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Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning Model for Intrusion Detection System

computer Systems science & Engineering 2023年第11期47卷 2497-2512页

作者： Mahmoud Ragab Sultanah M.Alshammari Abdullah S.Al-Malaise Al-Ghamdi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The Internet of Things(IoT)system has confronted dramatic growth in high dimensionality and data *** system named intrusion detection systems(IDS)is broadly utilized for the enhancement of security posture in an IT *** IDS is a practical and suitable method for assuring network security and identifying attacks by protecting it from intrusive ***,machine learning(ML)-related techniques were used for detecting intrusion in IoTs ***,the IoT IDS mechanism faces significant challenges because of physical and functional *** IoT features use every attribute and feature for IDS self-protection unrealistic and *** study develops a Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning(MM-WMVEDL)model for *** proposed MM-WMVEDL technique aims to discriminate distinct kinds of attacks in the IoT *** attain this,the presented MM-WMVEDL technique implements min-max normalization to scale the input *** feature selection purposes,the MM-WMVEDL technique exploits the Harris hawk optimization-based elite fractional derivative mutation(HHO-EFDM)*** the presented MM-WMVEDL technique,a Bi-directional long short-term memory(BiLSTM),extreme learning machine(ELM)and an ensemble of gated recurrent unit(GRU)models take place.A wide range of simulation analyses was performed on CICIDS-2017 dataset to exhibit the promising performance of the MM-WMVEDL *** comparison study pointed out the supremacy of the MM-WMVEDL method over other recent methods with accuracy of 99.67%.

关键词： Internet of Things intrusion detection system machine learning ensemble deep learning metaheuristics

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DE-ConvGraph 3D UNet: A Novel Deep Learning Model for Optimizing Radiotherapy Treatment Plans in Oropharyngeal Cancer

DE-ConvGraph 3D UNet: A Novel Deep Learning Model for Optimi...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Murugadoss, Bhuvanashree Amudha, J. Sugumaran, Vijayan Amrita Vishwa Vidyapeetham School of Computing Department of Computer Science and Engineering Karnataka Bengaluru India Oakland University Center for Data Science and Big Data Analytics RochesterMI United States Oakland University Department of Decision and Information Sciences RochesterMI United States

ISBN: (纸本)9798350359527

Automated radiotherapy treatment planning aims to improve treatment accuracy and efficiency. However, the prevalent Knowledge-Based Planning (KBP) method faces issues like lengthy manual problem formulation and challenges in accurately modeling human anatomy and processing high-dimensional data. This work focuses on treatment plan optimization and considers deep learning as a potential solution. Deep learning algorithms, by virtue of their ability to learn from large quantities of data and model complex relationships, can automate the formulation of the optimization problem in KBP, saving significant time and effort. However, despite these compelling advantages, it should be noted that the current convolution-based encoder-decoder models used for radiotherapy treatment plan optimization have a limited capability in capturing long-range dependencies between distant voxels. This work aims to introduce 'DE-ConvGraph 3D UNet', a novel deep learning model, to address these limitations and optimize radiotherapy treatment plans for oropharyngeal cancer. The proposed 'DE-ConvGraph 3D UNet' model includes a Graph Convolutional Network (GCN) component to capture long-range dependencies between distant voxels. Furthermore, the dual-encoder structure of the model combines the strengths of GCN and 3D Convolutional U-Net, enabling global relationships and local pattern capturing in a 3D patient volume. Experiments were conducted using the benchmark dataset-OpenKBP-Opt. The proposed model shows improvements in performance in comparison to state-of-The-Art U-Net variants in terms of mean squared voxel-wise error, dose volume histogram points and clinical criteria satisfaction. © 2023 IEEE.

关键词： Learning algorithms

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slackFS – resilient and persistent information hiding framework

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International Journal of Security and Networks 2024年第2期19卷 77-91页

作者： Srinivasan, Avinash Rose, Christian Wu, Jie Department of Cyber Science United States Naval Academy 597 McNair Road Hopper Hall – Room 478 AnnapolisMD21402 United States Amber Orchard Ct. OdentonMD21113 United States Department of Computer and Information Sciences Center of Networked Computing Temple University SERC 362 1925 N. 12th Street PhiladelphiaPA19122 United States

The ever-expanding cyberspace, driven by digital convergence, inadvertently broadens the attack surface. Savvy modern cybercriminals have embraced steganography as a key weapon. This paper introduces slackFS, a novel steganographic framework utilising file slack space for covert data concealment. Unlike prior methods focusing on individual files, slackFS hides entire filesystems, offering a structured means for data exfiltration. It ensures persistence across system reboots, robust detection resistance, portability, and minimal performance impact. Incorporating erasure-code-based fault-tolerance, slackFS enables recovery from partial loss due to accidental slack space overwriting. Prototype validation on Ubuntu 20.04 with ext4 filesystems as the cover medium and FAT16 as the hidden malicious filesystem is conducted. The study includes testing of three coding libraries and two Reed-Solomon erasure code implementations – VANDERMONDE and CAUCHY matrices – highlighting slackFS’s resilience and effectiveness. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Steganography

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Cybersecurity in Universities: An Evaluation Model

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SN computer science 2023年第5期4卷 569页

作者： Haque, Md Alimul Ahmad, Sultan John, Alok Mishra, Khushboo Mishra, Binay Kumar Kumar, Kailash Nazeer, Jabeen Department of Computer Science Veer Kunwar Singh University Ara 802301 India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Advertising and Marketing Management (AMM) Patna Women’s College Patna India Department of Physics Veer Kunwar Singh University Ara 802301 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

Cybercriminals have been increasingly targeting higher education institutions during the COVID-19 pandemic. The shift to online learning and remote work has created new opportunities for cybercriminals to exploit vulnerabilities in university networks and systems. This paper highlights the challenges faced by universities in ensuring the security of their information systems and employee data and the potential consequences of cyberattacks. The objective of this study is to conduct a comprehensive assessment of the cybersecurity situation in university facilities by identifying, quantifying, and modeling it. The resulting model will enable educational institutions to recognize potential operational risks and determine which measures to implement or improve to mitigate those risks. Consequently, we will create a novel assessment tool in this study to offer universities a precise representation of their current status, aiding university designers in incorporating the recommended cyber risk mitigation measures into the system design before deployment. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Attack Evaluation HEIs Malware Network threat Phishing University

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Assessing the Impact of IoT Enabled E-Learning System for Higher Education

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SN computer science 2023年第5期4卷 459页

作者： Haque, Shameemul Haque, Md Alimul Kumar, Devanshu Mishra, Khushboo Islam, Farheen Ahmad, Sultan Kumar, Kailash Mishra, Binay Kumar Al-Hafeez College Ara India Department of Computer Science Veer Kunwar Singh University Ara 802301 India Department of Physics Veer Kunwar Singh University Ara 802301 India Department of Education Patna Women’s College Patna India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

In this paper, a hypothetical investigation alongside a genuine review is investigated. The theoretical and empirical results demonstrated the significant impact of IoT in the physical training system in terms of learning and making due factors. Now and again, like the hyper-availability, joint effort, and exploration of potential open doors, the impact is critical. Eminent advancements, like, the IoT at present, are quickly created in the computerized world and changing the conventional school system into a more versatile, adaptable, and more productive E-learning system, where the enormous number of physical and virtual collaborating objects are engaged with the most common way of learning. IoT is a somewhat developing innovation that works with communication between genuine and virtual items. As it creates and designs, it fills in scale and aspects that impact the setting of our lives, such as education, particularly advanced education;this article focuses on examining the anticipated impact of IoT in unrivaled training. It is anticipated that the Internet of Things will enable the development of intelligent E-learning frameworks capable of anticipating and deciding the specific instructional needs of understudies based on data analysis from related data. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AI Distance learning Education Higher education IoT Learning platform

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Granular Network Traffic Classification on Mobile Application Service for Better Parental Control

Granular Network Traffic Classification on Mobile Applicatio...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Dhivvya J P Guru Aswini Dath Mahesh Kumar Chinta Ankit Karki Department of Computer Science and Engineering Amrita School of Computing Center for Wireless Networks and Applications Amrita Vishwa Vidyapeetham Amritapuri Kollam India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kollam India

The widespread use of mobile applications among children had its limitations in excessive screen usage in entertainment. Traditional parental control techniques mostly depend on blocking applications based on their name or categories and are ineffective at identifying potentially new dangerous applications. This study addresses this issue by introducing a paradigm shift for protecting kids' digital experiences through granular network traffic classifications. This system classifies mobile app network traffic into various groups by implementing machine learning or deep learning models. Machine learning models like linear regression, Adaboost Classifier, Support Vector Classification (SVC), Ridge Classifier and deep learning models like Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) were used to identify and classify mobile applications. Initially, the deep learning RNN model results had 80% accuracy in the classification of mobile apps, and the machine learning logistic SVC and Adaboost Classifier had 72% accuracy. After adding new applications to the existing dataset, the accuracy of the RNN model has remained the same 80% accuracy. Meanwhile, the machine learning model accuracy reduced to 70%, proving the flexibility of the system. This work can assist parents in monitoring their kids to use mobile applications more for education. This work has a huge scope for information technology management of social change using hybrid learning models in software-defined networking in local networks for the administrators to empower their users to wisely use the internet.

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Verifying quantum phase estimation using an expressive theorem-proving assistant

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Physical Review A 2023年第5期108卷 052609-052609页

作者： Wayne M. Witzel Warren D. Craft Robert Carr Deepak Kapur Center for Computing Research Quantum Computer Science Sandia National Laboratories Albuquerque New Mexico 87123 USA Department of Computer Science The University of New Mexico Albuquerque New Mexico 87131 USA

The general-purpose interactive theorem-proving assistant called prove-it was used to verify the quantum phase estimation (QPE) algorithm, specifically claims about its outcome probabilities. prove-it is unique in its ability to express sophisticated mathematical statements, including statements about quantum circuits, integrated firmly within its formal theorem-proving framework. We demonstrate our ability to follow a textbook proof to produce a formally certified proof, highlighting useful automation features to fill in obvious steps and make formal proving nearly as straightforward as informal theorem proving. Finally, we make comparisons with formal theorem proving in other systems where similar claims about QPE have been proven.

关键词： Quantum algorithms Quantum computation Quantum parameter estimation

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Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

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computers, Materials & Continua 2023年第2期74卷 3101-3115页

作者： Mohammed Maray Hamed Alqahtani Saud S.Alotaibi Fatma S.Alrayes Nuha Alshuqayran Mrim M.Alnfiai Amal S.Mehanna Mesfer Al Duhayyim Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of information systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

Cloud computing(CC)is the most promising and advanced technology to store data and offer online services in an effective *** such fast evolving technologies are used in the protection of computerbased systems from cyberattacks,it brings several advantages compared to conventional data protection *** of the computer-based systems that effectively protect the data include Cyber-Physical Systems(CPS),Internet of Things(IoT),mobile devices,desktop and laptop computer,and critical *** software(malware)is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity,secrecy,and accessibility of the *** current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification(OBDDBNCMC)*** presentedOBDDBN-CMCmodel intends to recognize and classify the malware that exists in IoT-based cloud *** attain this,Zscore data normalization is utilized to scale the data into a uniform *** addition,BDDBN model is also exploited for recognition and categorization of *** effectually fine-tune the hyperparameters related to BDDBN model,GrasshopperOptimizationAlgorithm(GOA)is *** scenario enhances the classification results and also shows the novelty of current *** experimental analysis was conducted upon OBDDBN-CMC model for validation and the results confirmed the enhanced performance ofOBDDBNCMC model over recent approaches.

关键词： Malware detection security Internet of Things cloud computing machine learning parameter adjustment

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A Hierarchical Deep Learning Approach for Predicting Job Queue Times in HPC Systems

A Hierarchical Deep Learning Approach for Predicting Job Que...

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High Performance computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Austin Lovell Philip Wisniewski Sarah Rodenbeck Ashish Department of Computer Science Purdue University Indiana USA Rosen Center for Advanced Computing Purdue University Indiana USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Accurate wait-time prediction for HPC jobs contributes to a positive user experience but has historically been a challenging task. Previous models lack the accuracy needed for confident predictions, and many were developed before the rise of deep *** this work, we investigate and develop TROUT, a neural network-based model to accurately predict wait times for jobs submitted to the Anvil HPC cluster. Data was taken from the Slurm Workload Manager on the cluster and transformed before performing additional feature engineering from jobs’ priorities, partitions, and states. We developed a hierarchical model that classifies job queue times into bins before applying regression, outperforming traditional methods. The model was then integrated into a CLI tool for queue time prediction. This study explores which queue time prediction methods are most applicable for modern HPC systems and shows that deep learning-based prediction models are viable solutions.

关键词： Deep learning Accuracy High performance computing Conferences Computational modeling Predictive models User experience Resource management Queueing analysis

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Multi-Attribute Decision-Making for Intrusion Detection Systems: A Systematic Review

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International Journal of Information Technology and Decision Making 2023年第1期22卷 589-636页

作者： Alamleh, Amneh Albahri, O.S. Zaidan, A.A. Alamoodi, A.H. Albahri, A.S. Zaidan, B.B. Qahtan, Sarah Binti Ismail, Amelia Ritahani Malik, R.Q. Baqer, M.J. Jasim, Ali Najm Al-Samarraay, Mohammed S. Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim35900 Malaysia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Faculty of Engineering and IT The British University in Dubai United Arab Emirates Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Center Middle Technical University's Baghdad Iraq International Islamic University Malaysia Kuala Lumpur Malaysia Department of Artificial Intelligence Faculty of Information Technology Zarqa University Zarqa Jordan Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University College Hillah Iraq Foundation of Alshuhda Baghdad Iraq

Intrusion detection systems (IDSs) employ sophisticated security techniques to detect malicious activities on hosts and/or networks. IDSs have been utilized to ensure the security of computer and network systems. However, numerous evaluation and selection issues related to several cybersecurity aspects of IDSs were solved using a decision support approach. The approach most often utilized for decision support in this regard is multi-Attribute decision-making (MADM). MADM can aid in selecting the most optimal solution from a huge pool of available alternatives when the appropriate evaluation attributes are provided. The openness of the MADM methods in solving numerous cybersecurity issues makes it largely efficient for IDS applications. We must first understand the available solutions and gaps in this area of research to provide an insightful analysis of the combination of MADM techniques with IDS and support researchers. Therefore, this study conducts a systematic review to organize the research landscape into a consistent taxonomy. A total of 28 articles were considered for this taxonomy and were classified into three main categories: data analysis and detection (n=4), response selection (n=7) and IDS evaluation (n=17). Each category was thoroughly analyzed in terms of a variety of aspects, including the issues and challenges confronted, as well as the contributions of each study. Furthermore, the datasets, evaluation attributes, MADM methods, evaluation and validation and bibliography analysis used by the selected articles are discussed. In this study, we highlighted the existing perspective and opportunities for MADM in the IDS literature through a systematic review, providing researchers with a valuable reference. © 2023 World Scientific Publishing Company.

关键词： Taxonomies

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