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Detecting Sybil Attack In Wireless Sensor Networks Using Machine Learning Algorithms

IOP Conference Series: Materials science and engineering 2021年第1期1042卷

作者： Mandala Mounica R. Vijayasaraswathi R. Vasavi M.Tech Student of Software Engineering Dept. of Computer Science & Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India Assistant Professor Dept. of Computer Science & Engineering VNR Vignana Jyothi Institute Of Engineering and Technology Hyderabad Telangana India

In sensitive areas such as battlefields, a Wireless Sensor Network (WSN) is especially in military and civilian applications and it is of utmost importance to develop security in these networks. In various respects, this can improve the quality of life. But to be used for protection reasons in multiple situations such as implementation. There is a high risk of being exposed to multiple viruses and hacking attacks. Unauthorized APs for information protection needs to be detected. Any malicious attacks against these networks, Like Sybil Attack leads to breach of security by enacting as a node that illegitimately declares several false identities at the same time. This misleads valid nodes, and they presume each of those identifiers as actual independent nodes by accident. Thus we proposed a machine learning model to detect Sybil attack in network where rawtraffic data has been collected and is used identify authorized and unauthorized APs in an integrated wired/wireless environment.

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Requirements Dependency Extraction by Integrating Active Learning with Ontology-Based Retrieval

Requirements Dependency Extraction by Integrating Active Lea...

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IEEE International Conference on Requirements engineering

作者： Gouri Deshpande Quim Motger Cristina Palomares Ikagarjot Kamra Katarzyna Biesialska Xavier Franch Guenther Ruhe Jason Ho dept. of Computer Science University of Calgary Calgary Canada dept. of Service and Information System Engineering (ESSI) Universitat Politècnica de Catalunya Barcelona Spain dept. of Electrical and Software Engineering University of Calgary Calgary Canada Blackline Safety. Inc Calgary Canada

ISBN: (数字)9781728174389

ISBN: (纸本)9781728174396

Context: Incomplete or incorrect detection of requirement dependencies has proven to result in reduced release quality and substantial rework. Additionally, the extraction of dependencies is challenging since requirements are mostly documented in natural language, which makes it a cognitively difficult task. Moreover, with ever-changing and new requirements, a manual analysis process must be repeated, which imposes extra hardship even for domain experts. Objective: The three main objectives of this research are: 1) Proposing a new dependency extraction method using a variant of Active Learning (AL). 2) Evaluating this AL and Ontology-based Retrieval (OBR) as baseline methods for dependency extraction on the two industrial data sets. 3) Analyzing the value gained from integrating these diverse approaches to form two hybrid methods. Method: Building on the general AL, ensemble and semi-supervised machine learning, a variant of AL was developed, which was further integrated with OBR to form two hybrid methods (Hybrid1, Hybrid2) for extracting three types of dependencies (requires, refines, other): Hybrid1 used OBR as a substitute for human expert; Hybrid2 used dependencies extracted through the OBR as an additional input for training set in AL. Results: For two industrial case studies, AL extracted more dependencies than OBR. Hybrid1 showed improvement for both data sets. For one of them, F1 score increased to 82.6% compared to the AL baseline score of 49.9%. Hybrid2 increased the accuracy by 25% to the level of 75.8% compared to the AL baseline accuracy. OBR also complemented the AL approach by reducing 50% of the human effort.

关键词： Training Ontologies Machine learning Data mining Electronic mail Requirements engineering Safety

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Towards automated boundary value testing with program derivatives and search

arXiv

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arXiv 2019年

作者： Feldt, Robert Dobslaw, Felix Dept. of Computer Science and Engineering Div. of Software Engineering Chalmers University of Technology Gothenburg Sweden

A natural and often used strategy when testing software is to use input values at boundaries, i.e. where behavior is expected to change the most, an approach often called boundary value testing or analysis (BVA). Even though this has been a key testing idea for long it has been hard to clearly define and formalize. Consequently, it has also been hard to automate. In this research note we propose one such formalization of BVA by, in a similar way as to how the derivative of a function is defined in mathematics, considering (software) program derivatives. Critical to our definition is the notion of distance between inputs and outputs which we can formalize and then quantify based on ideas from Information theory. However, for our (black-box) approach to be practical one must search for test inputs with specific properties. Coupling it with search-based software engineering is thus required and we discuss how program derivatives can be used as and within fitness functions. This brief note does not allow a deeper, empirical investigation but we use a simple illustrative example throughout to introduce the main ideas. By combining program derivatives with search, we thus propose a practical as well as theoretically interesting technique for automated boundary value (analysis and) testing. Copyright © 2019, The Authors. All rights reserved.

关键词： Information theory

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A two-level stacking model for detecting abnormal users in Wechat activities

A two-level stacking model for detecting abnormal users in W...

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Information Technology and computer Application (ITCA), International Conference on

作者： Jiayuan Ling Gangmin Li Dept. of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9781728164946

ISBN: (纸本)9781728164953

Machine learning algorithms are widely employed in plenty of classification or regression problems. While in real business world, it is confronted with huge and disorder data pattern. To recognize different kinds of users on the internet accurately and fast becomes a challenge. In a Wechat online bargain activity, the staff found that some strange users are highly like robots or malicious users. Thus we tried a two-level stacking model to detect them. This design got a good result of 0.98 accuracy after the training phase and an accuracy of 0.90 in a new term of the testing set. Moreover, this model is adaptable to linear and nonlinear datasets because of its diverse stacking of first-level classifiers. Therefore, this paper indicates a potential of the stacking classification model in big data times.

关键词： Stacking Companies Support vector machines Robots Data models Training Classification algorithms

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Deep Fusion Net for Coral Classification in Fluorescence and Reflectance Images

Deep Fusion Net for Coral Classification in Fluorescence and...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Uzair Nadeem Mohammed Bennamoun Ferdous Sohel Roberto Togneri Dept. of Computer Science and Software Engineering The University of Western Australia College of Science Health Engineering & Education Murdoch University Australia

Coral reefs are vital for marine ecosystem and fishing industry. Automatic classification of corals is essential for the preservation and study of coral reefs. However, significant intra-class variations and inter-class similarity among coral genera, as well as the challenges of underwater illumination present a great hindrance for the automatic classification. We propose an end-to-end trainable Deep Fusion Net for the classification of corals from two types of images. The network takes two simultaneous inputs of reflectance and fluorescence images. It is composed of three branches: Reflectance, Fluorescence and Integration. The branches are first trained individually and then fused together. Finally, the Deep Fusion Net is trained end-to-end for the classification of different coral genera and other non-coral classes. Experiments on the challenging Eliat Fluorescence Coral dataset show that the Deep Fusion net achieves superior classification accuracy compared to other methods.

关键词： Fluorescence Training Annotations Histograms Australia Industries Lighting

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Integrated software Development Methodology for Real-time Precise Control of UAV

Integrated Software Development Methodology for Real-time Pr...

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作者： Jung Kyu Park Eun Young Park Heung Seok Jeon Dept.of Computer Software Engineering Changshin Univ. Dept.Biomedical Laboratory science Shinhan University Dept.of Computer Engineering Konkuk University

Recently, drones have been widely used in education and industry. In this paper, we propose a navigation and control algorithm to develop unmanned drones quickly. To limit the proposed framework, we analysed the existing drone solution and simulation framework. We also used Hardware-in-the-Loop(HIL) simulation for rapid development. HIL will simulate physical systems that interface with embedded control equipment on real-time hardware. The code generated in this process can be experimented on a hardware device so that the control performance of the drones can be verified without draining the drones in the outdoor environment. Using the proposed method, you save time and money on drone development while providing stability.

关键词： Integrated software Development Methodology for Real-time Precise Control of UAV UAV

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Cell quantization tool for anterior chamber OCT images

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Transactions of the Korean Institute of Electrical Engineers 2020年第7期69卷 1073-1080页

作者： Lee, Yeongseop Kang, Taeseen Han, Yongseop Kim, Jinhyun Kim, Kyong Hoon Lee, Seongjin Dept. of Aerospace and Software Engineering Gyeongsang National University Korea Republic of Dept. of Informatics Gyeongsang National University Korea Republic of Dept of Ophthalmology Gyeongsang National University Changwon Hospital Changwon Korea Republic of Dept of Ophthalmology College of Medicine Gyeongsang National University Jinju Korea Republic of Dept of Information and Communication Engineering Gyeongsang University Korea Republic of School of Computer Science and Engineering Kyungpook National University Korea Republic of

In general, ophthalmologists visually grade the state of a patient by counting the cells within the anterior chamber OCT image. The manual cell counting method is highly inaccurate and spends a lot of time to determine the progress of the patient. In this work, we develop a new tool to count cells in anterior chamber OCT images to aid doctors in analyzing the state of patients. We exploit image processing to remove noises from images, segment the anterior chamber, and quantize the cells in OCT images. We also provide statistics to aid the doctors in determining the progress of the patients. © Korean Institute of Electrical Engineers

关键词： Cells

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MoCoP: Towards a Model Clone Portal

MoCoP: Towards a Model Clone Portal

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International Workshop on Modeling in software engineering (MISE)

作者： Önder Babur Matthew Stephan Dept. of Mathematics & Computer Science Eindhoven University of Technology Eindhoven The Netherlands Dept. of Computer Science & Software Engineering Miami University Oxford Ohio USA

Widespread and mature practice of model-driven engineering is leading to a growing number of modeling artifacts and challenges in their management. Model clone detection (MCD) is an important approach for managing and maintaining modeling artifacts. While its counterpart in traditional source code development, code clone detection, is enjoying popularity and more than two decades of development, MCD is still in its infancy in terms of research and tooling. We aim to develop a portal for model clone detection, MoCoP, as a central hub to mitigate adoption barriers and foster MCD research. In this short paper, we present our vision for MoCoP and its features and goals. We discuss MoCoP's key components that we plan on realizing in the short term including public tooling, curated data sets, and a body of MCD knowledge. Our longer term goals include a dedicated service-oriented infrastructure, contests, and forums. We believe MoCoP will strengthen MCD research, tooling, and the community, which in turn will lead to better quality, maintenance, and scalability for model-driven engineering practices.

关键词： Unified modeling language Cloning Tools Analytical models Portals Charge coupled devices software

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Question to Question Similarity Analysis using Morphological, Syntactic, Semantic, and Lexical Features

Question to Question Similarity Analysis using Morphological...

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ACS/IEEE International Conference on computer Systems and Applications

作者： Muntaha Al-Asa'd Nour Al-Khdour Mutaz Bni Younes Enas Khwaileh Mahmoud Hammad Mohammad AL-Smadi Dept. of Computer Science Jordan University of Science and Technology Irbid Jordan Dept. of Software Engineering Jordan University of Science and Technology Irbid Jordan

ISBN: (数字)9781728150529

ISBN: (纸本)9781728150536

In the digitally connected world that we are living in, people expect to get answers to their questions spontaneously. This fact increased the burden on the Question/Answer platforms such as Stack Overflow and many others. A promising solution to this problem is to detect if a question being asked similar to a question in the database and present the answer of the detected question to the user. To address this challenge, we propose a novel Natural Language Processing (NLP) approach that detects if two Arabic questions are similar or not using their extracted morphological, syntactic, semantic, lexical features. Our approach involves several phases including Arabic text processing, novel feature extraction, and text classifications. To conduct our experiments, we used a real-world questions dataset consisting of 4,000 pairs of Arabic questions in which our approach achieved 78.2% accuracy using XGBoost model on the best features selected by the Random Forest feature selection technique. This high accuracy shows the ability of our approach to correctly detect the similarity between two Arabic questions.

关键词： Feature extraction Semantics Syntactics Morphology Machine learning Support vector machines computer science

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Network2Vec: Learning Node Representation Based on Space Mapping in Networks

Network2Vec: Learning Node Representation Based on Space Map...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Zhenhua Huang Zhenyu Wang Rui Zhang Yangyang Zhao Xiaohui Xie Sharad Mehrotra School of Software Engineering South China University of Technology Dept. of Computer Science University of California Irvine

ISBN: (纸本)9781728148977

Complex networks represented as node adjacency matrices constrains the application of machine learning and parallel algorithms. To address this limitation, network embedding (i.e., graph representation) has been intensively studied to learn a fixed-length vector for each node in an embedding space, where the node properties in the original graph are preserved. Existing methods mainly focus on learning embedding vectors to preserve nodes proximity, i.e., nodes next to each other in the graph space should also be closed in the embedding space, but do not enforce algebraic statistical properties to be shared between the embedding space and graph space. In this work, we propose a lightweight model, entitled Network2Vec, to learn network embedding on the base of semantic distance mapping between the graph space and embedding space. The model builds a bridge between the two spaces leveraging the property of group homomorphism. Experiments on different learning tasks, including node classification, link prediction, and community visualization, demonstrate the effectiveness and efficiency of the new embedding method, which improves the state-of-the-art model by 19% in node classification and 7% in link prediction tasks at most. In addition, our method is significantly faster, consuming only a fraction of the time used by some famous methods.

关键词： Task analysis Markov processes Matrix decomposition software engineering Bridges Mathematical model Probability

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