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

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

ISBN: (纸本)9798350369106

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks. © 2024 IEEE.

关键词： Aircraft accidents

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AI-Driven Solutions for Cloud security Implementing Intelligent Threat Detection and Mitigation Strategies

AI-Driven Solutions for Cloud Security Implementing Intellig...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Yasani, Rajashekar Reddy Prasad, Putalpattu Muni Srinivas, Pattlola Reddy, N V Raja Sekhar Jawarkar, Parag Raghunath, Vedaprada Cloud Security Cloud Computing Cyber Security India University of Rome Tor Vergata Department of Industrial Engineering Italy Department of Computer Science and Engineering Telangana India Mlr Institute of Technology Department of It India Shri Ramdeobaba College of Engineering and Management Electronics Engineering Nagpur India Visveswaraya Technological University India

ISBN: (纸本)9798331508432

Adapting threat detection and response procedures is essential for businesses to stay ahead of the constantly developing cyberattack landscape. Researching cutting-edge tools like XDR, SIEM, SOAR, and NDR - which shed light on the dynamic realm of detection and response systems - is crucial. Other areas of investigation include real-time monitoring and network forensics. security and threat detection have both been greatly enhanced since businesses began storing their apps and data in the cloud. In order to safeguard vulnerable network infrastructures in cloud settings against advanced attacks, traditional security procedures must be revised. Artificial intelligence (AI) is used to learn more about this problem so that threat reaction and identification can be done more quickly and accurately. Artificial intelligence has changed cloud security and threat identification, as this study shows. Because cyberattacks on cloud infrastructures and service providers are becoming more common, strong security steps that are also easy to use are still needed. This piece will talk about how cloud security and AI operations work together, with a focus on how this collaboration speeds up the time it takes to respond to incidents. The paper will also show how this relationship helps a company improve its defences and lessen the impact of security events. Organisations that want to keep up with the constantly changing danger landscape need to use cloud security and AI and understand how the two work together. This is because it is necessary to keep a security stance that is both flexible and strong. © 2024 IEEE.

关键词： Cloud security

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A novel coarse-grained knowledge graph embedding framework for platform risk identification from relational data

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Neural Computing and Applications 2025年第18期37卷 12231-12251页

作者： Zhang, Qi Wang, Shicheng Wang, Lihong Sheng, Jiawei Guo, Shu Li, Chen He, Min Zhang, Renqiang School of Economics and Management Beihang University Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China School of Computer Science and Engineering Beihang University Beijing China

Platform risk identification from relational data aims to determine whether the target platforms are risky or not. However, in such a scenario, the relation data involve massive types of relations and numerous numerical-aware relations composed of attributes and infinite continuous values are challenges in this field. It is paramount to capture semantic information of relations and precise control power, drastically affecting the power of learning and the performance of identifying risky platforms. Recent financial risk studies often employed graph neural networks (GNNs) to model relational data, which fall short of capturing the intricacies of such complex relationships resulting in incorrect identification of platforms. To solve this problem, we propose a novel coarse-grained knowledge graph embedding (CKGE) framework that integrates coarse-grained knowledge graph construction and adopts five well-known knowledge graph embedding methods and ensemble learning. The extensive experimental results based on a real-world peer-to-peer lending platform dataset demonstrate that the CKGE framework achieves state of the art over GNNs for platform risk identification. Our study highlights the importance of utilizing the relational data of multi-type and multi-numerical relations for revealing platform risk situations and provides an alternative solution for identifying platform risk. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Knowledge graph

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Characterization and Properties of Bent-Negabent Functions

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Chinese Journal of Electronics 2022年第4期31卷 786-792页

作者： JIANG Niu ZHAO Min YANG Zhiyao ZHUO Zepeng CHEN Guolong School of Mathematical Sciences Huaibei Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University School of Cyber Science University of Science and Technology of China School of Computer Engineering Bengbu University

A further characterization of the bentnegabent functions is presented. Based on the concept of complete mapping polynomial, we provide a necessary and sufficient condition for a class of quadratic Boolean functions to be bent-negabent. A new characterization of negabent functions can be described by using the parity of Hamming weight. We further generalize the classical convolution theorem and give the nega-Hadamard transform of the composition of a Boolean function and a vectorial Boolean function. The nega-Hadamard transform of a generalized indirect sum is calculated by this composition method.

关键词： Boolean function Bent-negabent function Walsh-Hadamard transform Nega-Hadamard transform

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Unveiling the Nexus: Exploring the Societal Implications of AI-Driven cybersecurity and Data Analysis 13

Unveiling the Nexus: Exploring the Societal Implications of ...

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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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Get A Sense of Accomplishment in Doing Exercises: A Reinforcement Learning Perspective 25

Get A Sense of Accomplishment in Doing Exercises: A Reinforc...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Niu, Songdeng Cao, Sheng University of Electronic Science and Technology of China School of Computer Science and Engineering Institute for Cyber Security China

ISBN: (纸本)9781665405270

Doing exercises is vital to improving students' learning effects. Setting reasonable rewards in the process of doing exercises can promote students' problem-solving interests. Reinforcement learning has been employed on optimal instructional sequencing in education for decades. Existing reinforcement-learning-induced policies in instructional sequencing mainly pay attention to how students take actions in learning activities while attention to the setting of rewards in doing exercises has been little. To redress this imbalance, we first propose a set of calculation formulas of reward function closely associated with the difficulty of each exercise item and the consuming-time of each student. We put forward the confidence index of students as another important parameter in our reward function, which represents how sure each student is that he/she could do the exercises right. We then propose and prove a reward shaping scheme based on dynamic potential function, which can not only ensure invariance of the optimal policy of acquiring exercise-doing rewards but also accelerate the convergence of the exercise-doing process. This scheme provides a new paradigm for motivating students to do exercises in the e-learning environment, which helps to enhance students' learning enthusiasm, so as to improve their learning efficiencies impressively. © 2022 IEEE.

关键词： Reinforcement learning

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A Behavior-Aware Scheme for Personalized Credit Computing 25

A Behavior-Aware Scheme for Personalized Credit Computing

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Dang, Sixuan Cao, Sheng Institute for Cyber Security University of Electronic Science and Technology of China School of Computer Science and Engineering China

ISBN: (纸本)9781665405270

Quality of Service (QoS) and Quality of Experience (QoE) are employed to characterize network performance to improve network resource utilization in web/internet-based services. However, user credit that also contributes to efficient network resources allocation is ignored. Analyzing users' behaviors to estimate user credit is a common denominator studied in decades, yet they analyze incomplete attributes of users' behaviors. A general user credit computing method is urgent and challenging. Thus, we propose Quality of Credit (QoC) and define three metrics to systematically achieve user credit computing. Detailed QoC calculation and differential pricing scheme are proposed to regulate users' behaviors to improve network resource utilization, which is defined as Credit Level Agreement (CLA). We conduct extensive experiments to demonstrate the effectiveness and feasibility of QoC. Actually, QoS and QoE describe network performance metrics, while QoC depicts the credibility of users, so that they can complement each other, and jointly provide support for collaborative computing and various applications at a higher perspective than ever. © 2022 IEEE.

关键词： Network performance

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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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GAN-Based Anomaly Intrusion Detection for Industrial Controller System 1

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World Conference on Information Systems for Business Management, ISBM 2023

作者： Riya V. Raj Sangeetha, V. Amritha, P.P. TIFAC-CORE in Cyber Security Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Information Systems BITS Pilani Pilani India

ISBN: (数字)9789819983469

ISBN: (纸本)9789819983452

Industrial controller system (ICS) is becoming more and more important in daily lives. In recent years, ICS has become more frequent targets of cyberattacks. In addition to the system, the environment is also significantly impacted by the ICS cyberattack. The main aim of ICS intrusion detection is a process of anomaly detection because cyberthreats cause anomalies to occur in the ICS and components under its control. With a machine learning or deep learning aid, the IDS can produce precise detection outcomes. Though machine learning models can be used to detect cyberattacks, there is a challenge in handling imbalanced real-time data. In this paper, we have implemented generative adversarial networks, to resolve the issue of imbalance in datasets by creating class-specific adversarial samples and further detecting anomalies with greater efficiency. This proposed method is tested on Secure Water Treatment Dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Blockchain Based security Enabled Smart Contract for Energy Management 16

Blockchain Based Security Enabled Smart Contract for Energy ...

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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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