenuation of greater than 10 dB for both ears of the listener, and thus improve the performance robustness of the active headrest system.
Internet of Things (IoT) aims to create a vast network with billions of things that can seamlessly create and exchange data, establishing intelligent interactions between people and objects around them. It is characterized with openness, heterogeneity, and dynamicity, which inevitably introduce severe security, privacy, and trust issues that hinder the widespread application of IoT. Trust management (TM) holds great promise in identifying malicious nodes, maintaining trust relationships, and enhancing system security. Traditional TM systems (TMSs) can be classified into centralized, semi-centralized, and distributed ones, all three of which suffer from critical challenges and thus are not sufficient for facilitating IoT development. Blockchain, as a disruptive technology, can help addressing the challenges of TM in IoT, thanks to its advanced features, such as decentralization, consistency, and tamper-proofing. As a result, blockchain-based TM (BC-TM) has been extensively studied in recent years to achieve decentralized TM in IoT. However, it still lacks a comprehensive survey on the current state of the arts. To fill this gap, in this article, we conduct a serious survey on BC-TM in IoT. We first propose a set of evaluation criteria that should be met by a TMS in IoT. Then, we propose a taxonomy of TMSs and continue with a thorough review on BC-TM in IoT by employing the proposed criteria. In the end, based on the review, a series of open issues are identified, and future research directions are suggested.
Trust evaluation assesses trust relationships between entities and facilitates decision-making. Machine Learning (ML) shows great potential for trust evaluation owing to its learning capabilities. In recent years, Graph Neural Networks (GNNs), as a new ML paradigm, have demonstrated superiority in dealing with graph data. This has motivated researchers to explore their use in trust evaluation, as trust relationships among entities can be modeled as a graph. However, current trust evaluation methods that employ GNNs fail to fully satisfy the dynamic nature of trust, overlook the adverse effects of trust-related attacks, and cannot provide convincing explanations on evaluation results. To address these problems, we propose TrustGuard, a GNN-based accurate trust evaluation model that supports trust dynamicity, is robust against typical attacks, and provides explanations through visualization. Specifically, TrustGuard is designed with a layered architecture that contains a snapshot input layer, a spatial aggregation layer, a temporal aggregation layer, and a prediction layer. Among them, the spatial aggregation layer adopts a defense mechanism to robustly aggregate local trust, and the temporal aggregation layer applies an attention mechanism for effective learning of temporal patterns. Extensive experiments on two real-world datasets show that TrustGuard outperforms state-of-the-art GNN-based trust evaluation models with respect to trust prediction across single-timeslot and multi-timeslot, even in the presence of attacks. In addition, TrustGuard can explain its evaluation results by visualizing both spatial and temporal views.
Presents an efficient pipeline architecture to perform gray-scale morphologic operations. The features of the architecture are 1) lower hardware cost, 2) faster operation time in processing an image, 3) lower data access times from the image memory, 4) shorter latency, 5) suitability for VLSI implementation, and 6) adaptability for N*N morphologic operations.< >
With the increasingly widespread use of personal portable devices, it is essential to devise an efficient method for spoken data retrieval for its resource-limited identity. This investigation proposes two efficient feature-based sentence-matching algorithms for speaker-dependent personal spoken sentence retrieval. Such a system can efficiently retrieve database sentences only partially matched to query sentence inputs. The query and database sentences are initially segmented into equal-sized matching units. A matching plane that comprises matching blocks is then created. For each matching block, a local similarity score is then determined from the feature distance. A whole-matching-plane-based accumulation scheme and a column-based row-based accumulation scheme are then designed to determine the global similarity score. The global similarity score of the matching plane reveals the similarity between the query and database sentences. The proposed algorithms are based on the feature-level comparison and do not require acoustical and language models. Experiments on news titles and personal schedules were conducted. The experimental results show that the proposed algorithms can efficiently work on both PC and HP iPAQ H5550 PDA.
Speech recognition systems driven by Deep Neural Networks (DNNs) have revolutionized human-computer interaction through voice interfaces, which significantly facilitate our daily lives.However, the growing popularity of these systems also raises special concerns on their security, particularly regarding backdoor attacks.A backdoor attack inserts one or more hidden backdoors into a DNN model during its training process, such that it does not affect the model's performance on benign inputs, but forces the model to produce an adversary-desired output if a specific trigger is present in the model input.Despite the initial success of current audio backdoor attacks, they suffer from the following limitations: (i) Most of them require sufficient knowledge, which limits their widespread adoption.(ii) They are not stealthy enough, thus easy to be detected by humans.(iii) Most of them cannot attack live speech, reducing their practicality.To address these problems, in this paper, we propose FlowMur, a stealthy and practical audio backdoor attack that can be launched with limited knowledge.FlowMur constructs an auxiliary dataset and a surrogate model to augment adversary knowledge.To achieve dynamicity, it formulates trigger generation as an optimization problem and optimizes the trigger over different attachment positions.To enhance stealthiness, we propose an adaptive data poisoning method according to Signal-to-Noise Ratio (SNR).Furthermore, ambient noise is incorporated into the process of trigger generation and data poisoning to make FlowMur robust to ambient noise and improve its practicality.Extensive experiments conducted on two datasets demonstrate that FlowMur achieves high attack performance in both digital and physical settings while remaining resilient to state-ofthe-art defenses.In particular, a human study confirms that triggers generated by FlowMur are not easily detected by participants.
To address the barrier caused by the black-box nature of Deep Learning (DL) for practical deployment, eXplainable Artificial Intelligence (XAI) has emerged and is developing rapidly. While significant progress has been made in explanation techniques for DL models targeted to images and texts, research on explaining DL models for graph data is still in its infancy. As Graph Neural Networks (GNNs) have shown superiority over various network analysis tasks, their explainability has also gained attention from both academia and industry. However, despite the increasing number of GNN explanation methods, there is currently neither a fine-grained taxonomy of them, nor a holistic set of evaluation criteria for quantitative and qualitative evaluation. To fill this gap, we conduct a comprehensive survey on existing explanation methods of GNNs in this paper. Specifically, we propose a novel four-dimensional taxonomy of GNN explanation methods and summarize evaluation criteria in terms of correctness, robustness, usability, understandability, and computational complexity. Based on the taxonomy and criteria, we thoroughly review the recent advances in GNN explanation methods and analyze their pros and cons. In the end, we identify a series of open issues and put forward future research directions to facilitate XAI research in the field of GNNs.
With the proliferating of electric vehicles, charging infrastructure has developed rapidly. Meanwhile, security issues of smart charging station system are becoming increasingly serious. In this paper, we systematically analyze the architecture of the charging station system and the protocols between electric vehicles and charging stations. Furthermore, we present the security issues and challenges of the charging station system including charging protocol, TCU, and the communication between the charging station and the vehicle networking platform. As far as we know, our work is the first practical and systematic work for the security analysis of smart electric vehicle charging stations system.
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