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LMP-GAN: Out-Of-Distribution Detection For Non-Control Data Malware Attacks

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wood, David Kapp, David Kebede, Temesgen Hirakawa, Keigo Wuhan University School of Computer Science China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering China Zhongguancun Academy China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing China Sun Yat-sen University School of Geography and Planning China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Chongqing University College of Computer Science China The University of Tokyo Japan RIKEN Center for Advanced Intelligence Project Japan Intelligent Science & Technology Academy Limited CASIC China iFlytek Company Ltd. National Engineering Research Center of Speech and Language Information Processing China Nanyang Technological University College of Computing & Data Science Singapore Henan Academy of Sciences Aerospace Information Research Institute China

Anomaly detection is a common application of machine learning. Out-of-distribution (OOD) detection in particular is a semi-supervised anomaly detection technique where the detection method is trained only on the inlier (in-distribution) samples - unlike the fully supervised variant, the distribution of the outlier samples are never explicitly modeled in OOD detection tasks. In this work, we design a novel GAN-based OOD detection network specifically designed to protect a cyber-physical signal systems from novel Trojan malware called non-control data (NCD) attack that evades conventional malware detection techniques. Inspired in part by the classical locally most powerful (LMP) test in statistical inferences, the proposed LMP-GAN trains the OOD detector (discriminator) by generating OOD samples that are aimed at making maximal alteration to the inlier samples while evading detection. We experimentally compare the results to the state-of-the-art anomaly detection methods to demonstrate the benefits and the appropriateness of the LMP-GAN OOD detector. © 2025 IEEE.

关键词： Malware

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WOW: Self-organizing wide area overlay networks of virtual workstations

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Journal of Grid computing 2007年第2期5卷 151-172页

作者： Ganguly, A. Agrawal, A. Boykin, P.O. Figueiredo, Renato J. Advanced Computing and Information Systems Laboratory University of Florida Gainesville FL 32601 United States

This paper describes WOW, a distributed system that combines virtual machine, overlay networking and peer-to-peer techniques to create scalable wide-area networks of virtual workstations for high-throughput computing. The system is architected to: facilitate the addition of nodes to a pool of resources through the use of system virtual machines (VMs) and self-organizing virtual network links;to maintain IP connectivity even if VMs migrate across network domains;and to present to end-users and applications an environment that is functionally identical to a local-area network or cluster of workstations. We describe IPOP, a network virtualization technique that builds upon a novel, extensible user-level decentralized technique to discover, establish and maintain overlay links to tunnel IP packets over different transports (including UDP and TCP) and across firewalls. We evaluate latency and bandwidth overheads of IPOP and also time taken for a new node to become fully-routable over the virtual network. We also report on several experiments conducted on a testbed WOW deployment with 118 P2P router nodes over PlanetLab and 33 VMware-based VM nodes distributed across six firewalled domains. Experiments show that the testbed delivers good performance for two unmodified, representative benchmarks drawn from the life-sciences domain. We also demonstrate that the system is capable of seamlessly maintaining connectivity at the virtual IP layer for typical client/server applications (NFS, SSH, PBS) when VMs migrate across a WAN. © Springer Science + Business Media B.V. 2007.

关键词： Wide area networks

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ETL transformation algorithm for Facebook opinion data 11

ETL transformation algorithm for Facebook opinion data

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11th International Conference on Web information systems and Technologies, WEBIST 2015

作者： Walha, Afef Ghozzi, Faiza Gargouri, Faïez Multimedia InfoRmation Systems and Advanced Computing Laboratory Sfax Tunisia Institute of Computer Science and Multimedia Sfax University Sfax Tunisia

ISBN: (纸本)9789897581069

Considered as a rich source of information, social networking sites have been created lot of buzz because people share and discuss their opinions freely. Sentiment analysis is used for knowing voice or response of crowd for products, services, organizations, individuals, events, etc. Due to their importance, people opinions are analyzed in several domains including information retrieval, semantic web, text mining. These researches define new classification techniques to assign positive or negative opinion. Decisional systems like WeBhouse, known by their data-consuming must be enriched by this kind of pertinent opinions to give better help to decision makers. Nevertheless, cleaning and transformation processes recognized by several approaches as a key of WeBhouse development, don't deal with sentiment analysis. To fulfill this gap, we propose a new analysis algorithm which determines user's sentiment score of a post shared on the social network Facebook. This algorithm analyzes user's opinion depending on opinion terms and emoticons included in his comments. This algorithm is integrated in transformation process of ETL approach.

关键词： Sentiment analysis

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24th KES International Conference on Knowledge-Based and Intelligent information and Engineering systems, KES 2020

作者： Ammar, Randa Ben Ayed, Yassine Ben Multimedia Information System and Advanced Computing Laboratory Sfax University Tunisia

Alzheimer's disease (AD) is the most leading symptom of neurodegenerative dementia;AD is defined now as one of the most costly chronic diseases. For that automatic diagnosis and control of Alzheimer's disease may have a significant effect on society along with patient well-being. Language disorder is regarded to be among the most common symptoms of AD, as a direct and natural result of cognitive impairment. Hence, the diagnosis of Alzheimer's disease using speech-based features is gaining growing attention. The aim of this study is to extract linguistic features following a proposed taxonomy of language impairment of AD patients. Obtained results indicate that the proposed taxonomy of the linguistic features extracted from the speech samples can be used to differentiate between Alzheimer's disease patients and the healthy control group. Support Vector Machine (SVM) classifier obtained classification accuracy over 90 percent. © 2020 The Authors. Published by Elsevier B.V.

关键词： Taxonomies

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Verify All Traffic: Towards Zero-Trust In-Network Intrusion Detection against Multipath Routing

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2155-2171页

作者： Zhao, Ziming Li, Zhaoxuan Xie, Xiaofei Liu, Zhipeng Li, Tingting Yu, Jiongchi Zhang, Fan Chen, Binbin School of Software Technology Zhejiang University Ningbo315100 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Scientific and Technological Innovation Center 311200 China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province 310027 China Jiaxing Research Institute Zhejiang University 314000 China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou450001 China Singapore State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security UCAS Beijing100049 China School of Computing and Information Systems Singapore Management University Singapore188065 Singapore Advanced Digital Sciences Center Singapore138632 Singapore Singapore University of Technology and Design SingaporeB96049 Singapore

With the popularity of encryption protocols, machine learning (ML)-based traffic analysis technologies have attracted widespread attention. To adapt to modern high-speed bandwidth, recent research is dedicated to advancing zero-trust intrusion detection by offloading feature extraction and model inference into the network dataplane. Especially, with the rise of programmable switches, achieving line-speed ML inference becomes promising. However, existing research only considers a single switch node as a relay to conduct evaluation. This is far from real-world deployments involving multiple switches (given that zero-trust security assumes that threats can originate from anywhere, including within the network), particularly the multi-path routing phenomenon that exists in practice. In this paper, we reveal practical challenges in the context of enabling line-speed model inference in the network dataplane. Furthermore, we propose FCPlane, the forwarding and computing integrated dataplane for zero-trust intrusion detection that aims to enable efficient load balancing while providing reliable traffic analysis results, even against multipath routing. The core idea is to reconcile forwarding and computation to the flowlet level, for which a tailor-made Markov chain model is designed. Based on two public traffic datasets, we evaluate seven state-of-the-art in-network traffic analysis models deployed in four types of topologies (three with multipath routing and one without) to explore performance impact and demonstrate the effectiveness of our proposal. © 1983-2012 IEEE.

关键词： Network intrusion

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Detection of semantic relationships between terms with a new statistical method

Detection of semantic relationships between terms with a new...

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10th International Conference on Web information systems and Technologies, WEBIST 2014

作者： Ksentini, Nesrine Tmar, Mohamed Gargouri, Faïez MIRACL: Multimedia InfoRmation Systems and Advanced Computing Laboratory University of Sfax Higher Institute of Computer Science and Multimedia of Sfax Sfax Tunisia

ISBN: (纸本)9789897580246

Semantic relatedness between terms plays an important role in many applications, such as information retrieval, in order to disambiguate document content. This latter is generally studied among pairs of terms and is usually presented in a non-linear way. This paper presents a new statistical method for detecting relationships between terms called Least Square Mehod which defines these relations linear and between a set of terms. The evaluation of the proposed method has led to optimal results with low error rate and meaningful relationships. Experimental results show that the use of these relationships in query expansion process improves the retrieval results.

关键词： information retrieval

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Medical case-based retrieval by using a language model: MIRACL at imageCLEF 2012

Medical case-based retrieval by using a language model: MIRA...

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2012 Cross Language Evaluation Forum Conference, CLEF 2012

作者： Majdoubi, Jihen Loukil, Hatem Tmar, Mohamed Gargouri, Faiez Multimedia InfoRmation system and Advanced Computing Laboratory Higher Institute of Information Technologie and Multimedia University of sfax Tunisia

This paper reports the experiment results of the MIRACL team in participating in the medical case retrieval task of ImageCLEF 2012. In this paper, we propose our contribution for conceptual indexing of medical article... 详细信息

关键词： Indexing (of information)

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A new approach for TV program identification based on video grammar 09

A new approach for TV program identification based on video ...

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7th International Conference on Advances in Mobile computing and multimedia, MoMM2009

作者： Zlitni, Tarek Mahdi, Walid Ben Abdallah, Hanène Mir at cl Multimedia Information Systems and Advanced Computing Laboratory Higher Institute of Computer Science and Multimedia BP 3021 Sfax 69042 Tunisia

ISBN: (纸本)9781605586595

In this paper, we propose a new approach to identify programs in TV streams. In the first step of our approach, we construct a reference catalogue for video grammars of visual jingles. In the second step, we identify programs in TV streams by examining the similarity of the video signal to the visual grammars in the catalogue. After presenting our approach, we report the results of its experimental evaluation on several streams extracted from different channels and composed of several programs. Copyright 2009 ACM.

关键词： Indexing (of information)

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Evaluation of acoustic features for early diagnosis of Alzheimer disease 19th

Evaluation of acoustic features for early diagnosis of Alzhe...

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19th International Conference on Intelligent systems Design and Applications, ISDA 2019

作者： Ammar, Randa Ben Ayed, Yassine Ben Multimedia Information System and Advanced Computing Laboratory Sfax University Sfax Tunisia

ISBN: (纸本)9783030493417

Automatic diagnosis and monitoring of Alzheimer’s Disease (AD) can have a significant impact on society as well as the well-being of patients. It is known that Alzheimer’s disease (AD) influences the language abilities of affected peoples. Hence, the diagnosis of Alzheimer’s disease using speech-based features is gaining growing attention. The purpose of this article is extracting a set of acoustic features from Dementia Bank conversations from subjects with and without Alzheimer’s disease. Extracted features will be trained with Machine Learning (ML) algorithms to testing the detection accuracy. Obtained results indicate that the proposed features extracted from the speech samples can be used to distinguish between the patients with Alzheimer’s disease and the healthy control group. Classification accuracy over 90% was obtained with Support Vector Machine (SVM) classifier. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Diagnosis

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A Deep Neural Network Model for Predicting User Behavior on Facebook

A Deep Neural Network Model for Predicting User Behavior on ...

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2019 International Joint Conference on Neural Networks, IJCNN 2019

作者： Ameur, Hanen Jamoussi, Salma Hamadou, Abdelmajid Ben Multimedia InfoRmation System and Advanced Computing Laboratory University of Sfax Sfax Tunisia

ISBN: (纸本)9781728119854

On Facebook, the social media site, liking, commenting and sharing the posts or status of other users are usually considered to be the key mechanisms for exchanging opinions about different topics. Due to the non-availability of data and security constraints, only few research studies have analyzed such behavior. In this paper, we introduced a novel deep neural network model for user behavior prediction (like and comment). We presented an embedding representation method for the textual content of comments and posts based on the contextual recursive auto-encoders model. The users were represented using a deep joint auto-encoders model to fuse the users' like and comment information, and train the users' combined embedding representation. Then, the user behaviors towards a given post were embedded into the same feature space of users and posts, using the joint auto-encoders model. Thereafter, we used a fully connected layer for behavior prediction. To train and evaluate the effectiveness of the proposed method, we also constructed a large dataset collected from Facebook. The experimental results show that the proposed method could achieve better results than the previous alternative methods. © 2019 IEEE.

关键词： Embeddings

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