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4th IEEE International Conference on Smart Information Systems and Technologies, SIST 2024

作者： Tuhin, Hemayet Hossain Kayum, Md Abdul Rahmatul Kabir Rasel Sarker, Md. Tanvir Siddiquee, Shah Md Pandey, Bishwajeet Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Intelligent System and Cyber Security Astana IT University Kazakhstan

ISBN: (纸本)9798350374865

Coconut is one of the main economic crops in Bangladesh. It is a tree whose every part is useful in one way or another in public life. The leaves, flowers, fruits, stems, and roots of this tree are used as raw materials for various small and large industries, materials for making various delicious foods, delicious drinks, and food for patients. This is the world’s most beautiful tree and is well known and appreciated by all as the’heavenly tree’. However, it has recently become known that most coconut trees suffer from illnesses that gradually weaken the trees’ health and coconut yield. Pest illnesses and nutrient deficits have an impact on the majority of the tree’s leaves. The main reason is that most of the farmers in our country are unaware. They do not know what kind of measures to take in the case of any disease. They are using pesticides, assuming ancient principles, sometimes benefiting and sometimes counterproductively, and making the farmers poor. As a result, farmers are showing disinterest in coconut cultivation. Our main objective is to increase the viability of coconut leaves and detect problems early on so that farmers can benefit more from the cultivation of coconuts. The study suggests examining diseases and detecting insect attacks and nutritional deficiencies in coconut leaves. This will help grow more coconuts. It is expected that this model will help the agricultural field on both an economic and ecological level. This study focuses on the three coconut diseases: WCLWD Yellowing, WCLWD Flaccidity, and CCI Caterpillars. Three neural networks have_ been chosen in this study to identify the best model. After evaluating the VGG19, MobileNet v2, Inception v3, and ResNet50 models, the accuracy for a collection of 3166 images was determined to be 96%, 96.02%, 97.77%, and 99.36%, respectively. As a result of this technology, farmers will be able to produce more coconuts, surely bringing about a revolution in the agricultural industry. © 2024 IEEE.

关键词： coconut leaf disease Inception v3 MobileNet v2 nutrient deficiency pest attacks ResNet50 VGG19

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IDCE:Integrated Data Compression and Encryption for Enhanced security and Efficiency

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computer Modeling in engineering & Sciences 2025年第4期143卷 1029-1048页

作者： Muhammad Usama Arshad Aziz Suliman A.Alsuhibany Imtiaz Hassan Farrukh Yuldashev Department of Cyber Security Pakistan Navy Engineering CollegeNational University of Sciences and TechnologyKarachi75350Pakistan Department of Computer Science Main CampusIqra UniversityKarachi75500Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100170Uzbekistan

Data compression plays a vital role in datamanagement and information theory by reducing ***,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and *** the exponential growth of digital data,robust security measures are *** encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key *** their individual benefits,both require significant computational ***,performing them separately for the same data increases complexity and processing *** the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and *** on 128-bit block sizes and a 256-bit secret key *** combines Huffman coding for compression and a Tent map for ***,an iterative Arnold cat map further enhances cryptographic confusion *** analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.

关键词： Chaotic maps security data compression data encryption integrated compression and encryption

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Advancing Android Malware Detection with BioSentinel Neural Network using Hybrid Deep Learning Techniques 18

Advancing Android Malware Detection with BioSentinel Neural ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rani, D. Sandhya Gnaneshwar, K. Sampurnima Pattem, K. Sekhar, Soma Krishna, G. Bala Kakarla, Shirisha Cvr College of Engineering Department of Computer Science Engineering Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engineering-Cyber Security Hyderabad India Vardhaman College of Engineering Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

The paper introduces the BioSentinel Neural Network (BSNN), a novel hybrid deep learning model designed to enhance malware detection, particularly focusing on zero-day threats. The BSNN model integrates diverse neural network architectures, leveraging Graph Neural Networks (GNNs) for feature extraction and Transformers for sequential data analysis. It demonstrates significant improvements in accuracy (93.16%), precision (90.89%), recall (88.19%), and F1-score (90.45%) over traditional methods. The model exhibits a high detection rate of 88% for zero-day malware from 1000 samples tested and shows computational efficiency with an average inference time of 30 milliseconds. This research addresses critical gaps in current malware detection approaches, highlighting the potential of hybrid deep learning techniques in the cybersecurity domain. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： cybersecurity Deep Learning Hybrid Neural Networks Malware Detection Real-Time Analysis Zero-Day Threats

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Credit Card Fraud Detection Using ML Techniques 1

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International Conference on Recent Developments in cyber security, ReDCySec 2023

作者： Bonkoungou, Samiratou Roy, Nihar Ranjan Ako, Nomel Haymes Axel-Elie Junior Mishra, Alpna Department of Computer Science and Engineering Sharda University Noida India Center of Cyber Security and Cryptology Sharda University Noida India

ISBN: (数字)9789819998111

ISBN: (纸本)9789819998104

With the increasing use of Internet, online banking transactions have also increased. Everyday more and more users are being added to the cyberspace, which has also attracted cybercriminals. These scammers use stolen credit card information for making online purchases. The revolution of online business or commerce has given an increased cybersurface for financial fraud targeting digital cards. By analyzing the cardholder’s transaction activity, using machine learning techniques, we can easily identify the fraudulent activity or transaction. In case of any abnormal behavior, the transaction is termed as fraudulent, and numerous techniques have been proposed by the researchers to prevent these, among these techniques machine learning-based techniques are the most popular. In this paper, we have surveyed the solution with respect to ML-based techniques, conducted experiments, and concluded our findings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Secure Communication for MUM-T: A Blockchain and Lightweight Cryptography Framework 17

Secure Communication for MUM-T: A Blockchain and Lightweight...

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

作者： Yasar, Halimcan Bahtiyar, Serif Istanbul Technical University Cyber Security and Privacy Research Lab SPFLab Department of Computer Engineering Maslak Istanbul34469 Turkey

ISBN: (纸本)9798331509736

Manned-Unmanned Teaming (MUM-T) systems integrate manned aircraft and unmanned aerial vehicles (UAVs) to enhance mission effectiveness, allowing a single pilot to coordinate multiple UAVs for tasks like reconnaissance, communication, and targeting. However, the complexity and operational demands of MUM-T systems introduce significant security challenges, particularly for mission-critical data integrity and real-time communication. In this paper, we propose a new framework for adaptive blockchain cryptography that combines Proof of Authority (PoA)-based blockchain with XOR-based lightweight authentication. The blockchain component, with the manned aircraft that serves as the sole validator, ensures tamper-resistant logging of key mission data. Additionally, it supports accountability and traceability through an efficient PoA consensus algorithm. In parallel, XOR-based lightweight authentication secures control and telemetry signals with minimal computational overhead that enables low-latency and a real-time communication. Analyses results show that the proposed framework achieves a better transaction throughput with acceptable latency, which meets the stringent security and performance requirements of MUM-T operations. The proposed framework offers a scalable and resilient solution for secure communications in complex military environments. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Containerized IoT Simulation Environment for Network Warfare Training 17

A Containerized IoT Simulation Environment for Network Warfa...

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IEEE Congress on cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Chen, Ruihao Lyu, Chengcheng Wang, Wenzhao Lin, Hui Lin, Qingxin Fujian Normal University College of Computer and Cyber Security Fuzhou China Fuzhou University Zhicheng College Department of Computer Engineering Fuzhou350002 China

ISBN: (纸本)9798350351637

Amidst the growing prevalence of Internet of Things (IoT) devices and the expanding risks to enterprise networks, there is a sustained increase in the demand for controlled penetration testing to delineate attack surfaces and uncover vulnerabilities within business operations. While the market offers a plethora of cybersecurity companies providing third-party penetration testing services, these services are often prohibitively expensive and fail to ensure data security. In response, we propose a lightweight environment emulation platform based on containerization, which abstractly simulates various IoT devices and environments from real-world settings, enabling the creation of controllable virtual environments that closely mimic actual business *** constructed virtual environment not only permits users to conduct attack simulations but also allows for the simultaneous inclusion of defensive strategies, thereby simulating a comprehensive set of attacker-defender scenarios. This significantly enhances the diversity and complexity of training exercises. This approach satisfies the requirements for data security and controllability, thereby strengthening the enterprise's ability to both defend against and respond to cyber threats. © 2024 IEEE.

关键词： Virtual environments

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Enhancing the security of SDN in 5G: A Hybrid Feature Selection Based Ensemble Machine Learning Framework for Classification of cyber-Attacks

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SN computer Science 2025年第3期6卷 1-16页

作者： Singh, Mahendra Pratap Haimashreelakshmi Singh, Virendra Pratap Gupta, Maanak Cyber Security Research Lab (CSRL) Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangaluru 575025 India Department of Computer Science Tennessee Technological University Cookeville TN United States

In recent years, the adoption of 5G has significantly increased due to its numerous benefits, including high availability, lower latency, improved reliability, and high performance. To manage packet flow, 5G relies on Software-Defined Networking (SDN) that employs software controllers and Application Programming Interfaces (APIs) to route packets and communicate with the hardware, providing advantages like high efficiency, low cost, and dependability. However, due to centralized control, SDN controllers are vulnerable to various cyber-attacks, including Distributed Denial of Service (DDoS), Denial of Service (DoS), Password Brute Forcing, Web Attacks, etc. This paper proposes a framework that comprises a hybrid feature selection method and an ensemble machine learning model. The proposed ensemble model combines the strengths of three different machine learning (ML) classifiers to create a voting classifier for classifying traffic in SDN. Additionally, the optimal value for the hyperparameters of each classifier is obtained through hyperparameter tuning. Finally, the experimental analysis of the proposed model using the InSDN dataset shows 99.96% accuracy, highlighting the proposed model’s effectiveness in addressing the limitations of the existing approaches and detecting multiple attacks in the SDN context. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G Hybrid feature selection Machine learning Software defined networking

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Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis

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Blockchain(Research and Applications) 2024年第3期5卷 106-122页

作者： Mohammad Hasan Mohammad Shahriar Rahman Helge Janicke Iqbal H.Sarker Department of Computer Science and Engineering Premier University4000 ChitagongBangladesh Department of Computer Science and Engineering United International University1212 DhakaBangladesh Cyber Security Cooperative Research Centre 6027 PerthAustralia Centre for Securing Digital Futures Edith Cowan University6027 PerthAustralia

As the use of blockchain for digital payments continues to rise,it becomes susceptible to various malicious *** detecting anomalies within blockchain transactions is essential for bolstering trust in digital ***,the task of anomaly detection in blockchain transaction data is challenging due to the infrequent occurrence of illicit *** several studies have been conducted in the field,a limitation persists:the lack of explanations for the model’s *** study seeks to overcome this limitation by integrating explainable artificial intelligence(XAI)techniques and anomaly rules into tree-based ensemble classifiers for detecting anomalous Bitcoin *** shapley additive explanation(SHAP)method is employed to measure the contribution of each feature,and it is compatible with ensemble ***,we present rules for interpreting whether a Bitcoin transaction is anomalous or ***,we introduce an under-sampling algorithm named XGBCLUS,designed to balance anomalous and non-anomalous transaction *** algorithm is compared against other commonly used under-sampling and over-sampling ***,the outcomes of various tree-based single classifiers are compared with those of stacking and voting ensemble *** experimental results demonstrate that:(i)XGBCLUS enhances true positive rate(TPR)and receiver operating characteristic-area under curve(ROC-AUC)scores compared to state-of-the-art under-sampling and over-sampling techniques,and(ii)our proposed ensemble classifiers outperform traditional single tree-based machine learning classifiers in terms of accuracy,TPR,and false positive rate(FPR)scores.

关键词： Anomaly detection Blockchain Bitcoin transactions Data imbalance Data sampling Explainable AI Machine learning Decision tree Anomaly rules

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Data Analytic for Healthcare cyber Physical System

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IEEE Transactions on Network Science and engineering 2023年第5期10卷 2490-2502页

作者： Gan, Wensheng Hu, Kaixia Huang, Gengsen Chien, Wei-Che Chao, Han-Chieh Meng, Weizhi Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien97401 Taiwan National Dong Hwa University Department of Electrical Engineering Hualien97401 Taiwan UCSI University Institute of Computer Science and Innovation Kuala Lumpur Malaysia Department of Applied Mathematics and Computer Science Cyber Security Section Lyngby2800 Denmark

Nowadays, a large number of AI-powered healthcare cyber-physical systems (CPSs) have been used in healthcare services. In order to provide better care, AI-powered healthcare CPSs analyze the data they collect using a variety of techniques. Data analysis for artificial intelligence (AI)-driven healthcare CPS is one of these approaches. However, none of the techniques in data analysis can provide a good representation of contiguous and negative information. Therefore, we are the first to introduce the problem of contiguous negative sequential pattern mining. A novel algorithm called Contiguous Negative Sequential Pattern Miner (CNSPM) is proposed to discover and analyze contiguous negative sequential patterns (CNSPs) from the data collected by healthcare CPSs. Finally, we select some real medical and non-medical datasets to conduct numerous experiments. We further analyze the discovered patterns and show how healthcare services can use meaningful patterns for medical decision-making. The performance results on these datasets demonstrate that the proposed algorithm can discover more valuable patterns efficiently and effectively from the collected and transformed medical data. © 2013 IEEE.

关键词： Data mining

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