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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Palagati, Anusha Balan, Santhosh Kumar Arun Joe Babulo, S. Raja, Laxmi Natarajan, K.K. Kalimuthu, Rajkumar Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications K.S.R College of Arts and Science for Women Tamilnadu Tiruchengode India Department of Cyber Security Karpagam Academy of Higher Education Tamilnadu Coimbatore India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Oman Department of Cyber Security Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India

ISBN: (纸本)9798331510299

cyberbullying is defined as sharing of undesirable material in the cyberspace, primarily- the use of the social sites that instigate hostility and create feelings of hatred among the individuals. Another increasing threat due to increase use of internet-based communication is cyberbullying especially on the site such as Twitter. Different ML and NLP approaches including support vector machine (SVM), Naïve bayes, Random Forest, and logistic regression is employed to detect cyberbul Bullivating. These methods for supervised learning mine patterns in text and image, allowing computers to promptly flag risky interactions. Some of these approaches also utilizes OCR for the identification of image-based bullying. Consequently, the objective of this research is to identify the correct algorithm and dataset for improving the identification of cyberbullying and solving the problem that manifests itself in the steady increase in cases of this type of bullying. © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient Convolutional Neural Network Based Side Channel Attacks Based on AES Cryptography 21

Efficient Convolutional Neural Network Based Side Channel At...

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21st IEEE Student Conference on Research and Development, SCOReD 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Aman, Azana Hafizah Abdulkadir, Rabiu Aliyu Islam, Shayla Farhan, Basim Abdulkareem Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Department of Computer Techniques Engineering Baghdad10011 Iraq Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350318821

In recent years, profiled side-channel attacks have emerged as a particularly potent kind of side-channel attack that can circumvent cryptographic equipment's security. Convolutional neural networks (CNNs) have been widely used as deep learning infrastructure for attacks in recent research that has examined a new kind of profiled attack based on deep learning. The design of CNNs will have a significant impact on attack effectiveness. However, image recognition fields are frequently the foundation of the CNN architecture currently used for profiled attacks. Additionally, it is still challenging to choose the proper parameters and CNN infrastructure concerning adaptation to profiled assault types. In the current study, an effective CNN-based profiled attack was suggested that can be used against masking-protected cryptographic devices. The Grey Wolf Optimization (GWO) approach obtains the CNN architecture parameters proposed in this research. The characteristics of intriguing areas on the power trace determine these parameters. The proposed attacks were tested using experiments over a tracing set gathered from an intelligent card Atmega8515 processing the ASCAD public dataset. © 2023 IEEE.

关键词： Side channel attack

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A stacked ensemble approach to detect cyber attacks based on feature selection techniques

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 316-331页

作者： Urmi, Wahida Ferdose Uddin, Mohammed Nasir Uddin, Md Ashraf Talukder, Md. Alamin Hasan, Md. Rahat Paul, Souvik Chanda, Moumita Ayoade, John Khraisat, Ansam Hossen, Rakib Imran, Faisal Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia School of Information Technology Crown Institute of Higher Education 116 Pacific Highway North SydneyNSW2060 Australia Department of Cyber Security Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakoir Gazipur1750 Bangladesh Department of Electrical and Computer Engineering University of Ottawa Canada

The exponential growth of data and increased reliance on interconnected systems have heightened the need for robust network security. cyber-Attack Detection Systems (CADS) are essential for identifying and mitigating threats through network traffic analysis. However, the effectiveness of CADS is highly dependent on selecting pertinent features. This research evaluates the impact of three feature selection techniques—Recursive Feature Elimination (RFE), Mutual Information (MI), and Lasso Feature Selection (LFS)—on CADS performance. We propose a novel stacked ensemble classification approach, combining Random Forest, XGBoost, and Extra-Trees classifiers with a Logistic Regression meta-model. Performance is assessed using CICIDS2017 and NSL-KDD datasets. Results show that RFE achieves 100% accuracy for Brute Force attacks, 99.99% for Infiltration and Web Attacks on CICIDS2017, and 99.95% accuracy for all attacks on NSL-KDD, marking a significant improvement over traditional methods. This study demonstrates that optimizing feature selection and leveraging diverse classifiers can substantially enhance the accuracy of CADS, providing stronger protection against evolving cyber threats. © 2024 The Authors

关键词： Feature Selection

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Advance Public Bus Transport Management System: An Innovative Smart Bus Concept

Advance Public Bus Transport Management System: An Innovativ...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Patel, Keyurkumar Patel, Divya Chaudhary, Nishit Thakkar, Darsh Savaliya, Pratham Mistry, Armaan Rashtriya Raksha University Artificial Intelligence and Cyber Security Department of IT Gujarat Gandhinagar382305 India Karnavati University Unitedworld School of Computational Intelligence Department of Computer Science and Engineering Gujarat Gandhinagar382422 India

ISBN: (纸本)9798350324136

In the present era, public bus transportation services often lag behind in adopting modern technologies, resulting in numerous operational complexities. These issues encompass challenges such as poor passenger density control, operational inefficiencies, inadequate live tracking, limited real-Time capabilities, suboptimal resource management, route ambiguities, safety concerns, and more. This research paper introduces a comprehensive solution an Advance Public Bus Transport Management System (APBTMS) that aims to address these challenges for the benefit of both passengers and transport management authority. Our proposed system leverages various sensors and IoT (Internet of Things) modules deployed within buses to monitor various onboard activities and physical environmental factors. These enable the making of data-driven decisions in real-Time, Facilitating improved operational control. Key components of the system include GPS (Global Positioning System) tracking for real-Time bus location updates, passenger density/count through CV (computer Vision), and continuous monitoring of physical environmental factors such as temperature, humidity, sound magnitude, and vibration magnitude which facilitate accident/abnormalities detection, Fire detection and helps in continuously monitoring road quality. The application of CV technology, along with passenger count, can detect harm-causing objects inside a bus and can compare the faces of passengers with black-listed passengers (involved in previously reported crimes such as theft, and nuisance). Passengers benefit from these advancements through a dedicated mobile application. A User-friendly website is also offered to public bus transport management authority providing various useful functionalities. With the addition of safety features like real-Time speed detection and many other capabilities in the future, our proposed system can expand its benefits to school bus services and other public transportation services. © 2024

关键词： API (Application Programming Interface) AWS (Amazon Web Services) Cloud Computing CV (computer Vision) GPS (Global Positioning System) GSM (Global System for Mobile communication) IoT (Internet of Things) ML (Machine Learning)

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An Enhanced Product Quality Evaluation using Hybrid LSTM-FCNN Model and GWO Algorithm for Social Media Sentiment Analysis 8

An Enhanced Product Quality Evaluation using Hybrid LSTM-FCN...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Raj Kumar, V.S. Kumaresan, T. Kanna, P. Rajesh Jagadeesan, S. Showmiya, P. Nithin, P. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India Nandha Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Technology Department of Computer Science - Cyber Security Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India

ISBN: (纸本)9798350367904

Sentiment analysis is still in its developing era, and sometimes, struggles are faced in evaluating user sentiment due to the use of traditional models to analyze the relationship structures in social media interactions. To tackle the issue, we proposed an innovative approach Hybrid LSTM neural network and a fully connected neural network (FCNN) integrated with the Grey Wolf Optimization (GWO) algorithm. The objective of this proposed method is to improve sentiment analysis by employing the LSTM to capture the sequence of data and improve the ability of FCNNs to improve feature extraction and the analysis process. The proposed system process initiated with the data preprocessing of input Twitter sentiment dataset transformed into a suitable input to the Hybrid LSTM-FCNN neural network. Then, temporal dependency of the data captured by LSTM and FCNN is used to extract the features. The GWO method mimics the grey wolf hunting behaviour, for optimizing the proposed Hybrid LSTM-FCNN model's parameters to enhance the classification accuracy. The system models start with the initialization of the model, parameters, and training the models using the dataset to reduce the cross-entropy loss, estimating the testing dataset from unseen data and fine-tuning using GWO as necessary. Results demonstrate significant improvements in sentiment categorization compared to traditional methods. The Hybrid LSTM-FCNN model, enhanced with GWO, effectively captures nuanced sentiment patterns, leading to more accurate classification. Additionally, GWO's computational efficiency contributes to scalability. In conclusion, our proposed approach offers a promising solution for enhancing product quality evaluation through advanced neural network architectures and optimization algorithms applied to social media sentiment analysis. © 2024 IEEE.

关键词： Tweets

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Enhancing the Power of Advanced Arnold Cat Map for Secure Quantum Image Cryptography

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SN computer science 2025年第5期6卷 1-14页

作者： Satre, Shilpa M. Joshi, Bharti Department of Computer Engineering Ramrao Adik Institute of Technology D. Y. Patil Deemed to be University Nerul Maharashtra Navi Mumbai 400 706 India Computer Science and Engineering (IOT and Cyber Security With Block Chain Technology) Department Dwarkadas J. Sanghvi College of Engineering Vile Parle (West) Mumbai 400 056 India

Image encryption is critical for maintaining the security and integrity of digital images, as well as preventing illegal access and changes. However, present encryption techniques frequently entail difficult mathematical processes, limiting their efficiency and applicability. Because of their narrow chaotic space, certain low-dimensional chaotic maps can be easily predicted, and encrypted images may be *** study presents a novel quantum image encryption algorithm using the hash 256 algorithm. The input image has been confused using four different chaotic maps and a conventional permutation technique, including the Arnold cat map, chaotic bakers, pixel permutation, and the proposed Arnold cat map. At last, the backward diffusion process and bit-level permutation are taken into account for the scrambled images. The effectiveness of four chaotic maps-based quantum image cryptography is assessed by examining correlation features, histogram analysis, NPCR, UACI, Entropy, and SSIM. According to this analysis, the chaotic bakers and pixel permutation maps are not as effective as the Arnold cat map. The Arnold cat map’s UACI, SSIM, and NPCR values computed from the cameraman and pepper images outperform the pixel permutation and chaotic bakers maps. Thus the proposed model performs well providing the secret key and give large key space which makes brute-force attack impractical and based on the discrete chaotic maps in basic quantum chaotic cryptosystem algorithm and improved and without losing the image information. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Arnold cat map Chaotic bakers map Hash256 Pixel permutation Quantum cryptography

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Automated AI System for Online Phishing Detection and Mitigation

Automated AI System for Online Phishing Detection and Mitiga...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Raj, Daksh Kumar, Rajiv Joshi, Shivani Amrita Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India Galgotias University Department of Computer Scienceand Engineering U.P. Greater Noida India Sharda University Center for Cyber Security and Cryptology Computer Scienceand Engineering Sharda School of Engineeringand Technology Greater Noida India

ISBN: (纸本)9798350378092

This paper presents the critical challenge of phishing attacks by using advanced machine learning (ML) approaches to efficiently identify and mitigate malicious websites. Phishing websites betray users by impersonating legitimate entities to extract sensitive information. This paper emphases on developing and implementing a robust ML model capable of differentiating between legitimate and phishing websites through a comprehensive analysis of URL features, website content, and associated metadata. The proposed approach employs a diverse dataset that includes both phishing and legitimate websites, with features extracted using both traditional approaches and innovative methods. Various ML approaches are explored, such as Decision Trees, Support Vector Machines, Random Forest (RF), and Neural Networks. These approaches are evaluated for accuracy, recall, precision, and F1-score to determine the most effective model. Feature selection approaches are employed to discover the most significant indicators of phishing activity, improving the model's performance and interpretability. The RF Classifier demonstrated superior performance with an accuracy of 96.6%. © 2024 IEEE.

关键词： Phishing

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Empowering Intelligent Home Safety: Indoor Family Fall Detection with YOLOv5

Empowering Intelligent Home Safety: Indoor Family Fall Detec...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on cyber science and Technology Congress, DASC/PiCom/CBDCom/cyberSciTech 2023

作者： Ke, Yaojie Yao, Yinan Xie, Zhengye Xie, Hepeng Lin, Hui Dong, Chen College of Computer and Data Science Fuzhou University Fuzhou China Maynooth International Engineering College Fuzhou University Fuzhou China College of Computer and Cyber Security Fujian Normal University Fuzhou China

ISBN: (纸本)9798350304602

As we all know, population aging is a significant challenge faced by Chinese society, and ensuring the health and safety of elderly individuals has become an urgent topic of concern. Within the context of family safety, elderly individuals often experience falls or fainting due to age-related physical decline or underlying medical conditions. In response to this phenomenon, this paper presents a method for enhancing the wellbeing of family members by utilizing the YOLOv5 model to detect falls. Moreover, due to the built-in capability of YOLOv5 to read video from webcam, this technology can also be integrated into loT devices, turning these devices into a part of smart homes. Considering the specific nature of the home environment, CAU CAFall is considered to be the most suitable dataset. Various variations of the YOLOv5 model are experi-mented on a CAUCAFall dataset and achieve promising results. The YOLOv5x model achieved a precision of 82.2%, while the YOLOv5s model, with improved running speed, achieved an precision of 79.6%. Finally, we explored and selected the most suitable YOLOv5 model for home fall detection considering comprehensive evaluation metrics, and it is YOLOv5s. © 2023 IEEE.

关键词： Fall detection

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cybersecurity Revolution via Large Language Models and Explainable AI 17

Cybersecurity Revolution via Large Language Models and Expla...

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17th International Conference on security of Information and Networks, SIN 2024

作者： Ghazal, Taher M. Janjua, Jamshaid Iqbal Abushiba, Walid Ahmad, Munir Ihsan, Anuam Al-Dmour, Nidal A. College of Arts & Science Applied Science University Manama Bahrain Center for Cyber Security Faculty of Information Science and Technology Selangor Malaysia Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul02841 Korea Republic of College of Engineering Mutah University Department of Computer Engineering Jordan

ISBN: (纸本)9798331509736

Integrating Groundbreaking advancements in AI, like language models, interpretable AI, and machine learning, opens up a world of exciting new possibilities. The Evolving face of cybersecurity and Modern cyber threats are complex and well crafted;hence, conventional cybersecurity mechanisms show difficulty in staying relevant. LLMs, especially based on Transformer architecture will noticeably increase the accuracy and speed of detecting threats. Transparency and trust are increased by XAI approaches like SHAP and LIME, which offer facts about ML model predictions. This paper explores the literature that demonstrates the integration between XAI and LLMs in cybersecurity, exemplifying how this trinity of models has the potential to help attenuate errors producing reduced false positives and improve how we detect threats. Thinking about the possibilities the challenges including performance Explainability trade-offs, the need for common evaluation metrics, and the black-box nature of AI Models, remain in place. Solving these will help to enhance AI-driven solutions in cybersecurity. © 2024 IEEE.

关键词： cybersecurity Explainable AI (XAI) Large Language Models (LLMs) Machine Learning (ML) Reliability Threat Detection Transformer Architecture Transparency

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An Effective Span-based Multimodal Named Entity Recognition with Consistent Cross-Modal Alignment 30

An Effective Span-based Multimodal Named Entity Recognition ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Xu, Yongxiu Xu, Hao Huang, Heyan Cui, Shiyao Tang, Minghao Wang, Longzheng Xu, Hongbo Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9782493814104

With the increasing availability of multimodal content on social media, consisting primarily of text and images, multimodal named entity recognition (MNER) has gained a wide-spread attention. A fundamental challenge of MNER lies in effectively aligning different modalities. However, the majority of current approaches rely on word-based sequence labeling framework and align the image and text at inconsistent semantic levels (whole image-words or regions-words). This misalignment may lead to inferior entity recognition performance. To address this issue, we propose an effective span-based method, named SMNER, which achieves a more consistent multimodal alignment from the perspectives of information-theoretic and cross-modal interaction, respectively. Specifically, we first introduce a cross-modal information bottleneck module for the global-level multimodal alignment (whole image-whole text). This module aims to encourage the semantic distribution of the image to be closer to the semantic distribution of the text, which can enable the filtering out of visual noise. Next, we introduce a cross-modal attention module for the local-level multimodal alignment (regions-spans), which captures the correlations between regions in the image and spans in the text, enabling a more precise alignment of the two modalities. Extensive experiments conducted on two benchmark datasets demonstrate that SMNER outperforms the state-of-the-art baselines. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Alignment

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