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A survey on cyber security threats and challenges in modem society

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A survey on cyber security threats and challenges in modem s...

2019 IEEE International Conference on Electro Information Technology, EIT 2019

作者： Sajal, Sayeed Z. Jahan, Israt Nygard, Kendall E. Dept. of Math and Computer Science Minot State University MinotND United States Dept. of Computer Science Nueta Hidatsa Sahnish College New TownND United States Dept. of Computer Science North Dakota State University FargoND United States

ISBN: (纸本)9781728109275

With the advancement of new technologies, modern society is surrounded by devices and services with internet connectivity. It provides us the better lifestyle, convenience of services and easy communications. But it opens many security flaws if we can't take proper security measures. Cybercrime might occur from any place at any time and the consequences are endless. Cybercrime is not limited to any specific area. It can be in any sectors like the financial institute, government institute, educational institute, health sectors and so on. We need a solution to protect us from any cyber crimes. Without knowing the possible cyber threats and challenges, it is very difficult to deal with cyber attacks. In this paper, the summary of major cybersecurity threats and challenges in modern society are presented and the solutions are proposed to protect from cyber threats. © 2019 IEEE.

关键词： Cybersecurity

SCDP: Smart contract-based decentralized privacy system for securing data ownership management 8

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SCDP: Smart contract-based decentralized privacy system for ...

8th IEEE Global Conference on Consumer Electronics, GCCE 2019

作者： He, Yunmin Chen, Yu-Chi Guo, Zhong-Yi Tso, Raylin Ye, Shaozhen College of Math. and Computer Sci. Fuzhou University Fuzhou China Yuan Ze University Dept. of Computer Science and Engineering Taoyuan Taiwan National Chengchi University Dept. of Computer Science Taipei Taiwan

ISBN: (纸本)9781728135755

Secure data ownership management is significant for realizing personal and private data sharing, which can be widely used with consumer electronics. The notion of the decentralized privacy (DP) is introduced by Zyskind et al., and accordingly the first DP system is implemented through blockchain and off-blockchain distributed hashtable. To address the efficiency and overhead issues, we present a conceptually simple solution directly from smart contracts with cryptographic primitives. Our system is called the smart contract-based decentralized privacy (SCDP) system. We propose the basic SCDP system as a warm-up to introduce the design principle based on symmetric encryption. Moreover, the strong SCDP system is provided by using ciphertext-policy attribute-based encryption for supporting more flexible scenarios of access control and also eliminating some limitations of the basic system. © 2019 IEEE.

关键词： Smart contract

Augmented sparsifiers for generalized hypergraph cuts with applications to decomposable submodular function minimization

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arXiv 2020年

作者： Benson, Austin R. Kleinberg, Jon Veldt, Nate Computer Science Dept Cornell University United States Center for Applied Math Cornell University United States

In recent years, hypergraph generalizations of many graph cut problems and algorithms have been introduced and analyzed as a way to better explore and understand complex systems and datasets characterized by multiway relationships. The standard cut function for a hypergraph H = (V, E) assigns the same penalty to a cut hyperedge, regardless of how its nodes are separated by a partition of V . Recent work in theoretical computer science and machine learning has made use of a generalized hypergraph cut function that can be defined by associating each hyperedge e ∈ E with a splitting function we, which assigns a (possibly different) penalty to each way of separating the nodes of e. When each we is a submodular cardinality-based splitting function, meaning that we(S) = g(|S|) for some concave function g, previous work has shown that a generalized hypergraph cut problem can be reduced to a directed graph cut problem on an augmented node set. However, existing reduction procedures introduce up to O(|e|2) edges for a hyperedge e. This often results in a dense graph, even when the hypergraph is sparse, which leads to slow runtimes (in theory and practice) for algorithms that run on the reduced graph. We introduce a new framework of sparsifying hypergraph-to-graph reductions, where a hypergraph cut defined by submodular cardinality-based splitting functions is (1+Ε)-approximated by a cut on a directed graph. Our techniques are based on approximating concave functions using piecewise linear curves, and we show that they are optimal within an existing strategy for hypergraph reduction. We provide bounds on the number of edges needed to model different types of splitting functions. For Ε > 0, in the worst case, we need O(Ε−1|e|log |e|) edges to reduce any hyperedge e, which leads to faster runtimes for approximately solving generalized hypergraph s-t cut problems. For the common machine learning heuristic of a clique splitting function on a node set e, our approach requires only

关键词： Directed graphs

Non-iterative blind calibration of nested arrays with asymptotically optimal weighting

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arXiv 2020年

作者： Weiss, Amir Yeredor, Arie Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Israel School of Electrical Engineering Tel-Aviv University Israel

Blind calibration of sensors arrays (without using calibration signals) is an important, yet challenging problem in array processing. While many methods have been proposed for "classical" array structures, such as uniform linear arrays, not as many are found in the context of the more "modern" sparse arrays. In this paper, we present a novel blind calibration method for 2-level nested arrays. Specifically, and despite recent contradicting claims in the literature, we show that the Least-Squares (LS) approach can in fact be used for this purpose with such arrays. Moreover, the LS approach gives rise to optimally-weighted LS joint estimation of the sensors’ gains and phases offsets, which leads to more accurate calibration, and in turn, to higher accuracy in subsequent estimation tasks (e.g., direction-of-arrival). Our method, which can be extended to K-level arrays (K > 2), is superior to the current state of the art both in terms of accuracy and computational efficiency, as we demonstrate in simulation. Copyright © 2020, The Authors. All rights reserved.

关键词： Calibration

Empowering Hepatitis Diagnosis Using RFE Feature Selection

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Empowering Hepatitis Diagnosis Using RFE Feature Selection

International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Ravi Kumar Sachdeva Kritya Deo Singh Priyanka Bathla Anurag Jain Tanupriya Choudhury Ketan Kotecha Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh University Gharuan Mohali Punjab India School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand India CSE Dept. Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Maharashtra India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India India

Hepatitis, among prevalent diseases in today's world, affects the health of more than 1.5 million individuals annually worldwide. However, the accuracy of patient diagnosis during the early stages of symptom manifestation is insufficient for effective resolutions. Fortunately, the researchers behind this study have made significant progress by developing an advanced machine-learning model that surpasses its predecessors in accuracy. To conduct their experiment, they utilized the hepatitis dataset available to the public through the University of California Irvine (UCI) machine learning repository. This dataset comprises 155 instances and serves as a valuable resource for their analysis. The authors employed various classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Adaboost, and Decision Tree (DT). Remarkably, SVM achieved an impressive accuracy rate of 94.78%, outperforming all other classifiers when combined with Recursive Feature Elimination (RFE).

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Coverage analysis of net inscriptions in coloured petri net models

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arXiv 2020年

作者： Ahishakiye, Faustin Jarabo, José Ignacio Requeno Kristensen, Lars Michael Stolz, Volker Dept. of Computer Science Electrical Engineering and Mathematical Sciences Faculty of Engineering and Natural Science Western Norway University of Applied Sciences

High-level Petri net such as Coloured Petri Nets (CPNs) are characterised by the combination of Petri nets and a high-level programming language. In the context of CPNs and CPN Tools, the inscriptions (e.g., arc expressions and guards) are specified using Standard ML (SML). The application of simulation and state space exploration (SSE) for validating CPN models traditionally focusses on behavioural properties related to net structure, i.e., places and transitions. This means that the net inscriptions are only implicitly validated, and the extent to which these have been covered is not made explicit. The contribution of this paper is an approach that establishes a link between coverage analysis known from programming languages and net inscriptions of CPN models. Specifically, we consider Modified Condition/Decision Coverage (MC/DC) which generalises branch coverage of SML decisions. We have implemented our approach in a library for CPN Tools comprised of an annotation and instrumentation mechanism that transparently intercept and collect evaluations of boolean conditions, and a post-processing tool that determines whether each decision is MC/DC-covered by a set of models executions (runs). We evaluate our approach on four larger public-available CPN models. Copyright © 2020, The Authors. All rights reserved.

关键词： Petri nets

Non-homotopic loops with a bounded number of pairwise intersections

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arXiv 2021年

作者： Blažej, Václav Opler, Michal Šileikis, Matas Valtr, Pavel Faculty of Information Technology Czech Technical University in Prague Prague Czech Republic Computer Science Institute Charles University Prague Czech Republic The Czech Academy of Sciences Institute of Computer Science Prague Czech Republic Dept. of Applied Mathematics Faculty of Mathematics and Physics Charles University Prague Czech Republic

Let Vn be a set of n points in the plane and let x ∈/ Vn. An x-loop is a continuous closed curve not containing any point of Vn. We say that two x-loops are non-homotopic if they cannot be transformed continuously into each other without passing through a point of Vn. For n = 2, we give an upper bound eO(√k) on the maximum size of a family of pairwise non-homotopic x-loops such that every loop has fewer than k self-intersections and any two loops have fewer than k intersections. The exponent O(√k) is asymptotically tight. The previous upper bound bound 2(2k)4 was proved by Pach, Tardos, and Tóth [Graph Drawing 2020]. We prove the above result by proving the asymptotic upper bound eO(√k) for a similar problem when x ∈ Vn, and by proving a close relation between the two *** Codes 05C62, 05C10 Copyright © 2021, The Authors. All rights reserved.

关键词： Drawing (graphics)

Reliability assurance in early-life-failure test through improved nearest neighbor regression

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IEICE Electronics Express 2020年第2期17卷

作者： Song, Tai Liang, Huaguo Huang, Zhengfeng Hou, Jie Dept. of Electronic Science and Applied Physics Hefei University of Technology Hefei230009 China Institute of Computer Architecture and Computer Stuttgart University StuttgartD-70569 Germany

During manufacturing test, researchers usually overlook the importance of process variation defects and marginal defects, which can seriously affect test results of Early-Life-Failure (ELF). Theoretically, machine learning classification methods can be used to identify these two defects. In fact, when features overfitting or data distribution overlap seriously, classifiers perform poorly, it will not achieve the desired results. This paper first-ever proposes a kind of data preprocessing method combines improved K-Nearest Neighbors (KNN) regression classifier, so that the classification results will be enhanced in terms of classification performance. Experiment results demonstrate that the predictive accuracy is 45% higher than the traditional logistic regression method. This proposed method will drive critical new requirements for fault modeling, test generation and test application, and implementing them effectively will require a new level of collaboration between process and product developers. © 2020 Institute of Electronics Information Communication Engineers. All rights reserved.

关键词： Defects

Modelling and Mining Brain Network Data for Diagnosis of Neurodegenerative Diseases

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Modelling and Mining Brain Network Data for Diagnosis of Neu...

IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： B. A. Madhushree Nandyala D. Gangadhar K. S. Prafulla Kumari Dept. of Computer Science and Engineering M. S. Ramaiah University of Applied Sciences Bengaluru India

ISBN: (数字)9781728168289

ISBN: (纸本)9781728168296

Connectomes are brain networks represented as a graph with the vertices being the regions of the brain and weighted edges representing strength of connections between the regions inferred from brain imaging techniques such a Functional MRI (fMRI). An intense research activity is to use the connectomes to identify markers for brain disorders, especially neuro-degenerative diseases such as Autism Spectrum Disorder (ASD) by studying the differences in the connectomes of healthy subjects and patients. This paper presents a novel data model for the connectome data and analyzes its efficacy in the classification of ASD and Typically Developing (TD) (healthy) connectomes. The proposed data modelling begins by clustering the vertices (brain regions) using the Graph Spectral Clustering into fixed number of clusters, the number of clusters chosen as four based on the empirical evidence. The resulting clustering is used to map the vertices into a binary matrix which is then converted into a binary row vector to form a vector space model of the connectome data that is employed to classify the connectome data. The developed model is first validated using Human Connectome Protocol (HCP) FMRI derived connectome data of 812 healthy patents. Binary data models of the UCLA Autism dataset with fMRI and DTI scans of 42 ASD and 37 TD subjects are generated and employed for their classification. Different classification algorithms are trained, tested and their performance evaluated using the resulting dataset. Cross Validation (CV) estimates identified the best performance (83% recall and 83% precision) for DTI data and (73% recall and 89% precision) for the fMRI data achieved using Logistic Regression.

关键词： Data models Diffusion tensor imaging Functional magnetic resonance imaging Autism Brain modeling Logistics