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Data Augmented Hardware Trojan Detection Using Label Spreading algorithm Based Transductive Learning for Edge Computing-Assisted IoT Devices

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IEEE ACCESS 2022年 10卷 102789-102803页

作者： Sankar, Vaishnavi Nirmala Devi, M. Jayakumar, M. Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Commun Engn Coimbatore 641112 Tamil Nadu India

IoT devices handle a large amount of information including sensitive information pertaining to the deployed application. Such a scenario, makes IoT devices susceptible to various attacks. In addition to securing IoT devices, it is equally important to secure communication among devices and with the outside world. RS232 is a common communication protocol used in IoT and embedded devices. Hence ensuring, Trojan detection in RS232 plays a major role in providing secured communication among edge assisted IoT devices. The inclusion of malicious circuits known as hardware Trojans can occur at any stage of the IC design and manufacturing. Existing pre-silicon detection schemes with static features is limited by the number of features that are learned by the detection scheme. In contrast, machine learning allows enhanced Trojan space exploration. Existing machine learning-based Trojan detection consists primarily of supervised algorithms that rely on high-quality labeled datasets for efficient Trojan detection. Unsupervised methods, on the other hand, underperform due to limited training data and severe imbalance within the available data. To handle such a situation, a semi-supervised hardware Trojan detection has been proposed. In this work, permutation importance guided principal component analysis, correlation aware data augmentation, and hyper-parameter optimization using genetic algorithm aid in optimal dataset and model generation. Pseudo label generation using semi-supervised schemes is utilized to handle partially labeled datasets. For the Trust-HUB benchmarks, the proposed methodology achieves an average of 88.48% true positive rate and 95.77% true negative rate which, clearly indicates the effectiveness and feasibility of semi-supervised hardware Trojan detection.

关键词： Trojan horses Hardware Internet of Things Feature extraction Machine learning algorithms Prediction algorithms Data models semi-supervised algorithm hardware Trojan detection correlation-aware data augmentation hyper-parameter optimization genetic algorithm permutation importance XGBoost

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RETRACTED: Review of ensemble classification over data streams based on supervised and semi-supervised (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期43卷 3859-3878页

作者： Han, Meng Li, Xiaojuan Wang, Le Zhang, Ni Cheng, Haodong North Minzu Univ Sch Comp Sci & Engn Yinchuan Ningxia Peoples R China

Most data stream ensemble classification algorithms use supervised learning. This method needs to use a large number of labeled data to train the classifier, and the cost of obtaining labeled data is very high. Therefore, the semi supervised learning algorithm using labeled data and unlabeled data to train the classifier becomes more and more popular. This article is the first to review data stream ensemble classification methods from the perspectives of supervised learning and semi-supervised learning. Firstly, basic classifiers such as decision trees, neural networks, and support vector machines are introduced from the perspective of supervised learning and semi-supervised learning. Secondly, the key technologies in data stream ensemble classification are explained from the two aspects of incremental and online. Finally, the majority voting and weight voting are explained in the ensemble strategies. The different ensemble methods are summarized and the classic algorithms are quantitatively analyzed. Further research directions are given, including the handling of concept drift under supervised and semi-supervised learning, the study of homogeneous ensemble and heterogeneous ensemble, and the classification of data stream ensemble under unsupervised learning.

关键词： Review ensemble learning supervised algorithm semi-supervised algorithm

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Fighting against Web Spam: A Novel Propagation Method based on Click-through Data 12

Fighting against Web Spam: A Novel Propagation Method based ...

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35th ACM SIGIR Annual International Conference on Research and Development in Information Retrieval

作者： Wei, Chao Liu, Yiqun Zhang, Min Ma, Shaoping Ru, Liyun Zhang, Kuo Tsinghua Univ Dept Comp Sci & Technol Tsinghua Natl Lab Informat Sci & Technol State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China

ISBN: (纸本)9781450316583

Combating Web spam is one of the greatest challenges for Web search engines. State-of-the-art anti-spam techniques focus mainly on detecting varieties of spam strategies, such as content spamming and link-based spamming. Although these anti-spam approaches have had much success, they encounter problems when fighting against a continuous barrage of new types of spamming techniques. We attempt to solve the problem from a new perspective, by noticing that queries that are more likely to lead to spam pages/sites have the following characteristics: 1) they are popular or reflect heavy demands for search engine users and 2) there are usually few key resources or authoritative results for them. From these observations, we propose a novel method that is based on click-through data analysis by propagating the spamicity score iteratively between queries and URLs from a few seed pages/sites. Once we obtain the seed pages/sites, we use the link structure of the click-through bipartite graph to discover other pages/sites that are likely to be spam. Experiments show that our algorithm is both efficient and effective in detecting Web spam. Moreover, combining our method with some popular anti-spam techniques such as TrustRank achieves improvement compared with each technique taken individually.

关键词： Spam detection Web search engine click-through data/bipartite graph semi-supervised algorithm label propagation

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semi-supervised Metric Learning Using Composite Kernel

Semi-supervised Metric Learning Using Composite Kernel

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6th International Symposium on Telecommunications (IST) with Emphasis on Information and Communication Technology

作者： Zare, T. Sadeghi, M. T. Abutalebi, H. R. Yazd Univ Elect & Comp Engn Dept Signal Proc Res Grp Yazd Iran

ISBN: (纸本)9781467320733

Learning an appropriate distance metric using the available class labels or some other supervisory information is a very active research area. It has been shown that the metric learning based methods outperforms the traditionally used distance metrics such as the Euclidean distance metric. In kernelized version of metric learning algorithms, the data is implicitly transferred into a new feature space using a non-linear kernel function. The distance metric learning process is performed in the new feature space. Selecting an appropriate kernel function and/or tuning its parameters impose significant challenges in the kernel-based methods. Toward this goal, we present a semi-supervised metric learning algorithm using composite kernels. We demonstrate the usefulness of the proposed method on both synthetic and real-world data sets.

关键词： Distance Metric Learning Composite Kernel Similarity Pairs semi-supervised algorithm

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Background Knowledge Integration in Clustering Using Purity Indexes

Background Knowledge Integration in Clustering Using Purity ...

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4th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Forestier, Germain Wemmert, Cedric Gancarski, Pierre Univ Strasbourg Image Sci Comp Sci & Remote Sensing Lab Strasbourg France

ISBN: (纸本)9783642152795

In recent years, the use of background knowledge to improve the data mining process has been intensively studied. Indeed, background knowledge along with knowledge directly or indirectly provided by the user are often available. However, it is often difficult to formalize this kind of knowledge, as it is often dependent of the domain. In this article, we studied the integration of knowledge as labeled objects in clustering algorithms. Several criteria allowing the evaluation of the purity of a clustering are presented and their behaviours are compared using artificial datasets. Advantages and drawbacks of each criterion are analyzed in order to help the user to make a choice among them.

关键词： Clustering background knowledge semi-supervised algorithm purity indexes

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Real-Time Framework for Malware Detection Using Machine Learning Technique 2nd

Real-Time Framework for Malware Detection Using Machine Lear...

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2nd International Conference on Information and Communication Technology for Intelligent Systems (ICTIS)

作者： Mukesh, Sharma Divya Raval, Jigar A. Upadhyay, Hardik GTU PG Sch Ahmadabad Gujarat India Phys Res Lab Ahmadabad Gujarat India GPERI Mehsana Gujarat India

ISBN: (纸本)9783319636733;9783319636726

In this epoch, current web world where peoples groups are associated through correspondence channel and the majority of their information is facilitated on the web associated assets. Thusly the security is the significant concern of this internet community to protect the resources and to ensure the assets and the information facilitated on these networks. In current trends, the greater part of the end client are depending on the end security items, for example, Intrusion detection system, firewall, Anti-viruses etc. In this paper, we propose a machine learning based architecture to distinguish existing and recently developing malware by utilizing network and transport layer traffic features. This paper influences the precision of semi-supervised learning in identifying new malware classes. We show the adequacy of the framework utilizing genuine network traces. Amid this research, we will execute and design the proactive network security mechanism which will gather the malware traces. Assist those gathered malware traces can be utilized to fortify the signature based discovery mechanism.

关键词： Malware detection semi-supervised algorithm ClamAV Machine learning

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Naive Bayes using the expectation-maximization algorithm for reject inference

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Communications in Statistics Case Studies Data Analysis and Applications 2022年第3期8卷 484-504页

作者： Anderson, Billie UMKC Henry W. Bloch School of Management Kansas City MO United States

In the last several years, there has been significant research in applying semi-supervised machine learning models to the reject inference problem. When a financial institution wants to build a model to predict the default of credit applicants, the institution only has a known good/bad outcome loan status for the accepted applicants;this causes an inherent bias in the model. Reject inference is used to infer the good or bad loan status of credit applicants that were rejected by a financial institution. This paper presents a reject inference technique in which a semi-supervised framework is developed using a Naive Bayes model. The framework uses the expectation-maximization (EM) algorithm to incorporate rejected applicants into the parameter estimation of the model using a bootstrapping approach. The proposed method has an advantage over traditional reject inference methods because the rejected applicant data will participate in the estimation of the model parameters, thus avoiding the extrapolation problem. The Naive Bayes model using the EM algorithm is compared to logistic regression and several semi-supervised techniques. © 2022 Taylor & Francis Group, LLC.

关键词： Credit scoring expectation-maximization algorithm feature binning Naive Bayes reject inference semi-supervised algorithm

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Learning structured medical information from social media

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JOURNAL OF BIOMEDICAL INFORMATICS 2020年 110卷 103568-103568页

作者： Hasan, Abul Levene, Mark Weston, David Birkbeck Univ London Dept Comp Sci & Informat Syst London WC1E 7HX England

Our goal is to summarise and aggregate information from social media regarding the symptoms of a disease, the drugs used and the treatment effects both positive and negative. To achieve this we first apply a supervised machine learning method to automatically extract medical concepts from natural language text. In an environment such as social media, where new data is continuously streamed, we need a methodology that will allow us to continuously train with the new data. To attain such incremental re-training, a semi-supervised methodology is developed, which is capable of learning new concepts from a small set of labelled data together with the much larger set of unlabelled data. The semi-supervised methodology deploys a conditional random field (CRF) as the base-line training algorithm for extracting medical concepts. The methodology iteratively augments to the training set sentences having high confidence, and adds terms to existing dictionaries to be used as features with the base-line model for further classification. Our empirical results show that the base-line CRF performs strongly across a range of different dictionary and training sizes;when the base-line is built with the full training data the F, score reaches the range 84%-90%. Moreover, we show that the semi-supervised method produces a mild but significant improvement over the base-line. We also discuss the significance of the potential improvement of the semi-supervised methodology and found that it is significantly more accurate in most cases than the underlying base-line model.

关键词： Social media mining Medical concept extraction Pharmacovigilance Conditional random fields semi-supervised algorithm

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semi-supervised Metric Learning Using Composite Kernel

Semi-supervised Metric Learning Using Composite Kernel

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International Symposium on Telecommunications

作者： T. Zare M. T. Sadeghi H. R. Abutalebi Signal Processing Research Group Electrical and Computer Engineering Department Yazd University

ISBN: (纸本)9781467320726

关键词： Distance Metric Learning Composite Kernel Similarity Pairs semi-supervised algorithm

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