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Anomaly-Based Intrusion DetectionModel Using Deep Learning for IoT Networks

computer Modeling in engineering & Sciences 2024年第10期141卷 823-845页

作者： Muaadh A.Alsoufi Maheyzah Md Siraj Fuad A.Ghaleb Muna Al-Razgan Mahfoudh Saeed Al-Asaly Taha Alfakih Faisal Saeed Faculty of Computing Universiti Teknologi Malaysia(UTM)Johor Bahru81310Malaysia DAAI Research Group College of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11345Saudi Arabia

The rapid growth of Internet of Things(IoT)devices has brought numerous benefits to the interconnected ***,the ubiquitous nature of IoT networks exposes them to various security threats,including anomaly intrusion *** addition,IoT devices generate a high volume of unstructured *** intrusion detection systems often struggle to cope with the unique characteristics of IoT networks,such as resource constraints and heterogeneous data *** the unpredictable nature of network technologies and diverse intrusion methods,conventional machine-learning approaches seem to lack *** numerous research domains,deep learning techniques have demonstrated their capability to precisely detect *** study designs and enhances a novel anomaly-based intrusion detection system(AIDS)for IoT ***,a Sparse Autoencoder(SAE)is applied to reduce the high dimension and get a significant data representation by calculating the reconstructed ***,the Convolutional Neural Network(CNN)technique is employed to create a binary classification *** proposed SAE-CNN approach is validated using the Bot-IoT *** proposed models exceed the performance of the existing deep learning approach in the literature with an accuracy of 99.9%,precision of 99.9%,recall of 100%,F1 of 99.9%,False Positive Rate(FPR)of 0.0003,and True Positive Rate(TPR)of *** addition,alternative metrics,such as training and testing durations,indicated that SAE-CNN performs better.

关键词： IoT anomaly intrusion detection deep learning sparse autoencoder convolutional neural network

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An Intelligent Prediction Model for Target Protein Identification in Hepatic Carcinoma Using Novel Graph Theory and ANN Model

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computer Modeling in engineering & Sciences 2022年第10期133卷 31-46页

作者： G.Naveen Sundar Stalin Selvaraj D.Narmadha K.Martin Sagayam A.Amir Anton Jone Ayman A.Aly Dac-Nhuong Le Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatore641114India Centre for Nanotechnology&Advanced Biomaterials(CeNTAB) School of Chemical&BiotechnologySASTRA Deemed to be UniversityThanjavur613401India Department of ECE Karunya Institute of Technology and SciencesCoimbatore641114India Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Faculty of Information Technology Duy Tan UniversityDanang550000Vietnam

Hepatocellular carcinoma(HCC)is one major cause of cancer-related mortality around the ***,at advanced stages of HCC,systematic treatment options are currently *** a result,new pharmacological targetsmust be discovered regularly,and then tailored medicines against HCC must be *** this research,we used biomarkers of HCC to collect the protein interaction network related to ***,DC(Degree Centrality)was employed to assess the importance of each *** an improved Graph Coloring algorithm was used to rank the target proteins according to the interaction with the primary target protein after assessing the top ranked proteins related to ***,physio-chemical proteins are used to evaluate the outcome of the top ranked *** proposed graph theory and machine learning techniques have been compared with six existing *** the proposed approach,16 proteins have been identified as potential therapeutic drug targets for Hepatic *** is observable that the proposed method gives remarkable performance than the existing centrality measures in terms of Accuracy,Precision,Recall,Sensitivity,Specificity and F-measure.

关键词： Drug target detection hepatic carcinoma degree centrality graph coloring artificial neural network model

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Simulated Evolution and Learning 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Xiaodong Li Michael Kirley Mengjie Zhang David Green Vic Ciesielski Hussein Abbass Zbigniew Michalewicz Tim Hendtlass Kalyanmoy Deb Kay Chen Tan Jürgen Branke Yuhui Shi

ISBN: (数字)9783540896944

ISBN: (纸本)9783540896937

This LNCS volume contains the papers presented at SEAL 2008, the 7th Int- nationalConference on Simulated Evolutionand Learning,held December 7–10, 2008, in Melbourne, Australia. SEAL is a prestigious international conference series in evolutionary computation and learning. This biennial event was ?rst held in Seoul, Korea, in 1996, and then in Canberra, Australia (1998), Nagoya, Japan (2000), Singapore (2002), Busan, Korea (2004), and Hefei, China (2006). SEAL 2008 received 140 paper submissions from more than 30 countries. After a rigorous peer-review process involving at least 3 reviews for each paper (i.e., over 420 reviews in total), the best 65 papers were selected to be presented at the conference and included in this volume, resulting in an acceptance rate of about 46%. The papers included in this volume cover a wide range of topics in simulated evolution and learning: from evolutionarylearning to evolutionary optimization, from hybrid systems to adaptive systems, from theoretical issues to real-world applications. They represent some of the latest and best research in simulated evolution and learning in the world.

关键词： Programming Techniques Computation by Abstract Devices Artificial Intelligence Simulation and Modeling User Interfaces and Human computer Interaction Discrete Mathematics in computer Science

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First Order Features Extraction from Thermal Images for Human State Recognition 8

First Order Features Extraction from Thermal Images for Huma...

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8th International Symposium on Electrical and Electronics engineering, ISEEE 2023

作者： Moldovanu, Simona Pavel, Sorin Aiordachioaie, Dorel Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Computer Science and Information Technology Galati Romania Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Electronics and Telecommunications Galati Romania

ISBN: (纸本)9798350301670

Due to the common limitation of the human visual system, internal features of thermal images cannot be fully discovered. To overcome these drawbacks, a lot of studies analyzed the facial expressions corroborating the features extracted from thermal images with machine learning tools. This paper proposed the first-order features extracted from the image histogram and a binary classification performed by the k-nearest neighbor algorithm (K-NN). The proposed method tests separately the performance of different input data, firstly, the features were extracted from raw images and the robustness of the K-NN algorithm led to an accuracy of 92.6%. Secondly, the features were extracted sequentially from corrupted images with noise. The novelty of the proposed method consists of verifying the quality classification when the images have bad quality, in this case, the accuracy decreased to 70.7%, as a result, the classification of facial expressions is sensitive to the quality of images. © 2023 IEEE.

关键词： Emotion Recognition

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Designing Optimal Parameters for Single-Axis Solar-Tracker through Analysis of Sun-Path and Tracking Vector Dynamics 6

Designing Optimal Parameters for Single-Axis Solar-Tracker t...

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6th International Conference on Electrical engineering and information and Communication technology, ICEEICT 2024

作者： Akhtaruzzaman, M. Islam, Md Shofiqul Shahryer, Fahim Arif, Muammer Din Mollik, Md. Sazib Rahman, Muhammad Towfiqur Rahman, Md. Mahbubur Hasan, Mohammad Kamrul Ahmed, Shugata Ashrafuzzaman, Md. Department of Computer Science and Engineering Dhaka1216 Bangladesh Department of Mechanical Engineering Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Faculty of Information Science and Technology Bangi Malaysia Department of Robotics and Mechatronics Engineering Bangladesh Sdn. Bhd. Cyberjaya Malaysia

ISBN: (纸本)9798350385779

The persistent challenges faced by sun-tracking systems include inefficient power production, wastage of energy, ineffective control, and high costs. Most of the existing systems are either static or dual-axis tracking which lead to inefficient output or increasing energy wastage and cost overheads. Though, single-axis tracking system has some benefits over the static or dual-axis tracking, most of the systems do not analyze the selection of optimal sun-path which again leads to inefficient and inadequate absorption of sunlight. Addressing the issues, this article presents a design of optimal parameters for single-axis solar-tracking by analyzing the sun-path and tracking vector dynamics. The study mainly focuses on analyzing solar-tracking dynamics, variations in sunray angles, Azimuth and Elevation dynamics throughout the day for the twelve months, Sun-vector calculation for local coordinates, joint rotational-axis, and joint motion control using a Proportional-Derivative controller. Additionally, the article outlines a conceptual architecture for automated control, monitoring, and power distribution for the potential development of a sustainable smart city. The proposed strategy addresses the issues of the optimal design of a single-axis solar-tracker, thus ensuring the reduction of cost overhead and energy wastage. © 2024 IEEE.

关键词： Dynamics

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N-Gram-Based Machine Learning Approach for Bot or Human Detection from Text Messages 22

N-Gram-Based Machine Learning Approach for Bot or Human Dete...

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6th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2022

作者： Kavadi, Durga Prasad Sanaboina, Chandra Sekhar Patan, Rizwan Gandomi, Amir Department Of Information Technology B V Raju Institute Of Technology Narsapur Telangana India Department Of Computer Science And Engineering University College Of Engineering Kakinada Jawaharlal Nehru Technological University Kakinada India Decentralized Science Lab College Of Computing And Software Engineering Kennesaw State University MariettaGA30060 United States Faculty Of Engineering And Information Systems University Of Technology Sydney UltimoNSW Australia

ISBN: (纸本)9781450396288

Social bots are computer programs created for automating general human activities like the generation of messages. The rise of bots in social network platforms has led to malicious activities such as content pollution like spammers or malware dissemination of misinformation. Most of the researchers focused on detecting bot accounts in social media platforms to avoid the damages done to the opinions of users. In this work, n-gram based approach is proposed for a bot or human detection. The content-based features of character n-grams and word n-grams are used. The character and word n-grams are successfully proved in various authorship analysis tasks to improve accuracy. A huge number of n-grams is identified after applying different pre-processing techniques. The high dimensionality of features is reduced by using a feature selection technique of the Relevant Discrimination Criterion. The text is represented as vectors by using a reduced set of features. Different term weight measures are used in the experiment to compute the weight of n-grams features in the document vector representation. Two classification algorithms, Support Vector Machine, and Random Forest are used to train the model using document vectors. The proposed approach was applied to the dataset provided in PAN 2019 competition bot detection task. The Random Forest classifier obtained the best accuracy of 0.9456 for bot/human detection. © 2022 ACM.

关键词： Feature Selection

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Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing

Energy-Optimized with Multi-Population Differential Annealed...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wu, Ruixuan Shi, Yuliang Yuan, Haitao Bi, Jing Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Mobile devices (MDs) cannot fully run all computation/delay-sensitive tasks due to their limited computing resources. Mobile edge computing (MEC) meets the demand by providing massive resources for MDs and offloading task partitions to MEC servers. However, task offloading also brings communication delay and energy consumption. Therefore, it is challenging to associate resource-constrained MDs with appropriate MEC servers to minimize power consumption. To address this problem, a constrained mixed integer nonlinear program is formulated to optimize the total energy consumption of the system including MDs and MEC servers. To solve this problem, this work designs an improved meta-heuristic optimization algorithm called Self-adaptive and Multi-population Differential Annealed Optimization (SMDAO). Experimental results demonstrate that compared with its two state-of-the-art peers, the proposed SMDAO yields the best solution with the smallest total energy consumption in the least time. © 2023 IEEE.

关键词： computation offloading energy optimization Mobile edge computing multi-population optimization simulated annealing

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A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis

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computers, Materials & Continua 2022年第9期72卷 6029-6044页

作者： Ankur Dumka Parag Verma Rajesh Singh Anil Kumar Bisht Divya Anand Hani Moaiteq Aljahdali Irene Delgado Noya Silvia Aparicio Obregon Computer Science and Engineering Women Institute of TechnologyUttarakhand-248007India Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Research and Innovation Uttranchal UniversityDehradunUttrakhand248007India Department of Computer Science&IT MJP Rohilkhand UniversityBareilly236006UPIndia Computer Science and Engineering Lovely Professional UniversityPunjab144411India Higher Polytechnic School Universidad Europea del Atlántico39011SantanderSpain Faculty of Computing and Information Technology King Abdulaziz University37848JeddahSaudi Arabia Universidad Internacional Iberoamericana CampecheMexico Universidade Internacional do Cuanza Bairro Kaluanda EN 250CuitoBié.Angola

Coronavirus(COVID-19)has impacted nearly every person across the globe either in terms of losses of life or as of *** current coronavirus(COVID-19)pandemic is a rare/special situation where people can express their feelings on Internet-based social *** media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their *** research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 *** research is based on the logic expressed by people in this perspective and emotions for the suffering of COVID-19 and *** this research,we have used a Long Short-Term Memory(LSTM)network model with Convolutional Neural Network using Keras python deep-learning library to determine whether social media platform users are depressed in terms of positive,negative,or neutral emotional out bust based on their Twitter *** results showed that the model has 88.14%accuracy(representation of the correct prediction over the test dataset)after 10 epochs which most tweets showed had neutral *** evaluation shows interesting results in positive(1),negative(–1),and neutral(0)emotions through different visualization.

关键词： COVID-19 lockdown stress analysis depression analysis sentiment analysis social media COVID-19 twitter dataset coronavirus

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Methodology of Transformation of Fuzzy Queries into Queries in the SQL Standard

Methodology of Transformation of Fuzzy Queries into Queries ...

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IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems

作者： Grzegorz Nowakowski Department of Automatic Control and Information Technology Faculty of Electrical and Computer Engineering Cracow University of Technology

ISBN: (纸本)9781728140704

The article presents two approaches to the method of implementation of transformations of fuzzy queries into queries in the SQL standard. This implementation can be carried out: (1) in a database (in the form of functions, procedures, triggers or other acceptable form), using the existing query processing system in the database, and thus it can expand the functionality of that database;(2) in an application (which communicates with the database), and thus a considerable portion of calculations will be transferred from the database to that application. An analysis of those approaches was conducted and it was demonstrated that the presented methods should enjoy extensive practical application.

关键词： Fuzzy logic Fuzzy queries Conversion Transformation Methodology SQL SQL Database Fuzzy logic methodology

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An Adaptive Framework for Classification and Detection of Android Malware

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第21期18卷 59-73页

作者： Al Sharah, Ashraf Alrub, Yousef Abu Owida, Hamza Abu Elsoud, Esraa Abu Alshdaifat, Nawaf Khtatnaha, Hamzah Department of Electrical Engineering College of Engineering Technology Al-Balqa Applied University Amman Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Medical Engineering Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Information Technology Applied Science Private University Amman Jordan

The hardware and software of a computer are controlled by its operating system (OS), which performs essential tasks such as input and output processing, file and memory management, and the management of peripheral devices such as disk drives and printers. Application software refers to programs designed for specific purposes, these applications, often freely available and open source, contribute to the rising number of downloads. In the third quarter of 2022, combined downloads from the Apple App Store and Google Play Reached an estimated 35.3 billion. However, the prevalence of insecurity in these applications and technologies heightens the potential for cybercrimes. Protection against unauthorized intruders is crucial in identifying malicious applications. Machine learning (ML) serves as a promising avenue for detecting malware attacks, offering potential solutions to bolster cybersecurity measures. We propose a novel approach utilizing ML to enhance malware detection accuracy by segmenting datasets into distinct groups. Our research employs supervised ML techniques on the CICMaldroid2020 dataset, which includes comprehensive information such as intent actions, permissions, and sensitive APIs. The dataset was partitioned into four groups, each containing 150 features, and analyzed across four experiments to distinguish between attack and benign classes. Our proposed model demonstrated exceptional performance, with the random forest algorithm achieving an accuracy of 98.6% and a precision of 98.75%. These results highlight the effectiveness of our segmentation approach and its significant contribution to advancing malware detection in Android applications, offering a promising direction for future cybersecurity solutions. © 2024 by the authors of this article.

关键词： Android malware

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