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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Arif, Mohd Ahmad, Shahnawaz Nazim, Mohd Ahmad, Javed Onyarin, Ogala Justin Galgotias University Department of Computer Science Engineering and Technology Greater Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India Noida Institute of Engineering and Technology Department of Computer Science Greater Noida India University of Delta Pmb Cyber Security Department Delta State Agbor2090 Nigeria

ISBN: (纸本)9798350380583

AI when integrated with cybersecurity and data analysis forms a new field that will have great impact on the society. The present research focuses on the social consequences of integrating AI for cybersecurity and big data analytics, thus filling the gap in the existing literature. Using both qualitative and quantitative points of view, quantitative analysis and qualitative examination, the research offers an all-embracing understanding of this convergence. It aims to review and analyze the history, present usage, future prognosis of AI related cybersecurity and data analysis, including the reflection of ethical issues, privacy aspects and relevant laws. The lessons learnt are discussed based on case studies, best practices analysis, and the study provides recommendations for the effective use of AI-based solutions throughout their life cycle. The analysis broadens the knowledge on the effects of AI technology for cybersecurity and data analytics, and helps researchers, policymakers, and industry stakeholders apply the new knowledge. © 2024 IEEE.

关键词： cybersecurity

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Breast Cancer Detection using Domain-Adversarial Training (DANN) with Invariant Risk Minimization (IRM) Hybrid Approach 3

Breast Cancer Detection using Domain-Adversarial Training (D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Akash, K.V. Daniel, Esther Seetha, S. Durga, S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Cmr Institute of Technology Department of Information Science & Engineering India Amrita Vishwa Vidyapeetham Tifac Core in Cyber Security Amrita School of Engineering India

ISBN: (纸本)9798331509675

The breast cancer detection performs a key function in the health care network. The precise and early detection of cancer in the breast could aid to save life of the sufferer. The traditional machine learning methods struggle due to the data used to train is different from data used later this is known as Domain shifts. This project uses a hybrid model known as Domain-Adversarial Training of Neural Networks (DANN) with Invariant Risk Minimization (IRM) Hybrid Method to identification of the tumor in the breast. Through integrating the (DANN) and IRM works perfectly among the various type of data and also with the various image from the various sources. This model is more precise and reliable than any other older *** that this model act as an effective tool for detecting the breast cancer in the early stages. © 2025 IEEE.

关键词： Adversarial machine learning

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Empowering Smart Homes with Fog Computing for IoT Connectivity

Empowering Smart Homes with Fog Computing for IoT Connectivi...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Mahdi, Ali Jafer Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq AI-Zahraa University for Women Department of Scientific Affairs Bangi43600 Malaysia College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Innovative grid technology leverages Information and Communication technology (ICT) to enhance energy efficiency and mitigate losses. This paper introduces a 'novel three-tier hierarchical framework for smart homes within the Internet of Things (IoT)' ecosystem, addressing the current shortage of intelligent solutions that underutilize IoT advantages. Our framework's primary objective is to elevate smart homes to the microgrid level, enabling the seamless integration from the microgrid's renewable distributed energy sources and attaining maximum energy efficiency. In addition to traditional data processing, we propose the application of fog computing in smart homes. Through a rigorous simulation using real smart meter datasets, our study demonstrates that fog computing, mainly when using predictive filtering, may drastically cut down on the amount of data being sent and the load on the home's network. This innovation holds immense potential for revolutionizing energy management and resource allocation in the context of smart homes, thus contributing to a sustainable and efficient future. This research represents a critical step towards harnessing the power of IoT and fog computing in smart grids and homes, offering a promising path towards more intelligent, eco-friendly, and energy-efficient living spaces. © 2024 IEEE.

关键词： Smart homes

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The Future of Recruitment: Using Deep Learning to Build Intelligent Interview Bots 15

The Future of Recruitment: Using Deep Learning to Build Inte...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ahmad, Shahnawaz Hussain, Shahadat Wasid, Mohammed Onyarin, Ogala Justin Arif, Mohd Ahmad, Javed Bennett University School of Computer Science Engineering and Technology Greater Noida India University of Delta Cyber Security Department PMB 2090 Delta State Agbor Nigeria Galgotias University Department of Computer Science Engineering and Technology Greater Noida India Sharda University Department of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350370249

The hiring process for IT positions is often complex and time-consuming, posing significant challenges for businesses seeking to maintain consistency and efficiency. Interview scheduling is complicated by manual effort and scheduling difficulties, which have been further made worse by the COVID-19 pandemic, making in-person interviews difficult. To address these challenges, this study proposes a Natural Language Processing (NLP)-based Machine Learning (ML) solution that automates interview scheduling and communication in natural language. To develop the solution, two deep-learning NLP models served as the foundation of the system, and AWS serverless stack and Amazon SageMaker were used for training and inference. The solution is designed to be scalable and cost-effective, and new models can be easily added and trained on unique subjects. Furthermore, the Amazon Sumerian scene is customizable to meet branding requirements. This research offers significant implications, as the proposed ML solution can help businesses streamline their hiring process, reduce human error, and improve the accuracy of candidate assessments. The scalability and cost-effectiveness of the solution make it ideal for businesses of all sizes. The smart interview bot developed in this study can conduct live-like interviews that mimic human interactions, making the hiring process more engaging and personalized. Overall, this paper demonstrates the potential of NLP to develop a smart solution for an everyday business problem. The intelligent live-like interview process and smart interview bot developed in this research have the potential to revolutionize the hiring process by automating scheduling and reducing manual effort. By leveraging the proposed ML solution, businesses can improve the accuracy and consistency of their hiring practices, ultimately leading to more successful outcomes. © 2024 IEEE.

关键词： Amazon SOS Amazon Web Services BERT Dynamo DB Machine Learning Natural Language Processing

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Designing Biometric Automated Teller Machine: Opportunities and Challenges 4

Designing Biometric Automated Teller Machine: Opportunities ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Bastos, João Baptista Muchinenyika, Simon Hlekisana Shava, Fungai Bhunu Nyambuya, Tanaka Mamvura Namibia University of Science and Technology Cyber Security Department Windhoek Namibia Namibia University of Science and Technology Software Engineering Department Windhoek Namibia

ISBN: (纸本)9798331518981

The increasing sophistication of fraud in the financial sector, particularly in Automated Teller Machine (ATM) transactions, demands advanced security measures to protect users and their accounts. Despite the widespread use of Personal Identification Numbers (PINs), literature is replete with evidence that biometric authentication provides better security than PINs. However, a survey on ATMs around the globe shows that biometrics are not widely used for authentication compared to PINs. In an effort to close the gap, this paper presents a two-pronged approach to uncover the challenges and opportunities of their adaptation in Namibia. A literature survey was carried out to establish what is known and identify the gaps. Secondly, some semi-structured qualitative interviews were conducted with purposefully selected key personnel for banks in Namibia and law enforcement agencies to understand ATM security landscape in the country. From these two approaches, biometric designs used in authenticating ATM systems are discussed to unearth strengths and weaknesses that will guide the designing of secure biometric ATM systems. The researchers hope the findings will improve the adoption of biometric security systems, particularly in Namibia, where questionnaires and interviews were administered to ATM experts and the Police. © 2024 IEEE.

关键词： Automatic teller machines

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Vision-based UAV Detection in Complex Backgrounds and Rainy Conditions 2

Vision-based UAV Detection in Complex Backgrounds and Rainy ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Pranita Gupta, Keshav Jain, Amit Kumar Vishakha Jain, Abhishek Jain, Arpit Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Grd Institute of Management and Technology Department of Computer Science and Engineering Dehradun India Coer University Department of Cyber Security Roorkee India Uttaranchal University Department of Computer Science and Engineering Dehradun India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350371055

Studies aimed at detecting UAVs in real time using processer vision and deep learning are in their infancy. Although there are many possible advantages to using unmanned aerial vehicles (UAVs), some people are concerned about the risks and misuse of these machines. All potential threats to safety, privacy, and security fall under this category. It is common practice to combine hardware components, such as cameras, with software components when developing vision-based detection systems. Under difficult circumstances such as changing experiences, varying UAV sizes, complicated related settings, and light to heavy rain, this research evaluates the performance of popular and cutting-edge vision-based object-identification algorithms in detecting unmanned aerial vehicles (UAVs). Two datasets were created;one utilized the sky and the other had a more complex background. The study's overarching goal was to evaluate how various methods fared in this particular context. In YOLOv5, YOLOv8, Retina Net, and Faster-RCNN, we examine and evaluate indicators with one stage and two stages. The results of this research could provide light on the performance of the chosen models in difficult settings and lead to the creation of more trustworthy UAV detection systems. © 2024 IEEE.

关键词： Cameras UAV sizes Unmanned aerial vehicles Vision-based YOLOv5 and Faster-RCNN

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PriFace: a privacy-preserving face recognition framework under untrusted server

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2967-2979页

作者： Zhao, Shan Zhang, Lefeng Xiong, Ping School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan China Center for Cyber Security and Privacy School of Computer Science University of Technology Sydney Sydney Australia

Alongside the rapid development of artificial intelligence and mobile Internet, face recognition has gradually become a mainstream intelligent terminal authentication technology that is widely applied in security, finance, and social networking, among other fields. However, face images usually contain sensitive personal information, which can have serious consequences for users if this information is leaked or misused. Most existing face privacy protection methods are based on data encryption techniques or differential privacy mechanisms, which make it difficult to strike a good balance between privacy and usability. In this paper, we propose PriFace, a new privacy-preserving method for face recognition based on eigenfaces and locality-sensitive hashing (LSH). PriFace aims to achieve accurate and efficent face recognition with privacy preserved. It applies eigenface technique to reduce the dimension of face images for improving retrieval performance. With the assumption that the server is untrustworthy, PriFace uses the locality-sensitive hashing technique to introduce randomness into the face information for privacy preservation so that the server, or other adversaries, can not reconstruct any user’s face image. Experiments on real-world datasets demonstrate that the proposed PriFace can effectively preserve the privacy of face images and further outperforms the traditional methods. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Face recognition

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Deploying Hybrid Ensemble Machine Learning Techniques for Effective Cross-Site Scripting(XSS)Attack Detection

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computers, Materials & Continua 2024年第10期81卷 707-748页

作者： Noor Ullah Bacha Songfeng Lu Attiq Ur Rehman Muhammad Idrees Yazeed Yasin Ghadi Tahani Jaser Alahmadi School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430073China Department of Computer Science and Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain12555Abu Dhabi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving sophistication of cyber *** paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack *** approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in *** demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant ***,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time *** for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current *** innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.

关键词： Cross-site scripting machine learning XSS detection stacking ensemble learning hybrid learning

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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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