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security Flaw in TCP/IP and Proposed Measures 1

International Conference on Recent Developments in cyber security, ReDCySec 2023

作者： Upadhyay, Sourav Kumar Kumar, Prakash Department of Computer Science and Engineering BIT Sindri Dhanbad India Department of Computer Science and Cyber Security JRSU Ranchi India

ISBN: (数字)9789819998111

ISBN: (纸本)9789819998104

The whole Internet is based on TCP/IP protocol suite. The inherent flaw in the TCP/IP makes a cascading effect for other attacks like flooding, fingerprinting, and many more. All the switching and routing mechanisms are based on the TCP/IP, so it is safe to say that TCP/IP is the backbone protocol suite for communication for all major Internet applications. The architectural flaw of TCP/IP protocol and possible ways to mitigate those flaws are discussed in this paper. Especially, the problem with connection phase in TCP and how SYN/ACK/RST packets can be used to breach an ongoing handshake are brought in light. In order to help create a safe information network environment in the future, this study aims to provide some references. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Transmission control protocol

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Genetic Algorithm-optimized k-nearest Neighbors and Support Vector Machines for Breast Cancer Detection in Resource-constrained Environments

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第3期52卷 848-861页

作者： Alemu, Abebe Girma, Anteneh Abebe, Mesfin Srinivasagan, Ramasamy Adama Science and Technology University Adama Ethiopia Computer Science/Cyber-Security department University of the District of Columbia WashingtonDC United States Computer Science and Engineering department Adama Science and Technology University Adama Ethiopia Computer Engineering department CCSIT King Faisal University Al Hufuf Saudi Arabia

Breast cancer poses a significant global threat, highlighting the urgent need for early detection to reduce mortality rates. Researchers are working to minimize the occurrence of false positives and false negatives, thereby improving the efficiency of breast cancer detection models. To achieve this, they employ advanced techniques such as artificial intelligence, machine learning, deep learning, and computational intelligence. Support vector machines (SVM) and k-nearest neighbors (KNN) are two popular lightweight machine-learning techniques.;however, their effectiveness depends on proper feature selection and parameter tuning. Genetic algorithm optimization provides a solution by intelligently selecting relevant features and fine-tuning parameters, which enhances classification accuracy for early diagnosis. This study demonstrates the effectiveness of a hybrid computational intelligence model that utilizes genetic algorithms for feature selection. The proposed GAKNN-SVM model shows superior performance in detecting breast tumors, utilizing the Wisconsin Breast Cancer Diagnostic Dataset. The results indicate significant improvements, with accuracy, sensitivity, and specificity rates reaching 98.25%, 98.15%, and 98.41%, respectively, based on 171 test samples. Overall, genetic algorithms and machine learning approaches hold great promise for improving breast cancer detection accuracy, ultimately leading to better diagnostic outcomes and reduced mortality rates, especially in resource-constrained environments. © (2025), (International Association of Engineers). All rights reserved.

关键词： Support vector machines

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Blockchain-Empowered Asynchronous Federated Reinforcement Learning for IoT-Based Traffic Trajectory Prediction

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IEEE Internet of Things Journal 2025年第11期12卷 17095-17109页

作者： Wang, Bin Tian, Zhao Tang, Fengxiao Pan, Heng She, Wei Liu, Wei Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450002 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China Central South University School of Computer Science and Engineering Changsha410083 China Zhongyuan University of Technology School of Cyber Space Security Zhengzhou450007 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou Key Laboratory of Blockchain and Data Intelligence Zhengzhou450002 China

Vehicle trajectory prediction plays a crucial role in IoT-based intelligent transportation systems, which can effectively address key issues, such as driving safety and multivehicle collaboration. However, the sensitivity of trajectory data and the reluctance of data holders to share it constrain the prediction model’s ability to capture vehicle behavior patterns in different scenarios. To address the above problems, we propose a blockchain-enabled asynchronous federated proximal policy optimization framework (BE-AFPPO) for the trajectory prediction of self-driving vehicles. First, we propose a curiosity proximal policy optimization (C-PPO) algorithm. The method utilizes a driven exploration strategy to actively motivate the intelligent agent to explore the unknown state space. The avoidance policy model reaches a local optimum when processing trajectory data. In addition, we design historical gated recurrent unit (GRU) and future GRU as input layers. The target’s historical motion features and future trajectory features are extracted, respectively. Then, various data is received through asynchronous federated learning. This model can fully learn the vehicle’s behavior patterns in different scenarios, which improves prediction accuracy. Based on this, we develop a blockchain-based dynamic group practical Byzantine fault tolerance (DG-PBFT) consensus algorithm. This enhances the credibility and integrity of the data while enriching the sources of trajectory data. Finally, we perform the experiments on the publicly available dataset nuScenes. The results demonstrate that the proposed method improves the robustness and accuracy of trajectory prediction. © 2014 IEEE.

关键词： Federated learning

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Dynamic Graph-Enhanced Sequential LSTM for Context-Aware Point-of-Interest Recommendation 21

Dynamic Graph-Enhanced Sequential LSTM for Context-Aware Poi...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Yuwen, Zhang Shaobo, Ren School of Computer Science and Engineering School of Cyber Security University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331519254

Next Point-of-Interest (POI) recommendation is a critical task in location-based services, aiming to predict the user's next POI based on historical check-in data. Existing approaches often rely on static graphs or sequential models to capture user preferences, which are limited by the sparsity of check-in data and the inability to dynamically model spatio-temporal transitions. To address these challenges, we propose the Dynamic Graph-Enhanced LSTM (DGEL) model for next POI recommendation. DGEL constructs dynamic POI transition graphs by combining Transformer-based graph learning with embedding techniques, capturing implicit relationships between POIs. Additionally, a Customized Sequential LSTM (CS-LSTM) is introduced to model long-term and short-term dependencies, incorporating spatial, temporal, and relational signals. Experimental results on Gowalla and Foursquare datasets demonstrate that DGEL achieves significant performance improvements over state-of-the-art baselines, highlighting its effectiveness in leveraging dynamic graphs and structured sequential learning for POI recommendation. © 2024 IEEE.

关键词： Knowledge graph

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Multi-Behavioral Recommender System with Heterogeneous Graph Learning 4

Multi-Behavioral Recommender System with Heterogeneous Graph...

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4th International Conference on Consumer Electronics and computer engineering, ICCECE 2024

作者： Feng, Jiaming Liu, Yang Zhang, Junjie School of Computer Science and Engineering School of Cyber Security University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350384062

With the rapid development of online platforms and the explosive growth of user-generated data, personalized recommendation systems have become increasingly crucial in delivering relevant content to users. Traditional recommendation approaches often focus on modeling user-item interactions, neglecting the rich contextual information associated with user behaviors. In this paper, we propose a novel multi-behavior recommendation system based on heterogeneous graph learning. By leveraging the power of heterogeneity, our model integrates various types of user behaviors, such as browsing, adding cart, and purchasing, into a unified framework. We design a graph-based representation for capturing the complex relationships between users, items, and behaviors. Additionally, we introduce a graph neural network approach to effectively propagate and aggregate information across the heterogeneous graph. Experimental results on a real-world dataset demonstrate that our proposed system outperforms state-of-the-art recommendation methods in terms of accuracy and diversity. The findings highlight the potential of heterogeneous graph learning in enhancing the performance of multi-behavior recommendation systems. © 2024 IEEE.

关键词： Recommender systems

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A hybrid encryption for secure data deduplication the cloud

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International Journal of Cloud Computing 2023年第2-4期12卷 295-307页

作者： Ebinazer, Silambarasan Elkana Savarimuthu, Nickolas Bhanu, S. Mary Saira Department of Computer Science and Engineering Cyber Security IIIT Kottayam India Department of Computer Applications National Institute of Technology Tiruchirappalli India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

Cloud computing (CC) is a cost-effective platform for users to store their data on the internet rather than investing in additional devices for storage. Data deduplication (DD) defines a process of eliminating redundant data in the cloud by leaving a single copy. This paper provides a hybrid encryption scheme using genetic algorithm (GA) and attribute-based encryption (ABE) to ensure secure data transmission in the cloud and preventing the data deduplication by using message locked encryption (MLE). The hybrid encryption scheme provides a secure data transmission in the cloud. From the simulation output, the time requirement is reduced up to 94% and consumes 43% bandwidth compared with existing works. The proposed method tends to be quite effective in reducing storage and bandwidth costs. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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Effectiveness of a touch-based hand therapeutic application for hand dexterity in children with handwriting difficulties-a pilot study

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Multimedia Tools and Applications 2024年 1-35页

作者： Smitha, John Renumol, V.G. School of Computer Science and Technology Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India School of Engineering Cochin University of Science and Technology Kochi India

Handwriting is an important skill for children during their academic years. It is the coordination of perceptual-motor and cognitive abilities. Some children have difficulties in handwriting, which is an indication of issues in their fine motor skills. Nowadays, technology-based instructions are available to develop fine motor skills in children with handwriting disabilities. This paper describes the details of a newly developed software application namely ‘HanDex’ for children with handwriting difficulties and the experiments conducted to test its effectiveness. The application comprises six hand therapeutic activities which were designed based on the oscillation theory of handwriting. Initially, in the first phase, the experiments were conducted in nine primary school children from various schools, aged 8–12 years. Then, in the second phase, the software training was given to six upper primary school children (10–12 years) with handwriting issues. The results of using this application were promising. So one more study was conducted in another set of nine children (8–11 years) with handwriting difficulties. The participants were assessed by their special educators, class teachers and parents. After the pretest, they received a total of 18 handwriting practice sessions using this new application. At the end of the training sessions, a post test was conducted, to measure the progress in handwriting. The handwriting rubrics with eight handwriting parameters such as letter formation, placement, letter sizing, spacing of letters, legibility, speed, neatness, and spacing of words were used for handwriting assessment. The findings from the study suggested that touch-based hand therapeutic activities were beneficial for children with writing difficulties to improve their handwriting performance and tripod grip. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Children with handwriting difficulties Hand Dexterity Handwriting performance and tripod grip Tablet computer ‘HanDex’ Software

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Advanced functional connectivity analysis with integrated EEG signal enhancement and GTW-EEG-GAN for emotion detection

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International Journal of computers and Applications 2024年第12期46卷 1152-1165页

作者： Babu, Nirmal Varghese Kanaga, E. Grace Mary Division of Computer Science and Engineering School of Computer Science and Technology Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Division of Data Science and Cyber Security School of Computer Science and Technology Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

Emotion recognition is crucial in human-computer interaction and psychological research, utilizing modalities such as facial expressions, voice intonations, and EEG signals. This research investigates AI-driven techniques by employing Graph Neural Networks (GNNs) on functional connectivity matrices derived from EEG data to advance emotion recognition. Our approach integrates sophisticated preprocessing methods, including Integrated EEG Signal Enhancement (IESE) and novel augmentation techniques such as Gaussian Time Warping Generative Adversarial Network (GTW-EEG-GAN). This integration aims at enhancing data quality and diversity. Signal decomposition using EMD-Wavelet Hybrid Decomposition further refines feature extraction, enabling robust analysis of EEG signals. Functional connectivity metrics capture critical neural interactions intended for precise emotion characterization. The GNN architecture effectively processes these features, achieving significant accuracy improvements. Evaluations on the DEAP dataset demonstrate promising results, attaining 94.5% accuracy before hyperparameter tuning and further improving to 98.6% after tuning, highlighting the system's efficacy. The methodology showcases the integration of advanced signal processing techniques with AI, offering a comprehensive framework for future studies. The findings advocate potential applications in areas such as personalized mental health monitoring, adaptive learning environments, and responsive gaming experiences, demonstrating the broad impact and versatility of AI-driven EEG-based emotion recognition. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Emotion Recognition

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Brain Cancer Prediction Using EvoNet: A Modified Resnet Protocol

Brain Cancer Prediction Using EvoNet: A Modified Resnet Prot...

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2024 International Conference on Big Data Analytics in Bioinformatics, DABCon 2024

作者： Majumder, Sayan Das, Rohick Cyber Security the Heritage Academy Kolkata India Computer Science & Engineering Institute of Engineering & Management Kolkata India

ISBN: (纸本)9798350378047

Brain tumors are among the most common and malignant diseases. Prompt diagnosis makes a big difference in enhancing survival rates among brain tumor patients. The Magnetic Resonance Imaging, or MRI, is a technique for brain tumors since it displays full information about the structure of the brain and pathologies involving brain tissue. This has made possible the automatic detection of a brain tumor through the technological advancement of deep learning and computer vision technologies, while ConvNeural Networks classify them. In this study, we introduce a novel model, the Evolutionary Network (EvoNet), for classifying brain tumor images. Our model is a modified ResNet model where we have used 128 filters. Our EvoNet model is compared with CNN and ResNet models from which it is observed that our model has a higher accuracy in detecting brain tumour compared to the previously used models. © 2024 IEEE.

关键词： Deep learning

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Real-time Autonomous Vehicle Maneuver Generation Algorithm Based on Hierarchical Planning 24

Real-time Autonomous Vehicle Maneuver Generation Algorithm B...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Zhai, WenHao School of Computer Science and Engineering School of Cyber Security University of Electronic Science and Technology of China Sichua Chengdu China

ISBN: (纸本)9798400717147

Global path planning, whether based on sampling-based algorithms or heuristic search algorithms, can search for a global path from the start point to the destination in a relatively short time. However, they often struggle to avoid local obstacles and have limited adaptability to local dynamic environments, making it difficult to meet the requirements of obstacle avoidance in unknown environments. On the other hand, the local path planning DWA algorithm can perform online real-time planning and has excellent obstacle avoidance capabilities. However, in large-scale and complex environments, it may fail to obtain the optimal path or even reach the target point if there is a lack of intermediate waypoint guidance. Therefore, to leverage the strengths of both approaches, an instruction generation algorithm based on a hierarchical plan approach is introduced. This algorithm utilizes the global path points as relay points for the DWA algorithm, enabling real-time obstacle avoidance tasks. Finally, the superiority of the fusion algorithm is verified through comparative simulation experiments. © 2024 ACM.

关键词： Motion planning

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