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SEADer++: social engineering attack detection in online environments using machine learning

Journal of Information and Telecommunication 2020年第3期4卷 346-362页

作者： Lansley, Merton Mouton, Francois Kapetanakis, Stelios Polatidis, Nikolaos Department of Informatics University of Sussex Brighton UK Department of Applied Science Noroff University College Oslo Norway Department of Computer Science University of the Western Cape Bellville South Africa Department of Computer Science University of the Western Cape Bellville South Africa School of Computing Engineering and Mathematics University of Brighton Brighton UK

ABSTRACTFormulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to *** engineering attacks are one of the most well-known and easiest to apply attacks in the cybersecurity domain. Research has shown that the majority of attacks against computer systems was based on the use of social engineering methods. Considering the importance of emerging fields such as machine learning and cybersecurity we have developed a method that detects social engineering attacks that is based on natural language processing and artificial neural networks. This method can be applied in offline texts or online environments and flag a conversation as a social engineering attack or not. Initially, the conversation text is parsed and checked for grammatical errors using natural language processing techniques and then an artificial neural network is used to classify possible attacks. The proposed method has been evaluated using a real dataset and a semi-synthetic dataset with very high accuracy results. Furthermore, alternative classification methods have been used for comparisons in both datasets.

关键词： Social engineering online chat environments natural language processing machine learning neural networks ensemble learning

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Identifying reusable services from collaborative activities using activity theory (AT): The activity-based service identification framework (ASIF)

TechRxiv

引用

TechRxiv 2021年

作者： Ayalew, Sertse Abebe Twinomurinzi, Hossana Sudan University of Science and Technology Khartoum Sudan Bahir Dar University Bahir Dar Institute of Technology Computing Faculty Bahir Dar Ethiopia Department of Applied Information System School of Computer Intelligence and Information Systems University of Johannesburg South Africa

Service-Orientation (SO) is a paradigm for developing collaboration platforms that promote services as reusable components. Service identification (SI) occurs earlier to identify patterns of a business process (BP) or model that can become software services. Although there exist several SI strategies, most are devised within the context of organizations with mature business processes and formalized structures. There is therefore a need for SI strategies that cater for the emerging heterogeneous and collaborative environments such as exists for small and micro enterprises (SMEs). This paper develops the Activity-based SI Framework (ASIF) from Activity-Theory using design science research (DSR) methods. Six collaboration attributes were elicited for SME;domain-oriented collaboration activity, actions of stakeholders towards the collaboration objective, heterogeneous stakeholders in a problem domain, autonomous actions of a participant, norms(rules) and structures(DOL), and interactive processes of stakeholders during collaboration. The framework is demonstrated using peer-to-peer collaborative lending activities of SMEs in Ethiopia called ‘EQUIB’. The findings show that the framework is promising in accurately guiding the identification of reusable and composable services in SME heterogeneous and collaborative contexts. © 2021, CC BY.

关键词： Process design

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Terrain prickliness: theoretical grounds for high complexity viewsheds

arXiv

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arXiv 2021年

作者： Acharyya, Ankush Löffler, Maarten Meijer, Gert G.T. Saumell, Maria Silveira, Rodrigo I. Staals, Frank Department of Computer Science and Engineering National Institute of Technology Durgapur India Dept. of Information and Computing Sciences Utrecht University Netherlands Department of Computer Science Tulane University United States Academy of ICT and Creative Technologies NHL Stenden University of Applied Sciences Netherlands Dept. of Theoretical Computer Science Faculty of Information Technology Czech Technical University in Prague Czech Republic Dept. de Matemàtiques Universitat Politècnica de Catalunya Spain

MSC Codes 68U05An important task in terrain analysis is computing viewsheds. A viewshed is the union of all the parts of the terrain that are visible from a given viewpoint or set of viewpoints. The complexity of a viewshed can vary significantly depending on the terrain topography and the viewpoint position. In this work we study a new topographic attribute, the prickliness, that measures the number of local maxima in a terrain from all possible angles of view. We show that the prickliness effectively captures the potential of 2.5D TIN terrains to have high complexity viewsheds. We present optimal (for 1.5D terrains) and near-optimal (for 2.5D terrains) algorithms to compute it for TIN terrains, and efficient approximate algorithms for raster DEMs. We validate the usefulness of the prickliness attribute with experiments in a large set of real terrains. Copyright © 2021, The Authors. All rights reserved.

关键词： Topography

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Fast Estimation of Outcome Probabilities for Quantum Circuits

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PRX Quantum 2022年第2期3卷 020361-020361页

作者： Hakop Pashayan Oliver Reardon-Smith Kamil Korzekwa Stephen D. Bartlett Institute for Quantum Computing and Department of Combinatorics and Optimization University of Waterloo Ontario N2L 3G1 Canada Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków 30-348 Poland Centre for Engineered Quantum Systems School of Physics The University of Sydney Sydney New South Wales 2006 Australia

We present two classical algorithms for the simulation of universal quantum circuits on n qubits constructed from c instances of Clifford gates and t arbitrary-angle Z-rotation gates such as T gates. Our algorithms complement each other by performing best in different parameter regimes. The Estimate algorithm produces an additive precision estimate of the Born-rule probability of a chosen measurement outcome with the only source of run-time inefficiency being a linear dependence on the stabilizer extent (with scaling approximately equal to 1.17t for T gates). Our algorithm is state of the art for this task: as an example, in approximately 13 h (on a standard desktop computer), we estimate the Born-rule probability to within an additive error of 0.03, for a 50-qubit, 60 non-Clifford gate quantum circuit with more than 2000 Clifford gates. Our second algorithm, Compute, calculates the probability of a chosen measurement outcome to machine precision with run time O(2t−rt), where r is an efficiently computable, circuit-specific quantity. With high probability, r is very close to min{t,n−w} for random circuits with many Clifford gates, where w is the number of measured qubits. Compute can be effective in surprisingly challenging parameter regimes, e.g., we can randomly sample Clifford+T circuits with n=55, w=5, c=105, and t=80T gates, and then compute the Born-rule probability with a run time consistently less than 10 min using a single core of a standard desktop computer. We provide a C+Python implementation of our algorithms and benchmark them using random circuits, the hidden-shift algorithm, and the quantum approximate optimization algorithm.

关键词： Quantum simulation

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A Counselors-Based Intrusion Detection Architecture 9

A Counselors-Based Intrusion Detection Architecture

引用

9th Latin American Network Operations and Management Symposium, LANOMS 2019

作者： Quincozes, Silvio E. Raniery, Carlos Nunes, Raul Ceretta Albuquerque, Célio Passos, Diego Mosse, Daniel Computer Science Department Universidade Federal Fluminense Niterói RJ Brazil Department of Applied Computing Universidade Federal de Santa Maria Santa Maria RS Brazil Department of Applied Computing University of Pittsburgh PittsburghPA United States

ISBN: (纸本)9783903176232

Intrusion Detection Systems (IDSs) are a fundamental component of defensive solutions. In particular, signature-based IDSs aim to detect malicious activities on computer systems and networks by relying on data classification models built from a training dataset. However, classifiers performance can vary for each attack pattern. A common technique to overcome this issue is to use ensemble methods, where multiple classifiers are employed and a final decision is taken combining their outputs. Despite the potential advantages of such an approach, its usefulness is limited in scenarios where (i) multiple expert classifiers present divergent results or (ii) representative data are missing to detect a specific attack class. In this work, we introduce the concept of counselor networks to deal with conflicts from different classifiers by exploiting the collaboration between IDSs that analyze multiple and heterogeneous data sources. Our empirical results demonstrate the feasibility of the proposed architecture in improving the accuracy of the intrusion detection process. © 2019 IFIP.

关键词： computer crime

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Kurdish Handwritten Character Recognition using Deep Learning Techniques

arXiv

引用

arXiv 2022年

作者： Ahmed, Rebin M. Rashid, Tarik A. Fattah, Polla Alsadoon, Abeer Bacanin, Nebojsa Mirjalili, Seyedali Vimal, S. Chhabra, Amit IT Department Faculty of Applied Science Tishk International University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems. © 2022, CC BY.

关键词： Convolutional neural networks

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Sparse-grid Discontinuous Galerkin Methods for the Vlasov–Poisson–Lenard–Bernstein Model

arXiv

引用

arXiv 2024年

作者： Schnake, Stefan Kendrick, Coleman Endeve, Eirik Stoyanov, Miroslav Hahn, Steven Hauck, Cory D. Green, David L. Snyder, Phil Canik, John Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Fusion Energy Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Department of Mathematics University of Tennessee KnoxvilleTN37996 United States Type One Energy Group Oak RidgeTN37830 United States Commonwealth Scientific and Industrial Research Organisation Mayfield WestNSW2304 Australia

Sparse-grid methods have recently gained interest in reducing the computational cost of solving high-dimensional kinetic equations. In this paper, we construct adaptive and hybrid sparse-grid methods for the Vlasov–Poisson–Lenard–Bernstein (VPLB) model. This model has applications to plasma physics and is simulated in two reduced geometries: a 0x3v space homogeneous geometry and a 1x3v slab geometry. We use the discontinuous Galerkin (DG) method as a base discretization due to its high-order accuracy and ability to preserve important structural properties of partial differential equations. We utilize a multiwavelet basis expansion to determine the sparse-grid basis and the adaptive mesh criteria. We analyze the proposed sparse-grid methods on a suite of three test problems by computing the savings afforded by sparse-grids in comparison to standard solutions of the DG method. The results are obtained using the adaptive sparse-grid discretization library ASGarD. Copyright © 2024, The Authors. All rights reserved.

关键词： Integral equations

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A computational framework for Carreau nanomaterial induced by stretchy regime considering Brownian diffusion, viscous dissipation, ferromagnetism and thermophoresis

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Partial Differential Equations in applied Mathematics 2025年 13卷

作者： Hussain, Iftikhar Khan, Waqar Azeem Tabrez, Muhammad Waqas, M. Ibrahim, Talib K. Saidani, Taoufik Kausar, M.S. Zamri, Nurnadiah Abdullaeva, Barno Garalleh, Hakim AL Department of Mathematics Mohi-ud-Din Islamic University Nerian Sharif Jammu and Kashmir Azad 12010 Pakistan School of Mathematics and Statistics Beijing Institute of Technology Beijing 100081 China NUTECH School of Applied Sciences and Humanities National University of Technology Islamabad 44000 Pakistan Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut 22200 Malaysia Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Petroleum Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Petroleum Engineering Al-Kitab University Altun Kupri Iraq Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin (Kampus Gong Badak) Terengganu Kuala Terengganu 21300 Malaysia Department of Mathematics and Information Technologies Vice-Rector for Scientific Affairs Tashkent State Pedagogical University Tashkent Uzbekistan Department of Mathematical Science College of Engineering University of Business and Technology Jeddah 21361 Saudi Arabia

In recent decade, the research in nanotechnology brought the idea of nanofluids which are highly conducting materials. Some important applications of nanofluids are justified in pharmaceutical industry, coolants in vehicles engine, preparation process of various medicines for treatment of fatal diseases like cancer, aircraft industry, latest heat exchangers etc. The ferro particles are special class of nanomaterial which attract variety of interest from the researchers. The objective of current research is to perform the investigation for heat and mass transfer subject to Carreau nanofluid to observe the enhancement of heat transmission phenomena. The induction of magnetic dipole has been observed for thermal transport phenomenon. The flow is causing by stretched surface. A simplified system is obtained by utilizing the dimensionless variables. Physical interpretation of results have been presented. Result showed that temperature profile in presence of ferrofluid fluid boosts up with escalation in thermophoresis parameter, while opposite behavior is noted for power law index. The concentration of ferrofluid showing declining behavior for Schmidt numbers and rises for thermophoresis parameter. © 2024

关键词： Carreau ferrofluid Heat and mass transfer Magnetic dipole Numerical solution

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Automatic Detection of Breast Cancer in Thermal Images using Optimal Handcrafted Features

Automatic Detection of Breast Cancer in Thermal Images using...

引用

International Conference on computer Theory and Applications (ICCTA)

作者： Seifedine Kadry Venkatesan Rajinikanth Susmitha Dasari T. Suvathi Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai India Department of Computer Science and Engineering St. Joseph's College of Engineering OMR Chennai India

ISBN: (数字)9781665478540

ISBN: (纸本)9781665478557

Breast Cancer (BC) is one of the harsh illnesses which widely distress the women population and premature recognition is always preferred to reduce its harshness. BC is normally detected in hospitals using a chosen non-invasive medical imaging technique. The proposed work aims to develop and execute the machine learning supported automatic detection of the BC using breast Thermal Images (TI). In TI, examination of thermal silhouette helps to get the necessary information about the normal/cancer infected section. The developed system consist the following phases; (i) Image collection and pre-processing, (ii) Image enhancement and handcrafted feature extraction, (iii) Feature selection with Bat Algorithm, and (iv) Binary classification and validation. In this work, a 5-fold cross validation is employed to classify the test images and the detected accuracy of Fine KNN is better (>92%) compared to the alternatives. The proposed work is tested using benchmark breast TI and the outcome of this research confirms its clinical significance.

关键词： Hospitals Sociology Fitting Machine learning Feature extraction Breast cancer Classification algorithms

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Oxidation Dynamics of Nanophase Aluminum Clusters: A Molecular Dynamics Study

引用

MRS Online Proceedings Library 2020年第1期481卷 625-630页

作者： Ogata, Shuji Campbell, Timothy J. Tsuruta, Kenji Nakano, Aiichiro Kalia, Rajiv K. Vashishta, Priya Loong, Chun-K. Department of Applied Sciences Yamaguchi University Ube Japan Concurrent Computing Laboratory for Materials Simulations Department of Physics and Astronomy Department of Computer Science Louisiana State University Baton Rouge Argonne National Laboratory Argonne

Oxidation of an aluminum nanocluster (252,158 atoms) of radius 100Å placed in gaseous oxygen (530,727 atoms) is investigated by performing molecular-dynamics simulations on parallel computers. The simulation takes into account the effect of charge transfer between Al and 0 based on the electronegativity equalization principles. We find that the oxidation starts at the surface of the cluster and the oxide layer grows to a thickness of ∼28Å. Evolutions of local temperature and densities of Al and 0 are investigated. The surface oxide melts because of the high temperature resulting from the release of energy associated with Al-O bondings. Amorphous surface-oxides are obtained by quenching the cluster. Vibrational density-of-states for the surface oxide is analyzed through comparisons with those for crystalline Al, Al nanocluster, and α-Al2O3

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