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A New Multi Chaos-Based Compression Sensing Image Encryption

computers, Materials & Continua 2023年第7期76卷 437-453页

作者： Fadia Ali Khan Jameel Ahmed Suliman A.Alsuhibany Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia

The advancements in technology have substantially grown the size of image *** image encryption algorithms have limited capabilities to deal with the emerging challenges in big data,including compression and noise *** image encryption method that is based on chaotic maps and orthogonal matrix is proposed in this *** proposed scheme is built on the intriguing characteristics of an orthogonal *** Schmidt disperses the values of pixels in a plaintext image by generating a random orthogonal matrix using logistic chaotic *** the diffusion process,a block-wise random permutation of the data is performed using *** proposed scheme provides sufficient security and resilience to JPEG compression and channel noise through a series of experiments and security *** enables Partial Encryption(PE)for faster processing as well as complete encryption for increased *** higher values of the number of pixels change rates and unified average change intensity confirm the security of the encryption *** contrast to other schemes,the proposed approach can perform full and partial encryption depending on security requirements.

关键词： Chaos compression image encryption

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Impact of oxymoron features and deep learning techniques in the detection of sarcastic contents

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9197-9207页

作者： Seethappan, K. Premalatha, K. Department of Computer Science and Engineering University College of Engineering Tamilnadu Ramanathapuram India Department of Computer Science and Engineering Bannari Amman Institute of Technology Tamilnadu Sathyamangalam India

Even though various features have been investigated in the detection of figurative language, oxymoron features have not been considered in the classification of sarcastic content. The main objective of this work is to present a system that can automatically classify sarcastic phrases in multi-domain data. This multi-domain dataset consisting of 67850 sarcastic and non-sarcastic data is collected from various websites to identify sarcastic or non-sarcastic utterances. Multiple approaches are examined in this work to improve sarcasm identification: 1. A Combination of fasttext embedding, syntactic, semantic, lexical n-gram, and oxymoron features 2. TF-IDF feature weighting scheme 3. Three machine learning algorithms (SVM, Multinomial Naïve Bayes, and Random Forest), three deep learning algorithms (CNN, LSTM, MLP), and one ensemble model (CNN + LSTM) The CNN + LSTM model achieves a Precision of 91.32%, Recall of 92.85%, F-Score of 92.08%, accuracy of 92.01%, and Kappa of 0.84 by combining the fasttext embedding, bigram, syntactic, semantic, and oxymoron features with TF-IDF method. These experimental results show CNN + LSTM with a combination of all features outperforms the other algorithms in classifying the sarcasm in both datasets. The sarcasm classification performance of our dataset and another sarcasm news dataset was compared while applying the above model. © 2024 - IOS Press. All rights reserved.

关键词： Syntactics

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Semi‑supervised contour‑driven broad learning system for autonomous segmentation of concealed prohibited baggage items

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 1-18页

作者： Divya Velayudhan Abdelfatah Ahmed Taimur Hassan Muhammad Owais Neha Gour Mohammed Bennamoun Ernesto Damiani Naoufel Werghi Department of Electrical Engineering and Computer Sciences Center for Cyber‑Physical SystemsKhalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates Department of Electrical Computer and Biomedical EngineeringAbu Dhabi UniversityAbu Dhabi 59911United Arab Emirates Department of Computer Science and Software Engineering the University of Western AustraliaPerthWA 6009Australia

With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger *** address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation ***,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset ***,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as *** research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage *** proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage *** specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage *** multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign *** contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation *** proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,***,the lim

关键词： Baggage X-ray imagery Broad learning systems Threat detection Threat segmentation

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Supervised deep learning for content-aware image retargeting with Fourier Convolutions

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Multimedia Tools and Applications 2024年第36期83卷 83611-83627页

作者： Givkashi, MohammadHossein Naderi, MohammadReza Karimi, Nader Shirani, Shahram Samavi, Shadrokh Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Department of Electrical and Computer Engineering McMaster University HamiltonONL8S 4L8 Canada Computer Science Department Seattle University SeattleWA98122 United States

Image retargeting aims to alter the size of the image with attention to the contents. One of the main obstacles to training deep learning models for image retargeting is the need for a vast labeled dataset. Labeled datasets are unavailable for training deep learning models in the image retargeting tasks. As a result, we present a new supervised approach for training deep learning models. We use the original images as ground truth and create inputs for the model by resizing and cropping the original images. A second challenge is generating different image sizes in inference time. However, normal convolutional neural networks cannot generate images of different sizes than the input image. To address this issue, we introduced a new method for supervised learning. In our approach, a mask is generated to show the desired size and location of the object. Then the mask and the input image are fed to the network. Comparing image retargeting methods and our proposed method demonstrates the model’s ability to produce high-quality retargeted images. Afterward, we compute the image quality assessment score for each output image based on different techniques and illustrate the effectiveness of our approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolution

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Enhanced DDoS Detection Using Advanced Machine Learning and Ensemble Techniques in Software Defined Networking

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computers, Materials & Continua 2024年第11期81卷 3003-3031页

作者： Hira Akhtar Butt Khoula Said Al Harthy Mumtaz Ali Shah Mudassar Hussain Rashid Amin Mujeeb Ur Rehman Department of Computer Science University of Management and TechnologySialkot51040Pakistan Department of Computer Science and Creative Technologies Global College of Engineering and TechnologyMuscat2546Sultanate of Oman Department of Computer Science University of WahWah Cantt47040Pakistan Department of Computer Science and IT University of ChakwalChakwal48800Pakistan

Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern *** detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)*** Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent *** this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN *** model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant *** adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack *** proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble *** proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in *** provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving *** comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing *** results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.

关键词： Table 1(continued)OSI layer Possible DDoS attack Data link MAC Address Flooding Physical Cable disconnection JaMMING physical impersonation

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Enhancing energy efficiency: a protocol assessment in multi-hop mesh-based IOUT networks

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Multimedia Tools and Applications 2024年第37期83卷 84999-85026页

作者： Sisodia, Ankur Vishnoi, Swati Upadhyay, Sachin Goswami, Jayati Krishna Khenwar, Medha Yadav, Ajay Kumar Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science and Engineering GL Bajaj Group of Institutions Mathura India Department of Computer Science Banasthali Vidyapith Rajasthan Jaipur India

IOUT (Internet of Underwater Things) relies on underwater acoustic sensors, which have limited resources such as battery power and bandwidth. The exchange of data among these sensors faces challenges like propagation delay, node displacement, and environmental errors, making network maintenance difficult. The objective of this study is to address the energy efficiency and performance issues in IOUT networks by proposing and evaluating an energy-efficient routing protocol called Efficient Cost Wakeup Routing Protocol (ECWRP). To achieve the objective, the study focuses on two key parameters: Cost and Duty Cycle. The Duty Cycle parameter helps in reducing undesirable impacts during underwater communications, improving the performance of the routing protocol. The Cost parameter is utilized to select the most efficient path for data transmission, considering factors such as transmitting power levels. The protocol is applied to a multi-hop mesh-based network. The proposed ECWRP routing protocol is assessed through simulations, demonstrating its superior efficiency compared to the Ride algorithm. By eliminating unnecessary handshaking and optimizing route selection, ECWRP significantly enhances energy efficiency and overall performance within the IoUT network. The study's findings on the enhanced energy efficiency and performance improvements achieved by the ECWRP protocol hold promising implications for the design and optimization of IoUT networks, paving the way for more sustainable and effective communication systems in underwater environments. In conclusion, the study demonstrates the effectiveness of the Efficient Cost Wakeup Routing Protocol (ECWRP) in enhancing energy efficiency and performance in multi-hop mesh-based IoUT networks. The protocol's utilization of the Duty Cycle parameter reduces undesirable impacts, while the Cost parameter enables the selection of the most efficient path for data transmission. The results confirm the superiority of the ECWRP protoc

关键词： Energy efficiency

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Efficient Algorithms for Attributed Graph Alignment With Vanishing Edge Correlation

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IEEE Transactions on Information Theory 2025年第6期71卷 4556-4580页

作者： Wang, Ziao Wang, Weina Wang, Lele The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada University of Michigan Department of Electrical and Computer Engineering Ann ArborMI48109 United States Carnegie Mellon University Computer Science Department PittsburghPA15213 United States

Graph alignment refers to the task of finding the vertex correspondence between two correlated graphs of n vertices. Extensive study has been done on polynomial time algorithms for the graph alignment problem under the Erdős–Rényi graph pair model, where the two graphs are Erdős–Rényi graphs with edge probability qu , correlated under certain vertex correspondence. To achieve exact recovery of the correspondence, all existing algorithms at least require the edge correlation coefficient ρu between the two graphs to be non-vanishing as n → ∞. Moreover, it is conjectured that no polynomial-time algorithm can achieve exact recovery under vanishing edge correlation ρu u ≥ n−Θ(1). We identify a local tree structure, which incorporates one layer of user information and one layer of attribute information, and apply the subgraph counting technique to such structures. A polynomial-time algorithm is proposed that recovers the vertex correspondence for most of the vertices, and then refines the output to achieve exact recovery. The consideration of attribute information is motivated by real-world applications like LinkedIn and Twitter, where user attributes like birthplace and education background can aid alignment. © 1963-2012 IEEE.

关键词： Graph algorithms

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Comparison of deep learning algorithms for site detection of false data injection attacks in smart grids

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Energy Informatics 2024年第1期7卷 71页

作者： Nasir, Qassim Abu Talib, Manar Arshad, Muhammad Arbab Ishak, Tracy Berrim, Romaissa Alsaid, Basma Badway, Youssef Abu Waraga, Omnia Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Department of Computer Science Iowa State University AmesIA United States Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

False Data Injection Attacks (FDIA) pose a significant threat to the stability of smart grids. Traditional Bad Data Detection (BDD) algorithms, deployed to remove low-quality data, can easily be bypassed by these attacks which require minimal knowledge about the parameters of the power bus systems. This makes it essential to develop defence approaches that are generic and scalable to all types of power systems. Deep learning algorithms provide state-of-the-art detection for FDIA while requiring no knowledge about system parameters. However, there are very few works in the literature that evaluate these models for FDIA detection at the level of an individual node in the power system. In this paper, we compare several recent deep learning-based model that proven their high performance and accuracy in detecting the exact location of the attack node, which are convolutional neural networks (CNN), Long Short-Term Memory (LSTM), attention-based bidirectional LSTM, and hybrid models. We, then, compare their performance with baseline multi-layer perceptron (MLP)., All the models are evaluated on IEEE-14 and IEEE-118 bus systems in terms of row accuracy (RACC), computational time, and memory space required for training the deep learning model. Each model was further investigated through a manual grid search to determine the optimal architecture of the deep learning model, including the number of layers and neurons in each layer. Based on the results, CNN model exhibited consistently high performance in very short training time. LSTM achieved the second highest accuracy;however, it had required an averagely higher training time. The attention-based LSTM model achieved a high accuracy of 94.53 during hyperparameter tuning, while the CNN model achieved a moderately lower accuracy with only one-fourth of the training time. Finally, the performance of each model was quantified on different variants of the dataset—which varied in their l2-norm. Based on the results, LSTM, CNN obta

关键词： Smart power grids

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Microgrids with day-ahead energy forecasting for efficient energy management in smart grids: hybrid CS-RERNN

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Australian Journal of electrical and Electronics engineering 2024年第3期21卷 213-227页

作者： Shirley, C.P. Pattar, Jagannath Kavitha Rani, P. Saini, Sumit Ranga, Jarabala Elangovan, D. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Mechanical Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Electrical Engineering School of Engineering & Technology Central University of Haryana Mahendergarh India Detartment of Electrical and Electronics Engineering Ramachandra College of Engineering Eluru India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi institute of technology Hyderabad India

By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and lowering energy bills. In this manuscript, a hybrid approach is proposed for smart grid home energy management with microgrids and day-ahead energy forecasts. The proposed control approach combines the Circle Search (CS) algorithm and Recalling-Enhanced Recurrent Neural Network (RERNN). Commonly it is named as CS-RERNN technique. The novelty of this paper is to optimise energy consumption and production within microgrids, thereby contributing to the overall efficiency of the smart grid system. The proposed method is used to reduce the electricity cost, peak-to-average ratio (PAR), and maximising consumer comfort. Energy management is performed based on the CS algorithm. A smart home connected to the external power grid (PG) is managed by the proposed method. Here, load demand is predicted by using RERNN. By then, the performance of the proposed method is implemented in MATLAB platform. The proposed method shows a high efficiency of 96%, and 22 $ of low electricity bill cost compared with other existing methods such as Particle swarm optimisation (PSO), Cuckoo search algorithm (CSA, and Border collie Optimisation (BCO). ©, Engineers Australia.

关键词： Energy utilization

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