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Hybrid lightweight encryption for IoT: integrating chaotic key generators with Feistel and substitution-permutation networks for secure 3DV transmission

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Multimedia Tools and Applications 2025年 1-47页

作者： El-Shafai, Walid Mesrega, Ahmed K. Ahmed, Hossam Eldin H. Elbahnasawy, Nirmeen A. El-Samie, Fathi E. Abd Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Deptartment of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The Internet of Things (IoT) stands as a revolutionary leap in digital connectivity, envisioning a future network connecting billions of devices, seamlessly. Amidst the myriad benefits, there arises an intricate web of challenges, prominently centered around potential threats and data security implications. Recent cryptography techniques, such as DNA-based cryptography, 3D chaos-based cryptography, and optical cryptography, face challenges including large encryption times, high energy consumption, and suboptimal rather than optimal performance. Particularly, the burden of long encryption cycles strains the energy resources of typical low-power and compact IoT devices. These challenges render the devices vulnerable to unauthorized breaches, despite large storage capacities. The hallmark of the IoT ecosystem, characterized by its low-power compact devices, is the burgeoning volume of data they generate. This escalating data influx, while necessitating expansive storage, remains vulnerable to unauthorized access and breaches. Historically, encryption algorithms, with their multifaceted architectures, have been the bulwark against such intrusions. However, their inherently-complex nature, entailing multiple encryption cycles, strains the limited energy reserves of typical IoT devices. In response to this intricate dilemma, we present a hybrid lightweight encryption strategy. Our algorithm innovatively leverages both one-dimensional (1D) and two-dimensional (2D) chaotic key generators. Furthermore, it amalgamates a classical encryption philosophy, harmonizing the strengths of Feistel and substitution-permutation networks. The centerpiece of our strategy is achieving effective encryption in merely three rounds, tailored expressly for compressed Three-Dimensional Video (3DV) frames, ensuring their unwavering integrity. Our workflow commences with the H.264/MVC compression algorithm, setting the stage for the subsequent encryption phase. Through rigorous MATLAB simulations,

关键词： Image coding

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Ensemble Learning for Breast Cancer Image Classification Using Pre-trained CNNs and Random Oversampling Method 5

Ensemble Learning for Breast Cancer Image Classification Usi...

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5th International Conference on Advanced information Technologies, ICAIT 2024

作者： Thuzar, Win Myat Moe Htay, Moe Uinversity of Computer Studies Mandalay Department of Information Technology Supporting and Maintenance Mandalay Myanmar Uinversity of Computer Studies Mandalay Faculty of Computer Science Mandalay Myanmar

ISBN: (纸本)9798331510046

The classification of breast cancer images presents significant challenges, especially when dealing with imbalanced datasets that underrepresent minority classes. This study tackles the issue by implementing ensemble learning techniques with pre-trained convolutional neural networks (CNNs) such as ResNet50, GoogleNet, and EfficientNet. The random oversampling method was employed to address the imbalance, which effectively increases the representation of minority classes within the dataset. This study performs detailed data analysis after this adjustment to improve the model's ability to identify these minority classes. By combining the predictions of the three models into an ensemble, the approach aims to improve both the accuracy and robustness of the classification process. The ensemble method works much better at finding minority classes, as shown by the results of experiments. This proves that it can handle uneven datasets when classifying breast cancer images. © 2024 IEEE.

关键词： Convolutional neural networks

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Preliminary Exploration on Node-To-Node Fault Tolerance Coordination in Distributed System

Preliminary Exploration on Node-To-Node Fault Tolerance Coor...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Saadoon, Muntadher Hamid, Siti Hafizah Ab. Sofian, Hazrina Altarturi, Hamza Nasuha, Nur Azizul, Zati Hakim Khalid, Haqi Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Artificial Intelligence Kuala Lumpur50603 Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Selangor 43400 Malaysia

ISBN: (纸本)9798350302684

Internet of Things, edge computing devices, the widespread use of artificial intelligence and machine learning applications, and the extensive adoption of cloud computing pose significant challenges to maintaining fault tolerance in distributed systems. As the volume and complexity of systems continue to grow, the probability of system failures also grows, leading to downtime and poor performance. Fault tolerance is pivotal in modern computing systems by preventing downtime and service disruptions, even during failures. However, adopting fault tolerance comes with additional resource consumption, such as memory, CPU, disk, and network bandwidth usage, impacting system performance. Balancing fault tolerance with system performance is crucial to optimize resource utilization while minimizing the costs associated with detecting and recovering from faults. This paper introduces a node-to-node fault tolerance coordination mechanism for distributed systems to facilitate the tradeoff between fault tolerance and the cost of performance penalties and resource consumption. The proposed mechanism is preliminarily implemented on HDFS cluster. Our proposal enables opportunities to minimize the cost of the fault tolerance impact on performance and resource usage compared to the existing fault tolerance coordination mechanism. © 2023 IEEE.

关键词： Fault tolerance

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Generating of Test Data by Harmony Search Against Genetic Algorithms

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Intelligent Automation & Soft Computing 2023年第4期36卷 647-665页

作者： Ahmed S.Ghiduk Abdullah Alharbi Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceBeni-Suef UniversityBeni-Suef62521Egypt

Many search-based algorithms have been successfully applied in sev-eral software engineering *** algorithms(GAs)are the most used in the scientiﬁc domains by scholars to solve software testing *** imi-tate the theory of natural selection and *** harmony search algorithm(HSA)is one of the most recent search algorithms in the last *** imitates the behavior of a musician toﬁnd the best *** have estimated the simi-larities and the differences between genetic algorithms and the harmony search algorithm in diverse research *** test data generation process represents a critical task in software ***,there is no work comparing the performance of genetic algorithms and the harmony search algorithm in the test data generation *** paper studies the similarities and the differences between genetic algorithms and the harmony search algorithm based on the ability and speed ofﬁnding the required test *** current research performs an empirical comparison of the HSA and the GAs,and then the signiﬁcance of the results is estimated using the *** study investigates the efﬁciency of the harmony search algorithm and the genetic algorithms according to(1)the time performance,(2)the signiﬁcance of the generated test data,and(3)the adequacy of the generated test data to satisfy a given testing *** results showed that the harmony search algorithm is signiﬁcantly faster than the genetic algo-rithms because the t-Test showed that the p-value of the time values is 0.026<α(αis the signiﬁcance level=0.05 at 95%conﬁdence level).In contrast,there is no signiﬁcant difference between the two algorithms in generating the adequate test data because the t-Test showed that the p-value of theﬁtness values is 0.25>α.

关键词： Harmony search algorithm genetic algorithms test data generation

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Performance Improvement Technique of RSA Algorithm in Document Data Security 12

Performance Improvement Technique of RSA Algorithm in Docume...

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12th International Conference on Cyber and IT Service Management, CITSM 2024

作者： Wandira, Agtori Nasution, Benny Benyamin Zarlis, M. Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia Politeknik Negeri Medan Departement of Computer Engineering Medan Indonesia

ISBN: (纸本)9798331528799

One of the first public key schemes was developed in 1977 by Ron Rivest, Adi Shamir, and Len Adleman at MIT and first published in 1978. Since then, the RSA scheme has become the most widely accepted and applied public-key encryption approach. RSA is a block cipher where the plaintext and ciphertext are integers between 0 and n - 1 for some $n$. However, since the calculations involved (both in key generation and in encryption/decryption) are complex, the larger the key size, the slower the system will run. To overcome the performance constraints of the RSA algorithm, various techniques such as iteration, Divide and Conquer, and Square and Multiply have been developed. Tests were conducted on files with various key lengths to evaluate encryption and decryption performance. The results show that all three algorithms experience an increase in computation time as the key length increases. The iteration algorithm shows the best performance for short keys, while the square and multiply algorithm provides the highest efficiency for medium to long keys. Further testing recommends using square and multiply for keys of 128 bits and above due to its superior efficiency. It can be concluded that the selection of appropriate support algorithms can overcome RSA's performance issues without compromising security, as well as provide suggestions for further security analysis and development of more efficient algorithms. © 2024 IEEE.

关键词： Ciphertext

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An Efficient Path Planning Strategy in Mobile Sink Wireless Sensor Networks

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computers, Materials & Continua 2022年第10期73卷 1237-1267页

作者： Najla Bagais Etimad Fadel Amal Al-Mansour Department of Computer Science Faculty of Computing and Information TechnologyKing Abdul-Aziz UniversityJeddah21589Saudi Arabia

Wireless sensor networks (WSNs) are considered the backbone ofthe Internet of Things (IoT), which enables sensor nodes (SNs) to achieveapplications similarly to human intelligence. However, integrating a WSNwith the IoT is challenging and causes issues that require careful *** the lifetime of a network through appropriately utilising energyconsumption is among the essential challenges due to the limited resourcesof SNs. Thus, recent research has examined mobile sinks (MSs), which havebeen introduced to improve the overall efficiency of WSNs. MSs bear theburden of data collection instead of consuming energy at the routeing bySNs. In a network, some areas generate more data through SNs that containfrequent, urgent messages. These messages carry sensitive data that must bedelivered immediately to user applications. Collecting such messages via MSs,especially on a large scale, increases delays, which are not tolerable in some realapplications. This issue has not been studied much. Thus, the present studyutilises the advantages of the priority parameter to concentrate on these areasand proposes a new model named ‘energy efficient path planning of MS-basedarea priority’ (EEPP-BAP). This method involves non-urgent and urgentmessages. It is comprised of four procedures. Initially, after SNs are distributedrandomly in a wide monitoring field, the monitoring field is partitionedinto equal zones according to priority, either differently or equally. Next isclustering based on the cluster head (CH) selected to perform the particleswarm optimisation algorithm (PSO). Then, the MS moves first to the zoneswith higher priority and less distance to perform the brain storm optimisationalgorithm. Finally, for urgent messages from the other zones at which theMS continues, the proposed approach establishes a routeing technique usingmulti-hop communication based on the MS position and using PSO. The proposed solution is aimed at delivering urgent messages to MSs free of

关键词： Wireless sensor network priority urgent message swarm intelligence optimisation mobile sink clustering energy efficiency

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Decision Tree-Based IoT Botnet Attack Detection 2

Decision Tree-Based IoT Botnet Attack Detection

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Najjar, Hazem Al-Rousan, Nadia College Of Information Technology University Of Petra Department Of Computer Science Amman Jordan German Jordanian University Faculty Of Electrical Engineering And Information Systems Department Of Computer Engineering Jordan

ISBN: (纸本)9798350394962

This paper investigated the predictive capabilities of three decision tree models for IoT botnet attack prediction using packet information while minimizing the number of predictors. The study employed three decision tree models (C5, CHAID, and Random Forest) and two additional models (Logistic Regression and Bayesian Network) for comparison purposes. The IoT botnet attack dataset comprised various devices, mainly two types of attacks (Gafgyt and Mirai) and 23 feature engineering variables. Simulation results from all models showed that Accuracy, Precision, Recall, and False Omission Rate (FOR) values are approximately one, with an F1 score of around 0.5. CHAID and C5 models outperform other models in predicting IoT botnet attacks, as they are developed using only four and two variables, respectively. These results demonstrated that decision tree models with fewer variables can perform better than models that utilize all predictors. © 2024 IEEE.

关键词： Forecasting

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A Review of Fintech Management and Security Development 10

A Review of Fintech Management and Security Development

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10th IEEE information technology International Seminar, ITIS 2024

作者： Ritonga, Ali Akbar Zuhri Harahap, Syaiful Sooai, Adri Gabriel Nugroho, Aryo Samsir Prasetyo Adi, Puput Dani Universitas Labuhanbatu Faculty of Science and Technology Department of Informatics Management North Sumatera Labuhanbatu Indonesia Universitas Labuhanbatu Faculty of Science and Technology Department of Information System North Sumatera Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Universitas Narotama Faculty of Computer Science Department of Information System Surabaya Indonesia Universitas Labuhanbatu Faculty of Computer Science Department of Informatics Engineering North Sumatera Labuhanbatu Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This research focuses on the review of Fintech and its development on the IoT Platform and also the risks that can be posed to the IoT network used. Finance is the most essential side of several other sectors which in this case often experience many losses, one of the biggest losses is the existence of hackers and theft of money through the Internet platform or Internet Server. This research reviews how the ins and outs of the flow of funds located on servers or online banks. And ways to overcome hackers and bypass data. Comprehensively reviewed in this research review. This research discusses Cryptocurrency Analysis, Bitcoin, and Ethereum Comparison which are some of the methods used in online-based Finance, which is without currency but has the same value even more than currency if the conversion process is carried out at the bank. In this research, it is comprehensively discussed how methods are carried out in the process of securing data, Cryptocurrency, Bitcoin, and Ethereum are comprehensively discussed to get a lot of data and comparisons and the right steps to deal with crime via the internet. The predicted use of IoT in the Fintech sector is 140 billion connected devices, IoT Loans 110 billion connected devices, and IoT insurance with an estimated 90 billion connected devices, in the approaching 2026. © 2024 IEEE.

关键词： Ethereum

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Why Should I Trust Your Explanation? An Evaluation Approach for XAI Methods Applied to Predictive Process Monitoring Results

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1458-1472页

作者： Elkhawaga, Ghada Elzeki, Omar M. Abu-Elkheir, Mervat Reichert, Manfred Ulm University Institute of Databases and Information Systems Ulm89081 Germany Mansoura University Faculty of Computers and Information Mansoura35516 Egypt New Mansoura University Faculty of Computer Science and Engineering Mansoura35516 Egypt German University in Cairo Faculty of Media Engineering and Technology New Cairo11835 Egypt

As a use case of process mining, predictive process monitoring (PPM) aims to provide information on the future course of running business process instances. A large number of available PPM approaches adopt predictive models based on machine learning (ML). With the improved efficiency and accuracy of ML models usually being coupled with increasing complexity, their understandability becomes compromised. Having the user at the center of attention, various eXplainable artificial intelligence (XAI) methods emerged to provide users with explanations of the reasoning process of an ML model. Though there is a growing interest in applying XAI methods to PPM results, various proposals have been made to evaluate explanations according to different criteria. In this article, we propose an approach to quantitatively evaluate XAI methods concerning their ability to reflect the facts learned from the underlying stores of business-related data, i.e., event logs. Our approach includes procedures to extract features that are crucial for generating predictions. Moreover, it computes ratios that have proven to be useful in differentiating XAI methods. We conduct experiments that produce useful insights into the effects of the various choices made through a PPM workflow. We can show that underlying data and model issues can be highlighted using the applied XAI methods. Furthermore, we could penalize and reward XAI methods for achieving certain levels of consistency with the facts learned about the underlying data. Our approach has been applied to different real-life event logs using different configurations of the PPM workflow. © 2020 IEEE.

关键词： Process monitoring

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