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Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules

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Cybersecurity Threats in the Era of AI: Detection of Phishin...

Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Mohammad Aljaidi Ayoub Alsarhan Dimah Al-Fraihat Ahmed Al-Arjan Bashar Igried Subhieh M. El-Salhi Muhammad Khalid Qais Al-Na'amneh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Information Technology The Hashemite University Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince AL-Hussein bin Abdullah II of Information Technology The Hashemite University Jordan Department of Computer Science and Applications The Hashemite University Zarqa Jordan The Hashemite University Zarqa Jordan Department of Computer Sciences and Technology University of Hull Kingston upon Hull UK Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing.

关键词： Adaptation models Accuracy Phishing Identity theft Refining Machine learning Passwords

Knowledge Cores in large formal contexts

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arXiv 2020年

作者： Hanika, Tom Hirth, Johannes Knowledge and Data Engineering Group Dep. of Electrical Engineering and Computer Science University of Kassel

Knowledge computation tasks are often infeasible for large data sets. This is in particular true when deriving knowledge bases in formal concept analysis (FCA). Hence, it is essential to come up with techniques to cope with this problem. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and interesting patterns. An essentially different approach is used in network science, called k-cores. These are able to reflect rare patterns if they are well connected in the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence to bi-partite graphs. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts data *** Codes 68T30 03G10 97R40 Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge based systems

Robust machine learning in critical care - Software engineering and medical perspectives

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arXiv 2021年

作者： Staron, Miroslaw Hergès, Helena Odenstedt Naredi, Silvana Block, Linda El-Merhi, Ali Vithal, Richard Elam, Mikael Computer Science and Engineering Chalmers University of Gothenburg Gothenburg Sweden Dep. of Anesthesiology and Intensive Care Sahlgrenska Academy Region Västra Götaland Sahlgrenska University Hospital Dep. of Anesthesia and Intensive Care Gothenburg Sweden Institute of Neurosc. and Physiology Dep. of Clinical Neuroscience Sahlgrenska Academy Dep. of Clinical Neurophysiology Sahlgrenska Univ. Hospital MedTech West Sahlgrenska Univ. Hospital Gothenburg Sweden

Using machine learning in clinical practice poses hard requirements on explainability, reliability, replicability and robustness of these systems. Therefore, developing reliable software for monitoring critically ill patients requires close collaboration between physicians and software engineers. However, these two different disciplines need to find own research perspectives in order to contribute to both the medical and the software engineering domain. In this paper, we address the problem of how to establish a collaboration where software engineering and medicine meets to design robust machine learning systems to be used in patient care. We describe how we designed software systems for monitoring patients under carotid endarterectomy, in particular focusing on the process of knowledge building in the research team. Our results show what to consider when setting up such a collaboration, how it develops over time and what kind of systems can be constructed based on it. We conclude that the main challenge is to find a good research team, where different competences are committed to a common goal. Copyright © 2021, The Authors. All rights reserved.

关键词： Software design

Object Detection During Newborn Resuscitation Activities

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arXiv 2023年

作者： Meinich-Bache, Øyvind Engan, Kjersti Austvoll, Ivar Eftestøl, Trygve Myklebust, Helge Yarrot, Ladislaus Blacy Kidanto, Hussein Ersdal, Hege The Dep. of Electrical Engineering and Computer Science University of Stavanger Norway Laerdal Medical Norway The Research Institute Haydom Lutheran Hospital Manyara Tanzania United Republic of The School of Medicine Aga Khan University Dar es Salaam Tanzania United Republic of The Faculty of Health Sciences University of Stavanger Norway The Department of Anesthesiology and Intensive Care Stavanger University Hospital Norway

Objective: Birth asphyxia is a major newborn mortality problem in low-resource countries. International guideline provides treatment recommendations;however, the importance and effect of the different treatments are not fully explored. The available data is collected in Tanzania, during newborn resuscitation, for analysis of the resuscitation activities and the response of the newborn. An important step in the analysis is to create activity timelines of the episodes, where activities include ventilation, suction, stimulation etc. Methods: The available recordings are noisy real-world videos with large variations. We propose a two-step process in order to detect activities possibly overlapping in time. The first step is to detect and track the relevant objects, like bag-mask resuscitator, heart rate sensors etc., and the second step is to use this information to recognize the resuscitation activities. The topic of this paper is the first step, and the object detection and tracking are based on convolutional neural networks followed by post processing. Results: The performance of the object detection during activities were 96.97 % (ventilations), 100 % (attaching/removing heart rate sensor) and 75 % (suction) on a test set of 20 videos. The system also estimate the number of health care providers present with a performance of 71.16 %. Conclusion: The proposed object detection and tracking system provides promising results in noisy newborn resuscitation videos. Significance: This is the first step in a thorough analysis of newborn resuscitation episodes, which could provide important insight about the importance and effect of different newborn resuscitation activities. © 2023, CC BY.

关键词： Object detection

Activity Recognition from Newborn Resuscitation Videos

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arXiv 2023年

作者： Meinich-Bache, Øyvind Austnes, Simon Lennart Engan, Kjersti Austvoll, Ivar Eftestøl, Trygve Myklebust, Helge Kusulla, Simeon Kidanto, Hussein Ersdal, Hege The Dep. of Electrical Engineering and Computer Science University of Stavanger Norway Laerdal Medical Norway The Research Institute Haydom Lutheran Hospital Manyara Tanzania United Republic of The School of Medicine Aga Khan University Dar es Salaam Tanzania United Republic of The Faculty of Health Sciences University of Stavanger Norway The Department of Anesthesiology and Intensive Care Stavanger University Hospital Norway

Objective: Birth asphyxia is one of the leading causes of neonatal deaths. A key for survival is performing immediate and continuous quality newborn resuscitation. A dataset of recorded signals during newborn resuscitation, including videos, has been collected in Haydom, Tanzania, and the aim is to analyze the treatment and its effect on the newborn outcome. An important step is to generate timelines of relevant resuscitation activities, including ventilation, stimulation, suction, etc., during the resuscitation episodes. Methods: We propose a two-step deep neural network system, ORAA-net, utilizing low-quality video recordings of resuscitation episodes to do activity recognition during newborn resuscitation. The first step is to detect and track relevant objects using Convolutional Neural Networks (CNN) and post-processing, and the second step is to analyze the proposed activity regions from step 1 to do activity recognition using 3D CNNs. Results: The system recognized the activities newborn uncovered, stimulation, ventilation and suction with a mean precision of 77.67 %, a mean recall of 77,64 %, and a mean accuracy of 92.40 %. Moreover, the accuracy of the estimated number of Health Care Providers (HCPs) present during the resuscitation episodes was 68.32 %. Conclusion: The results indicate that the proposed CNN-based two-step ORAA-net could be used for object detection and activity recognition in noisy low-quality newborn resuscitation videos. Significance: A thorough analysis of the effect the different resuscitation activities have on the newborn outcome could potentially allow us to optimize treatment guidelines, training, debriefing, and local quality improvement in newborn resuscitation. © 2023, CC BY.

关键词： Object detection

Extremum Seeking Control for Beam Steering using Hypersurfaces

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Extremum Seeking Control for Beam Steering using Hypersurfac...

IEEE International Conference on Communications Workshops, ICC

作者： Nouman Ashraf Marios Lestas Taqwa Saeed Hamidreza Taghvaee Sergi Abadal Andreas Pitsillides Christos Liaskos Dep. of Electrical Engineering Frederick University Dep. of Computer Science University of Cyprus Nicosia Cyprus Dep. of Computer Architecture Universitat Politècnica de Catalunya Barcelona Spain Institute of Computer Science Foundation of Research and Technology Hellas Heraklion Greece

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

Hypersurface, a software defined metasurface (SDM) paradigm constitutes a revolutionary technology aiming at offering programmatic control over all aspects of a propagating wave, altering its direction, power, polarization and phase. In the absence of line of sight (LOS) between a transmitter and a receiver, HSF can provide seamless connectivity via programmatic reflection. However, such an application requires fine tuning of the metasurface reconfiguration parameters, which may not be effective when done in an open loop fashion due to model uncertainties. In this work, we consider a feedback-based formulation of the problem, which involves maximization of the received power and propose the use of Extremum Seeking Control (ESC) for the controller implementation due to fact that it is model-free and adheres to the maximization problem. Extensive simulations indicate that the proposed scheme is successful in guiding the impinging wave to the receiver within reasonable time even in the presence of time varying delays incurred by message propagation. In addition, a discrete time implementation is considered, investigating its limitations as we increase the sampling time while also characterizing the traffic within the controller network.

关键词： Receivers Beam steering Transmitters Aerospace electronics computer science Software Delays

An Overview on Big Data Mining Using Evolutionary Techniques

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An Overview on Big Data Mining Using Evolutionary Techniques

2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies, 3ICT 2020

作者： Alaeddin, Fadia Khalifeh, Ala' Darabkh, Khalid A. Princess Sumaya University for Technology Luminus Technical University College Computer Science Department Amman Jordan German Jordanian University Dep. of Electrical Eng. Amman Jordan University of Jordan Dept. of Computer Engineering Amman Jordan

ISBN: (纸本)9781728196732

Big Data processing suffers from several limitations due to its magnitude making it time consuming to implement any kind of analysis such as data mining. Evolutionary Algorithms (EAs) are metaheuristic optimization algorithms inspired by the natural behavior of the population evolution such as Genetic Algorithm, Artificial Bee Colony, Artificial Ant Colony and other swarm intelligence algorithms. EAs have recently been used to overcome Big Data limitations, especially memory consumption and the long execution time. This paper provides an overview of the recent research papers that utilize evolutionary algorithms to deal with the optimization problems related to Big Data mining such as clustering, classification and features' selection. © 2020 IEEE.

关键词： K-means clustering

High-speed AWG exploiting parallel time interleaved DAC cores

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High-speed AWG exploiting parallel time interleaved DAC core...

IEEE Instrumentation and Measurement Technology Conference

作者： M. D’Arco L. Angrisani P. Monsurrò A. Trifiletti Dep. of Computer Science and Electrical Engineering University of Naples Federico II Naples ITALY Dep. of Electronic Engineering University of Rome Sapienza Rome ITALY

ISBN: (数字)9781728144603

ISBN: (纸本)9781728144610

An arbitrary waveform generator architecture that exploits multiple DAC cores to increase the sample rate with respect to AWG architectures based on an individual DAC is proposed. The processing operations necessary to distribute the waveform samples between the DAC cores are illustrated. The preliminary results highlighting the performance expected from the proposed architecture are shown.

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Controller Chip Design of Serial-Parallel-Switched-Capacitor Coupled-Inductor Boost DC-AC Inverter

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Controller Chip Design of Serial-Parallel-Switched-Capacitor...

IEEE International Conference on Power and Energy Applications (ICPEA)

作者： Yuen-Haw Chang Zheng-Bin Li Dep. of Computer Science and Information Engineering Chaoyang University of Technology Wufong Taichung Taiwan

ISBN: (数字)9781728190297

ISBN: (纸本)9781728190303

This work is focused on the controller chip design/ implementation of serial-parallel-switched-capacitor coupledinductor (SPSCCI) inverter applied to high-gain boost DC-AC conversion. The controlled plant is its power part made up of SPSCCI booster and DC-link inverter to perform a practical DC-AC conversion from 12V to 110VRMS, 60Hz. Here, for achieving this goal, the controller is its control part constructed by sinusoidal pulse-width-modulation (SPWM) compensator plus phase generator, and then is implemented under Cadence full-custom design through pre-layout simulation (PRE-SIM) and post-layout simulation (POST-SIM) on a fabricated chip of integrated circuit: no. D35-105B-E0008, TSMC 0.35μm 2P4M, 1500×1500μm 2 , 5.0V, 2.68mW, max frequency 200kHz. Finally, with the help of this controller chip, a closed-loop prototype circuit of this SPSCCI can be realized and verified experimentally for some test cases of DC-AC to validate the feasibility of this inverter.

关键词： Inverters Layout Integrated circuit modeling Inductors Switches Chip scale packaging Generators