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16th International Conference on security of Information and Networks, SIN 2023

作者： Sharma, Aditi Patel, Mukta Choudhary, Arjun Kumar, Gajender Department of Computer Science and Engineering Maharashtra Pune India Parul Institute of Technology Parul University Department of Computer Science and Engineering Gujarat Vadodara India Sardar Patel University of Police Center for Cyber Security Security and Criminal Justice Jodhpur India Srm University Department of Computer Science and Engineering Delhi-NCR Campus India

ISBN: (纸本)9798350343212

Energy consumption is highly demanded area for major security concerns due to the high eye from attackers. In 2020, the 'Recorded future' released report for massive power outage in India[1]. With the increasing adoption of renewable power sector and increasing demand for efficient energy management, the need for secure and transparent solutions in the energy sector has become paramount. This research introduces an innovative solution to tackle these difficulties by creating a Blockchain-Based security Enabled Smart Contract (BSESC) system for energy management. The BSESC system uses blockchain technology to provide a secure and decentralised platform for the management of energy resources. It guarantees the honesty and openness of energy transactions while reducing the hazards linked to centralised energy management systems. Smart contracts, implemented on the blockchain, provide automatic and tamper-resistant execution of energy agreements among producers, consumers, and grid operators. The BSESC system offers real-time monitoring and management of energy production, distribution, and consumption as its main characteristics. This research paper focuses on the design and management of spread power production with customers in the domestic, corporate, and manufacturing sectors. The energy demand side management is initially proposed using a hierarchical structure based on P2P information and energy exchange in a dynamic economy. The smooth and efficient trading system is created using smart contracts supported by blockchain. The advantages of distributed energy management are discussed, including cost savings, reduced peak demand, and better market efficiency, all of which are facilitated by blockchain. © 2023 IEEE.

关键词： Smart contract

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Toward Correlated Sequential Rules

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5340-5351页

作者： Chen, Lili Gan, Wensheng Chen, Chien-Ming Tongji University Department of Computer Science and Technology Shanghai200082 China Jinan University College of Cyber Security Guangzhou510632 China Pazhou Laboratory Guangzhou510330 China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing210044 China

The goal of high-utility sequential pattern mining (HUSPM) is to efficiently discover profitable or useful sequential patterns in a large number of sequences. However, simply being aware of utility-eligible patterns is insufficient for making predictions. To compensate for this deficiency, high-utility sequential rule mining (HUSRM) is designed to explore the confidence or probability of predicting the occurrence of consequence sequential patterns based on the appearance of premise sequential patterns. It has numerous applications, such as product recommendation and weather prediction. However, the existing algorithm, known as HUSRM, is limited to extracting all eligible rules while neglecting the correlation between the generated sequential rules. To address this issue, we propose a novel algorithm called correlated high-utility sequential rule miner (CoUSR) to integrate the concept of correlation into HUSRM. The proposed algorithm requires not only that each rule be correlated but also that the patterns in the antecedent and consequent of the high-utility sequential rule be correlated. The algorithm adopts a utility-list structure to avoid multiple database scans. Additionally, several pruning strategies are used to improve the algorithm's efficiency and performance. Based on several real-world datasets, subsequent experiments demonstrated that CoUSR is effective and efficient in terms of operation time and memory consumption. All codes are accessible on GitHub: https://***/DSI-Lab1/CoUSR. © 2020 IEEE.

关键词： Forecasting

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A Novel Deep Learning Framework for Contraband Items Recognition in Smart City Applications 2nd

A Novel Deep Learning Framework for Contraband Items Recogni...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Khalid, Shehzad Ahmed, Zubair Noor, Ayesha Sedik, Ahmed Asif, Waqar Department of Computer Engineering Bahria University Islamabad Pakistan Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh Saudi Arabia Computer Science and Cyber Security University of West London London United Kingdom

ISBN: (纸本)9789819783441

Ensuring safety and security is paramount in today’s complex environment, and the effective detection of contraband items plays a pivotal role in achieving this objective. Contraband items, ranging from illegal substances to unauthorized goods, pose a threat to public safety, security, and the overall well-being of smart city inhabitants. Such items are currently detected by human operator reviewing the images from X-ray baggage scanners. However, manual detection of contraband items is inherently challenging and time-consuming resulting in significant delays at crowded places such as airports, train-stations, shopping malls etc. Moreover, there is a significant risk of overlooking certain items that could pose potential harm. To address these challenges, there is a growing demand for intelligent systems for contraband items detection that can efficiently and accurately detect items whilst minimizing false negatives. Automated deep learning solutions offer a sophisticated and technologically advanced approach to enhance the accuracy and speed of the detection process. In our pursuit to address this challenge comprehensively, we have obtained an X-ray Imaging Dataset specifically curated for this purpose. The dataset includes five types of objects including guns, knives, pliers, scissors, and wrenches that are typically banned to carry along. In this paper, we have proposed a deep learning-based approach to efficiently and accurately detect contraband items from X-ray images. The proposed approach is based on YOLO architectures that has been shown to perform better for object detection in variety of domains both in terms of accuracy and real-time performance. We have evaluated different versions of YOLO to select the version that works best for contraband item detection from X-ray images. Yolo-v8 has shown superior performance followed by Yolo-v5 in terms of accuracy. Challenges regarding class imbalance have been addressed using data augmentation especially for clas

关键词： Deep learning

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Deep-Based Hybrid Model For Enhancing Land Region Prediction Outcomes 2

Deep-Based Hybrid Model For Enhancing Land Region Prediction...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Selvapandian, D. Raja, Laxmi Tharageswari, K. Vm, Senthilkumar Jc, Nikitha Hemalatha, M. Karpagam Academy of Higher Education Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Faculty of Engineering Department of Cyber Security Coimbatore India Mahendra Institute of Technology Department of Information Technology Namakkal India Nandha Arts and Science College Department of Information Technology Erode India

ISBN: (纸本)9798331534950

Although satellite data is not a novel approach to mapping wetlands, its accuracy depends on data quality and categorization algorithms, making it a continuous challenge. The increasing accessibility of satellite data and the development of data modelling technologies, such as big data modelling using machine learning (ML) algorithms, have improved the accuracy of this type of mapping. Our work demonstrated a simple, scalable, and reliable wetland classification using linear Support Vector Machine (l-SVM) and unsupervised k-Nearest Neighbor (k-NN) machine learning approaches. Normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), the two primary inputs were modelled using Landsat optical data. Subsequently, l-SVM was used to supervise k-NN data using training data collected from outside observations. The accuracy test results indicate that there is a considerable reduction in error for both optical indices, and all prediction models have an accuracy of about 99%. © 2024 IEEE.

关键词： Data accuracy

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Speech-to-text Emotion Detection System using SVM, CNN, and BERT 1

Speech-to-text Emotion Detection System using SVM, CNN, and ...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Yadav, Uma Bondre, Shweta Thakre, Bhakti Likhar, Kiran Shri Ramdeobaba College of Engineering and Management Nagpur India Shri Ramdeobaba College of Engineering and Management Information Technology Department Nagpur India St. Vincent Pallotti College of Engineering and Technology Cyber Security Department Nagpur India Computer Science & Engineering G H Raisoni Institute of Engineering and Technology Nagpur India

ISBN: (纸本)9798350377002

Emotions have a tremendous impact on human experiences and decision-making processes. People are frequently motivated to repeat happy behaviors while avoiding depressing ones. Emotions may spread like wildfire in the modern digital era due to the quick broadcast of information and the heavy use of emotive language in communication. The identification of these emotional undercurrents in a variety of literary forms has been made easier by the analysis of indigenous languages, allowing for prompt interventions when needed. This study presents an algorithm that can analyze statements and then categorize them into sentiments in order to determine a person's emotional state. An analysis of the algorithm's performance shows that BERT performs better than SVM and CNN in a variety of emotional categories, showing better recall, accuracy, and F1-scores in the identification of emotions including Joy, Sadness, Fear, and Anger. For example, BERT outperforms SVM and CNN by large margins, achieving an average F1-score of 0.85 across all emotions. CNN performs competitively, especially when it comes to identifying emotions such as Fear and Sadness, but SVM performs somewhat worse overall than BERT and CNN. Interestingly, all models are good at recognizing Neutral emotions, with BERT and CNN doing somewhat better than SVM. As a result, BERT stands out as the best model for classifying emotions, demonstrating its effectiveness in identifying complex emotional states. © 2024 IEEE.

关键词： Convolutional neural networks

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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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Terahertz metasurface biosensor for high-sensitivity salinity detection and data encoding with machine learning optimization based on random forest regression

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Optical and Quantum Electronics 2024年第11期56卷 1-39页

作者： Wekalao, Jacob Mandela, Ngaira Department of Optics and Optical Engineering University of Science and Technology of China Hefei230026 China School of Digital Forensics and Cyber Security National Forensic Sciences University Gujarat Gandhinagar382007 India

This research presents a terahertz-based biosensor for high-precision salinity detection, employing a synergistic integration of graphene, gold, and silver in a metasurface configuration. The sensor exhibits exceptional performance characteristics within the 0.6–2 THz frequency range, demonstrating a maximum sensitivity of 571 GHzRIU−1, quality factor of up to 6.107, and a figure of merit of 2.189 RIU−1. Electromagnetic field distribution analysis demonstrates enhanced light-matter interactions, with peak performance observed at 1.6 THz. The investigation further explored the sensor's potential for multilevel data encoding, illustrating its versatility beyond conventional detection methods. Simulation results indicate the efficiency of the proposed sensor which surpasses some of the previous existing designs in terms of sensitivity among other performance parameters. A machine learning approach using random forest regression was employed to predict sensor responses, achieving coefficient of determination (R2) values ranging from 0.88 to 1.00 across multiple parametric studies. This research presents a promising platform for high-precision salinity sensing with potential applications in environmental monitoring, desalination processes, and aquaculture industries. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Aquaculture

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rafPS:A shapley-based visual analytics approach to interpret traffic

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Computational Visual Media 2024年第6期10卷 1101-1119页

作者： Zezheng Feng Yifan Jiang Hongjun Wang Zipei Fan Yuxin Ma Shuang-Hua Yang Huamin Qu Xuan Song Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 18055China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong KongChina Department of Computer Science University of ReadingBerkshire RG66AHUK Center for Spatial Information Science the University of TokyoTokyo 113-00331Japan Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and TechnologyShenzhen 518055China

Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic *** predictions are beneficial for understanding the situation and making traffic control ***,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end *** previous studies attempted to“open the black box”and increase the interpretability of generated ***,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain *** overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban *** measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different *** on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow *** real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

关键词： data visualization model interpretation urban planning urban visual analytics

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Vehicular Intrusion Detection System Based on Hybrid Quantum Neural Networks

Vehicular Intrusion Detection System Based on Hybrid Quantum...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhou, Jianhua Liu, Yueheng Li, Sifan Cao, Yue Chen, Wenkai Ahmed, Naveed Wuhan University School of Cyber Science and Engineering China Wuhan Cyber Security Association China Technology Co. Ltd China Prince Sultan University College of Computer and Information Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798350351255

Traditional Deep Learning (DL) method is increasingly used in vehicular Intrusion Detection Systems (IDSs). However, there are still some limitations. It combines various models, resulting in a massive model size and numerous parameters, requiring more computational resources. Furthermore, real-time performance is crucial in vehicular IDS. Large models typically require more time for detection, failing to meet the timeliness requirements. Quantum computing offers parallel computing capabilities, prompting our proposal of a Hybrid Quantum Neural Network (HQNN)-based IDS to improve the timeliness of intrusion detection. This IDS employs HQNN to enhance feature extraction and speed up traditional convolution. It retains most of the structure of classical Convolutional Neural Network (CNN), comprising attention layers, batch normalization layers, and quantum circuits. Through quantum superposition and entanglement, certain complex non-linear functions are compressed. Furthermore, residual connections enable seamless gradient flow during back propagation. This accelerates computation and expedites convergence. Comparative experiments on the Car-hacking dataset show improved detection time and convergence speed, with exceptionally high accuracy in practical applications. © 2024 IEEE.

关键词： Network intrusion

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Enhancing Anomaly Detection in Smart Water Metering Networks with LSTM-Autoencoder and Data Augmentation Techniques 4

Enhancing Anomaly Detection in Smart Water Metering Networks...

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4th International Multidisciplinary Information technology and engineering Conference, IMITEC 2024

作者： Nelago Kanyama, Maria Bhunu Shava, Fungai Gamundani, Attlee M Hartmann, Andreas Namibia University of Science and Technology Department of Computer Science Windhoek Namibia Namibia University of Science and Technology Department of Cyber Security Windhoek Namibia Hydrology Institute of Groundwater Management Dresden Germany

ISBN: (纸本)9798350387988

Machine Learning (ML) based anomaly detection in Smart Water Metering Networks (SWMNs) and traditional water metering networks faces significant challenges, including small dataset sizes, unlabeled data, and class imbalance. These issues often lead to high false detection rates, model overfitting, and increased model complexity. This paper aims to enhance anomaly detection in SWMNs using a ML-based Long Short-Term Memory Autoencoder (LSTM-AE) augmented with data augmentation techniques. The dataset, collected from Location A in Windhoek, Namibia, comprised of only 72 monthly water consumption data points per household, which is insufficient for effectively training ML models. To address this limitation, we employed two interpolation-based data augmentation techniques: Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) and Cubic Spline, to generate synthetic data instances to expand the dataset. These techniques were selected based on the patterns and trends of water consumption data and were compared for their accuracy. The original and augmented datasets were then used to train the LSTM-AE model. The results demonstrate that, despite the limited data available in the water sector, data augmentation techniques can significantly enrich datasets, leading to substantial performance improvement with augmented data. The proposed approach greatly enhanced the accuracy of the LSTM-AE model in reconstructing data from small datasets. This study underscores the potential of data augmentation to overcome data scarcity in the water sector and enhance anomaly detection in SWMNs. © 2024 IEEE.

关键词： Interpolation

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