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MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition

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arXiv 2023年

作者： Guo, Jingcai Xu, Yuanyuan Xu, Wenchao Zhan, Yufeng Sun, Yuxia Guo, Song Department of Computing The Hong Kong Polytechnic University Hong Kong The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen518057 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Automation Beijing Institute of Technology Beijing100081 China Department of Computer Science Jinan University Guangzhou510632 China Guangdong Provincial Key Laboratory of High-Performance Computing Guangzhou510275 China

Malware open-set recognition (MOSR) aims at jointly classifying malware samples from known families and detect the ones from novel unknown families, respectively. Existing works mostly rely on a well-trained classifier considering the predicted probabilities of each known family with a threshold-based detection to achieve the MOSR. However, our observation reveals that the feature distributions of malware samples are extremely similar to each other even between known and unknown families. Thus the obtained classifier may produce overly high probabilities of testing unknown samples toward known families and degrade the model performance. In this paper, we propose the Multi-modal Dual-Embedding Networks, dubbed MDENet, to take advantage of comprehensive malware features from different modalities to enhance the diversity of malware feature space, which is more representative and discriminative for down-stream recognition. Concretely, we first generate a malware image for each observed sample based on their numeric features using our proposed numeric encoder with a re-designed multi-scale CNN structure, which can better explore their statistical and spatial correlations. Besides, we propose to organize tokenized malware features into a sentence for each sample considering its behaviors and dynamics, and utilize language models as the textual encoder to transform it into a representable and computable textual vector. Such parallel multi-modal encoders can fuse the above two components to enhance the feature diversity. Last, to further guarantee the open-set recognition, we dually embed the fused multi-modal representation into one primary space and an associated subspace, i.e., discriminative and exclusive spaces, with contrastive sampling and ρ-bounded enclosing sphere regularizations, which resort to classification and detection, respectively. Moreover, we also enrich our previously proposed large-scaled malware dataset MAL-100 with multi-modal characteristics and cont

关键词： Modal analysis

Quantum advantage for differential equation analysis

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Physical Review A 2022年第2期105卷 022415-022415页

作者： Bobak Toussi Kiani Giacomo De Palma Dirk Englund William Kaminsky Milad Marvian Seth Lloyd Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Giacomo De Palma Scuola Normale Superiore 56126 Pisa Italy QuEra Computing Boston Massachusetts 02143 USA Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical and Computer Engineering Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131 USA

Quantum algorithms for differential equation solving, data processing, and machine learning potentially offer an exponential speedup over all known classical algorithms. However, there also exist obstacles to obtaining this potential speedup in useful problem instances. The essential obstacle for quantum differential equation solving is that outputting useful information may require difficult postprocessing, and the essential obstacle for quantum data processing and machine learning is that inputting the data is a difficult task just by itself. In this study, we demonstrate that, when combined, these difficulties solve one another. We show how the output of quantum differential equation solving can serve as the input for quantum data processing and machine learning, allowing dynamical analysis in terms of principal components, power spectra, and wavelet decompositions. To illustrate this, we consider continuous-time Markov processes on epidemiological and social networks. These quantum algorithms provide an exponential advantage over existing classical Monte Carlo methods.

关键词： Machine learning Quantum algorithms Quantum computation Markovian processes Numerical techniques

Authentication of Vehicles and Road Side Units in Intelligent Transportation System

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computers, Materials & Continua 2020年第7期64卷 359-371页

作者： Muhammad Waqas Shanshan Tu Sadaqat Ur Rehman Zahid Halim Sajid Anwar Ghulam Abbas Ziaul Haq Abbas Obaid Ur Rehman Beijing Key Laboratory of Trusted Computing Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science&Engineering Ghulam Ishaq Khan Institute of Engineering Sciences&TechnologyTopi23460Pakistan Beijing Electro-Meahnical Engineering Institute Beijing100074China Faculty of Electrical Engineering Ghulam Ishaq Khan Institute of Engineering Sciences&TechnologyTopi23460Pakistan Department of Electrical Engineering Sarhad University of Science and Information TechnologyPeshawar25000Pakistan

Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents,economically damaging traffic jams,hijacking,motivating to wrong routes,and financial losses for businesses and *** and autonomous vehicles are connected wirelessly,which are more attracted for attackers due to the open nature of wireless *** of the problems is the rogue attack,in which the attacker pretends to be a legitimate user or access point by utilizing fake *** figure out the problem of a rogue attack,we propose a reinforcement learning algorithm to identify rogue nodes by exploiting the channel state information of the communication *** consider the communication link between vehicle-to-vehicle,and *** evaluate the performance of our proposed technique by measuring the rogue attack probability,false alarm rate(FAR),mis-detection rate(MDR),and utility function of a receiver based on the test threshold values of reinforcement learning *** results show that the FAR and MDR are decreased significantly by selecting an appropriate threshold value in order to improve the receiver’s utility.

关键词： Intelligent transportation system authentication rogue attack

Theoretical Analysis of Impact of Delayed Updates on Decentralized Federated Learning

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arXiv 2023年

作者： Zeng, Yong Liu, Siyuan Xu, Zhiwei Tian, Jie Sichuan Tengden Technology Co. Ltd China College of Information Engineering Inner Mongolia University of Technology Hohhot100080 China Institute of Computing Technology Chinese Academy of Sciences Beijing53035 China Haihe Laboratory of Information Technology Application Innovation Tianjin300350 China Department of Computer Science New Jersey Institute of Technology NewarkNJ07102 United States

Decentralized Federated learning is a distributed edge intelligence framework by exchanging parameter updates instead of training data among participators, in order to retrain or fine-tune deep learning models for mobile intelligent applications. Considering the various topologies of edge networks in mobile internet, the impact of transmission delay of updates during model training is non-negligible for data-intensive intelligent applications on mobile devices, e.g., intelligent medical services, automated driving vehicles, etc.. To address this problem, we analyze the impact of delayed updates for decentralized federated learning, and provide a theoretical bound for these updates to achieve model convergence. Within the theoretical bound of updating period, the latest versions for the delayed updates are reused to continue aggregation, in case the model parameters from a specific neighbor are not collected or updated in time. Copyright © 2023, The Authors. All rights reserved.

关键词： Topology

STOPPAGE: Spatio-temporal data driven cloud-fog-edge computing framework for pandemic monitoring and management

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arXiv 2021年

作者： Ghosh, Shreya Mukherjee, Anwesha Ghosh, Soumya K. Buyya, Rajkumar Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science Mahishadal Raj College West Bengal India Laboratory School of Computing and Information Systems University of Melbourne Australia

Several researches and evidence show the increasing likelihood of pandemics (large-scale outbreaks of infectious disease) which has far reaching sequels in all aspects of human lives ranging from rapid mortality rates to economic and social disruption across the world. In the recent time, COVID-19 (Coronavirus Disease 2019) pandemic disrupted normal human lives, and motivated by the urgent need of combating COVID-19, researchers have put significant efforts in modelling and analysing the disease spread patterns for effective preventive measures (in addition to developing pharmaceutical solutions, like vaccine). In this regards, it is absolutely necessary to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data-sources to deliver insights in improving administrative policy and enhance the preparedness to combat the pandemic. Specifically, human mobility, travel history and other transport statistics have significant impacts on the spread of any infectious disease. In this direction, this paper proposes a spatio-temporal knowledge mining framework, named STOPPAGE to model the impact of human mobility and other contextual information over large geographic area in different temporal scales. The framework has two major modules: (i) Spatio-temporal data and computing infrastructure using fog/edge based architecture;and (ii) Spatio-temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. Typically, we develop a Pandemic-knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hot-spot zones;and provide necessary support in home-health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real-life datasets related to COVID-19 in India illustrate the efficacy of the proposed methods. © 2021, CC BY.

关键词： Coronavirus

A Time-Driven Cloudlet Placement Strategy for Workflow Applications in Wireless Metropolitan Area Networks

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Research Square

Research Square 2021年

作者： Zhang, Jianshan Li, Ming Zheng, Xianghan Hsu, Ching-Hsien College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory Network Computing and Intelligent Information Processing Fuzhou China Department of Computer Science and Information Engineering Asia University Taichung Taiwan

With the rapid development of mobile technology, mobile applications have increasing requirements for computational resources, and mobile devices can no longer meet these requirements. Mobile edge computing (MEC) has emerged in this context and has brought innovation into the working mode of traditional cloud computing. By provisioning edge server placement, the computing power of the cloud centre is distributed to the edge of the network. The abundant computational resources of edge servers compensate for the lack of mobile devices and shorten the communication delay between servers and users. Constituting a specific forms of edge servers, cloudlets have been widely studied within academia and industry in recent years. However, existing studies have mainly focused on computation offloading for general computing tasks under fixed cloudlet placement positions. They ignored the impact on computation offloading results from cloudlet placement positions and data dependencies among mobile application components. In this paper, we study the cloudlet placement problem based on Workflow Applications (WAs) in Wireless Metropolitan Area Networks (WMANs). We devise a cloudlet placement strategy based on a Particle swarm optimization algorithm using Genetic algorithm operators with the Encoding Library updating mode (PGEL), which enables the cloudlet to be placed in appropriate positions. The simulation results show that the proposed strategy can obtain a near-optimal cloudlets placement scheme, and compared with other classic algorithms, this algorithm can reduce the execution time of WAs by 15.04%-44.99%. © 2021, CC BY.

关键词： computation offloading

Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

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SSRN

SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

BIG-FUSION: Brain-Inspired Global-Local Context Fusion Framework for Multimodal Emotion Recognition in Conversations 39

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BIG-FUSION: Brain-Inspired Global-Local Context Fusion Frame...

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Yusong Fang, Xuanye Yin, Huifeng Li, Dongyuan Li, Guoqi Xu, Qi Xu, Yi Zhong, Shuai Xu, Mingkun Guangdong Institute of Intelligence Science and Technology Hengqin Zhuhai China Department of Information and Communications Engineering Tokyo Institute of Technology Tokyo Japan School of Computer Science and Technology Dalian University of Technology Dalian China Center for Brain Inspired Computing Research (CBICR) Department of Precision Instrument Tsinghua University Beijing China Institute of Automation Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)157735897X

Considering the importance of capturing both global conversational topics and local speaker dependencies for multimodal emotion recognition in conversations, current approaches first utilize sequence models like Transformer to extract global context information, then apply Graph Neural Networks to model local speaker dependencies for local context information extraction, coupled with Graph Contrastive Learning (GCL) to enhance node representation learning. However, this sequential design introduces potential biases: the extracted global context information inevitably influences subsequent processing, compromising the independence and diversity of the original local features;current graph augmentation methods in GCL cannot consider both global and local context information in conversations to evaluate the node importance, hindering the learning of key information. Inspired by the human brain excels at handling complex tasks by efficiently integrating local and global information processing mechanisms, we propose an aligned global-local context fusion framework for sequence-based design to address these problems. This design includes a dual-attention Transformer and a dual-evaluation method for graph augmentation in GCL. The dual-attention Transformer combines global attention for overall context extraction with sliding-window attention for local context capture. Meanwhile, spiking neuron dynamics are introduced to enhance the representation capability of the extracted features, supporting the effective the interaction between different modalities from these global and local cues. The dual-evaluation method in GCL comprises global importance evaluation to identify nodes crucial for overall conversation context, and local importance evaluation to detect nodes significant for local semantics, generating augmented graph views that preserve both global and local information. This approach ensures balanced information processing throughout the pipeline, enhancing biologica

关键词： Graph neural networks

Wireless Hallucination in Generative AI-enabled Communications: Concepts, Issues, and Solutions

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arXiv 2025年

作者： Wang, Xudong Wang, Jiacheng Feng, Lei Niyato, Dusit Zhang, Ruichen Kang, Jiawen Xiong, Zehui Du, Hongyang Mao, Shiwen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn United States

Generative AI (GenAI) is driving the intelligence of wireless communications. Due to data limitations, random generation, and dynamic environments, GenAI may generate channel information or optimization strategies that violate physical laws or deviate from actual real-world requirements. We refer to this phenomenon as wireless hallucination, which results in invalid channel information, spectrum wastage, and low communication reliability but remains underexplored. To address this gap, this article provides a comprehensive concept of wireless hallucinations in GenAI-driven communications, focusing on hallucination mitigation. Specifically, we first introduce the fundamental, analyze its causes based on the GenAI workflow, and propose mitigation solutions at the data, model, and post-generation levels. Then, we systematically examines representative hallucination scenarios in GenAI-enabled communications and their corresponding solutions. Finally, we propose a novel integrated mitigation solution for GenAI-based channel estimation. At the data level, we establish a channel estimation hallucination dataset and employ generative adversarial networks (GANs)-based data augmentation. Additionally, we incorporate attention mechanisms and large language models (LLMs) to enhance both training and inference performance. Experimental results demonstrate that the proposed hybrid solutions reduce the normalized mean square error (NMSE) by 0.19, effectively reducing wireless hallucinations. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks