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Hybrid two-level protection system for preserving pre-trained DNN models ownership

Neural Computing and Applications 2024年第34期36卷 21415-21449页

作者： Fkirin, Alaa Moursi, Ahmed Samy Attiya, Gamal El-Sayed, Ayman Shouman, Marwa A. Department of Electrical Engineering Faculty of Engineering Fayoum University Fayoum Egypt Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia Governorate Menouf32952 Egypt

Recent advancements in deep neural networks (DNNs) have made them indispensable for numerous commercial applications. These include healthcare systems and self-driving cars. Training DNN models typically demands substantial time, vast datasets and high computational costs. However, these valuable models face significant risks. Attackers can steal and sell pre-trained DNN models for profit. Unauthorised sharing of these models poses a serious threat. Once sold, they can be easily copied and redistributed. Therefore, a well-built pre-trained DNN model is a valuable asset that requires protection. This paper introduces a robust hybrid two-level protection system for safeguarding the ownership of pre-trained DNN models. The first-level employs zero-bit watermarking. The second-level incorporates an adversarial attack as a watermark by using a perturbation technique to embed the watermark. The robustness of the proposed system is evaluated against seven types of attacks. These are Fast Gradient Method Attack, Auto Projected Gradient Descent Attack, Auto Conjugate Gradient Attack, Basic Iterative Method Attack, Momentum Iterative Method Attack, Square Attack and Auto Attack. The proposed two-level protection system withstands all seven attack types. It maintains accuracy and surpasses current state-of-the-art methods. © The Author(s) 2024.

关键词： Deep neural networks

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Drug–target interactions prediction based on similarity graph features extraction and deep learning

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Neural Computing and Applications 2025年第6期37卷 4303-4322页

作者： Torkey, Hanaa El-Behery, Heba Attia, Abdel-Fattah El-Fishawy, Nawal Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Computer Science and Engineering Faculty of Engineering Kafrelsheikh University Kafrelsheikh Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Identifying drug–target interactions (DTIs) is a critical step in both drug repositioning. The labor-intensive, time-consuming, and costly nature of classic DTI laboratory studies makes it imperative to create efficient computer algorithms to forecast possible DTIs. However, current computational approaches that predict potential drug–target interactions (DTIs) suffer from some limitations, like finding the best similarity measures or negative samples, and thus require substantial performance improvement. This study proposes an integrated approach based on feature representation and deep learning to predict DTIs. We extract the relevant features of drugs and proteins from heterogeneous networks using graph mining techniques. The proposed approach constructs a heterogeneous graph from the known drug–protein interactions, protein–protein, and drug–drug similarities. Then applying two feature extraction techniques to extract the features, then utilizing these features in training a deep learning model to predict the potential DTIs. Also, a novel algorithm is proposed to find the negative samples based on the drug and protein similarity matrices. Four Benchmark datasets are used to evaluate the proposed approach. Our approach achieves the highest AUC (area under the ROC curve) across all datasets (0.98) with around 2% increases over the existing methods. Experimental results demonstrate that our proposed approach outperforms the baseline methods in predicting DTI, and our negative sample-identifying algorithm could be established as a competitive solution. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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Care4U: Integrated healthcare systems based on blockchain

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Blockchain(Research and Applications) 2023年第4期4卷 2-13页

作者： Randa Kamal Ezz El-Din Hemdan Nawal El-Fishway Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityEgypt

During the COVID-19 crisis, the need to stay at home has increased dramatically. In addition, the number of sickpeople, especially elderly persons, has increased exponentially. In such a scenario, home monitoring of patientscan ensure remote healthcare at home using advanced technologies such as the Internet of Medical Things (IoMT).The IoMT can monitor and transmit sensitive health data;however, it may be vulnerable to various attacks. In thispaper, an efficient healthcare security system is proposed for IoMT applications. In the proposed system, themedical sensors can transmit sensed encrypted health data via a mobile application to the doctor for ***, three consortium blockchains are constructed for load balancing of transactions and reducing transactionlatency. They store the credentials of system entities, doctors' prescriptions and recommendations according to thedata transmitted via mobile applications, and the medical treatment process. Besides, cancelable biometrics areused for providing authentication and increasing the security of the proposed medical system. The investigationalresults show that the proposed system outperforms existing work where the proposed model consumed lessprocessing time by values of 18%, 22%, and 40%, and less energy for processing a 200 KB file by values of 9%,13%, and 17%. Finally, the proposed model consumed less memory usage by values of 7%, 7%, and 18.75%. Fromthese results, it is clear that the proposed system gives a very reliable and secure performance for efficientlysecuring medical applications.

关键词： Healthcare Blockchain Cyber security Internet of things Internet of Medical Things Cancelable biometrics

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Food ingredient recognition model via image and textual feature extraction and hybrid classification strategy

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International Journal of Reasoning-based Intelligent Systems 2024年第1期16卷 74-90页

作者： Madival, Sharanabasappa A. Jawaligi, Shivkumar S. Department of Computer Science and Engineering Faculty of Engineering and Technology Sharnbasva University India Department of Electronic and Communication Engineering Faculty of Engineering and Technology Sharnbasva University Karanataka Kalburgi India

This research work focuses on food recognition, especially, the identification of the ingredients from food images. Here, the developed model includes two stages namely: 1) feature extraction;2) classification. Initially, the image features and textual features will be extracted, where image features like SIFT and improved CNN-based deep features, textural features are extracted. Then, the hybrid classifier is used for the identification of food ingredients that combines the models like neural network (NN) as well as long short-term memory (LSTM). In order to make the accurate results, the weights of NN and LSTM are fine-tuned via the Chebyshev map evaluated teamwork optimisation (CME-TWO) algorithm. At the final stage, the primacy of the offered scheme is proven concerning varied metrics. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Feature extraction

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Evolutionary recurrent neural network based on equilibrium optimization method for cloud-edge resource management in internet of things

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Neural Computing and Applications 2025年第6期37卷 4957-4969页

作者： Ebrahimi Mood, Sepehr Rouhbakhsh, Adel Souri, Alireza Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China

Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most significant being resource management within the cloud environment. Specifically, the allocation of physical machines (PMs) to virtual machines (VMs) in the Infrastructure as a service (IaaS) layer aims to achieve optimal efficiency. To address this challenge, we propose an evolutionary recurrent neural network (RNN) for resource allocation, enhanced by the equilibrium optimization (EO) algorithm. This method improves the generalization performance and reduces the architecture complexity of RNNs, while ensuring better adaptability to various demand patterns in cloud environments. Additionally, the effectiveness of RNNs heavily relies on the quality of training data;hence, we generate appropriate training data to facilitate the development of an efficient resource management model. Through extensive simulations, our method demonstrates superior performance compared to traditional algorithms. However, it is worth noting that this study is primarily based on simulation results, highlighting the need for real-world validation to confirm its practical applicability. The proposed method's applicability to different cloud computing environments and scenarios, including various service models (IaaS, PaaS, and SaaS) and deployment models (public, private, and hybrid), is also briefly discussed. Future studies should explore the computational efficiency and potential optimization techniques of the EO-RNN approach and consider real-world implementations to validate its effectiveness and uncover practical challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cloud platforms

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Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

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computer Modeling in engineering & Sciences 2024年第3期138卷 2103-2127页

作者： Othman S.Al-Heety Zahriladha Zakaria Ahmed Abu-Khadrah Mahamod Ismail Sarmad Nozad Mahmood Mohammed Mudhafar Shakir Hussein Alsariera Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka(UTeM)Durian TunggalMalaysia College of Computing&Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Electrical Electronic and System EngineeringUniversiti Kebangsaan MalaysiaUKM BangiSelangorMalaysia Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkukIraq Faculty of Engineering and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Batu PahatMalaysia 6Research Center DepartmentThe University of MashreqBaghdadIraq

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the *** this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things *** framework integrates Kalman filtering and *** smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction *** traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction *** evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art ***,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

关键词： Q-learning intelligent transportation system(ITS) traffic control vehicular communication kalman filtering smart city Internet of Things

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Enhancing biometric authentication security through the novel integration of graph theory encryption and chaotic logistic mapping

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Multimedia Tools and Applications 2025年第16期84卷 16909-16943页

作者： El-Shafai, Walid El-Mesady, A. Kamal, F.M. Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Physics and Engineering Mathematics Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University PO 35516 Mansoura Egypt

The escalating reliance on biometric systems for identity verification underscores the imperative for robust data protection mechanisms. Biometric authentication, leveraging unique biological and behavioral characteristics, offers unparalleled precision in individual identification. However, the integrity and confidentiality of biometric data remain paramount concerns, given its susceptibility to compromise. This research delineates the development and implementation of an innovative framework for cancellable biometrics, focusing on facial and fingerprint recognition. This study introduces a novel cancellable biometrics framework that integrates graph theory encryption with three-dimensional chaotic logistic mapping. The methodology encompasses a multifaceted approach: initially employing graph theory for the secure and efficient encryption of biometric data, subsequently enhanced by the complexity and unpredictability of three-dimensional chaotic logistic mapping. This dual-layered strategy ensures the robustness of the encryption, thereby significantly elevating the security of biometric data against unauthorized access and potential compromise. Thus, the resulting cancellable biometrics, characterized by the ability to transform biometric data into an adjustable representation, addresses critical challenges in biometric security. It allows for the revocation and reissuance of biometric credentials, thereby safeguarding the original biometric characteristics of individuals. This feature not only enhances user privacy and data security but also introduces a dynamic aspect to biometric authentication, facilitating adaptability across diverse systems and applications. Preliminary evaluations of the proposed framework demonstrate a marked improvement in the security of face and fingerprint recognition systems. Through the application of graph theory encryption, coupled with three-dimensional chaotic logistic mapping, our framework mitigates the risks associated with t

关键词： Cryptography

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Secure speech coding using chaotic shift keying for encryption combined with error recovery

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Multimedia Tools and Applications 2025年第7期84卷 3893-3927页

作者： Boumaraf, Messaouda Merazka, Fatiha LISIC Laboratory Telecommunications Department Electronic and Computer Engineering Faculty USTHB University Algiers Algeria LISIC Laboratory Telecommunications Department Electrical Engineering Faculty USTHB University Algiers Algeria

This paper presents a speech encryption algorithm utilizing chaotic shift keying alongside error recovery techniques for the G.722.2 codec. The algorithm employs chaotic data interleaving on speech inter-frames to replace continuous packet loss with isolated losses, thus restoring missing data packets. Codec parameters undergo encryption through the combination of multiple chaotic maps applied to intra sub-frames of speech, facilitating confusion and diffusion processes. Enhanced security is achieved through the utilization of multiple chaotic maps, including chaotic shift keying generators applied to codec sub-frame parameters. Specifically, the chaotic shift keying mechanism is employed using four chaotic maps: a Henon map for xn and yn, a logistic map for zn, a tent map for wn, and a cubic map for un. The keys derived from these maps are utilized for chaotic data interleaving on both inter-frames and intra sub-frames of the speech file to shuffle frame parameters (confusion) and scramble sub-frame parameters (diffusion). Additionally, the proposed system enhances speech quality by mitigating degradation caused by packet loss through a waveform substitution-based recovery method, which replaces lost packets with previously received copies. A series of experiments, including waveform representation, spectrogram analysis, Enhanced Modified Bark Spectral Distortion (EMBSD) test, WideBand Perceptual Evaluation of Speech Quality (WB-PESQ) test, MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) score, Mean Opinion Score (MOS) score, correlation test, entropy test, Number of Samples Change Rate (NSCR) test, Unified Average Modified Intensity (UACI) test, key space analysis, and sensitivity test, are conducted to evaluate the security and speech quality of the proposed system. The results validate the system’s security and confirm its ability to maintain good audio quality even under packet loss rates ranging from 5% to 20%. © The Author(s), under exclusive licen

关键词： Cryptography

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A comprehensive analysis of real-time video anomaly detection methods for human and vehicular movement

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Multimedia Tools and Applications 2025年第10期84卷 7519-7564页

作者： Pathirannahalage, Iroshan Jayasooriya, Vidura Samarabandu, Jagath Subasinghe, Akila Department of Electrical and Electronic Engineering Faculty of Engineering University of Sri Jayewardenepura Gangodawila Nugegoda10250 Sri Lanka Department of Electrical and Computer Engineering Faculty of Engineering Western University LondonONN6A 3K7 Canada

The ubiquitous integration of computer vision technologies has revolutionised various application domains, ranging from agriculture and transportation to healthcare and the military. In this context, real-time anomaly detection in video streams has emerged as a critical facet, particularly concerning the movement of vehicles and humans. Despite the increasing adoption of these technologies in real-life applications, a conspicuous research gap exists, the necessity for robust validation of benchmarked datasets and state-of-the-art methods before deployment in real-time settings. This paper addresses this gap through a holistic approach to extensively review the real-time video anomaly detection literature in Human and Vehicular Movement applications. Specifically, we examine the various architectures used for real-time video anomaly detection and provide a detailed analysis of their performance on benchmarked datasets. Furthermore, we delve into the multifaceted challenges encountered when identifying anomalies across different categories, encompassing issues related to computational resources, noise interference, frame complexity, data sparsity, diversity, ethical considerations and their combinations. We address these challenges by proposing potential future trends in anomaly detection, including the development of more sophisticated datasets, exploration of hybrid architectural models, integration of multi-camera systems, advancement in open-set action recognition algorithms, model development for cutting-edge process automation systems, harnessing the potential of IoT, leveraging edge computing, and incorporating drone technologies into anomaly detection strategies. This paper aims to illuminate the direction of real-time anomaly detection for future research in vehicular and human movement. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Anomaly detection

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Modified SPICE-Compatible Model Integrating NBTI and Self-Heating Effects for VDMOS Transistors 11

Modified SPICE-Compatible Model Integrating NBTI and Self-He...

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11th International Conference on Electrical, electronic and Computing engineering, IcETRAN 2024

作者： Marjanović, Milos Veljković, Sandra Mitrović, Nikola Živanović, Emilija Gavrić, Aleksandar Danković, Danijel Faculty of Electronic Engineering Department of Microelectronics Niš Serbia Faculty of Electronic Engineering Department of Computer Science Niš Serbia

ISBN: (纸本)9798350386998

This paper describes a modified SPICE-compatible VDMOS transistor model that includes NBT and self-heating effects. A complete circuit diagram of the transistor is given, which includes the electrical part of the circuit that models the NBT effect and the equivalent thermal part of the circuit that models the self-heating effect. The parameters of the model were determined based on the technical documentation and the fitting method based on the performed experiments. Fresh transistors and NBT-stressed transistors, with and without a heatsink in the load-driving circuit, were considered. The results of experiments and simulations correspond to each other with a maximum deviation of up to 10%. The proposed model can be used in the design and analysis of transistor reliability parameters. © 2024 IEEE.

关键词： SPICE

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