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Characterization and Properties of Bent-Negabent Functions

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： Algebra complete mapping polynomial Integral transforms Function theory, analysis quadratic Boolean functions vectorial Boolean function convolution generalized indirect sum sufficient condition nega-Hadamard transform Algebra, set theory, and graph theory polynomials Boolean functions classical convolution theorem Mathematical analysis Hadamard transforms bent-negabent functions necessary condition

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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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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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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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A Review Paper on Designing Intelligent Intrusion Detection System Using Deep Learning 11

A Review Paper on Designing Intelligent Intrusion Detection ...

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11th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Wasnik, Priyanka Chavhan, Nekita G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India G H Raisoni College of Engineering IoT and Cyber Security Department of Data Science Nagpur India

ISBN: (纸本)9798350348422

Machine learning techniques have been have proven to be more effective than conventional extensively used in the creation of intrusion detection systems (IDS) that can swiftly and automatically identify and classify cyber attacks at the host-and network-levels. A scalable solution is needed since destructive attacks are happening so quickly and are changing all the time. For more investigation, the malware community has access to a number of malware databases. The performance of several machine learning algorithms on a range of datasets that were made available to the general public, however, has not yet been thoroughly evaluated by any study. The publicly available malware datasets should be regularly updated and benchmarked due to the dynamic nature of malware and the continuously evolving attacking techniques. In this study, a deep neural network (DNN), a type of deep learning model, is examined in order to create a flexible and efficient IDS to identify and categorise unexpected and unanticipated cyber threats. In order to analyse a variety of datasets that have been produced throughout time using both static and dynamic methodologies, it is vital to take into account the rapid increase in attacks and the constant evolution of network behaviour. It is simpler to select the most effective algorithm for accurately predicting forthcoming cyber attacks with the help of this type of research. Many publicly available benchmark malware datasets are used to offer a thorough review of DNN and other conventional machine learning classifier studies. The KDDCup 99 dataset and the accompanying hyper parameter selection techniques are used to choose the ideal network parameters and topologies for DNNs. A learning rate of [0.01-0.5] is applied to every 1,000-epoch DNN experiment. A variety of datasets, including NSL-KDD, UNSW-NB15, Kyoto, WSN-DS, and CICIDS 2017, as well as the DNN model that performed well on KDDCup 99 are used to conduct the benchmark. Our DNN model trains a

关键词： Intrusion detection

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Lung Cancer Image Identify and Categorization using Artificial Neural Networks

Lung Cancer Image Identify and Categorization using Artifici...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Lingappa, Ediga Begum, D Jareena Pachouri, Priya Rashmi, Kuderu Varalakshmi, Thokala Tharuna Kumar, Anoop Mallareddy College of Engineering Maisammaguda Dept of Computer Science and Engineering TS Secunderabad500100 India CMR Institute of Technology Dept. of Artificial Intelligence and Data Science Bengaluru560037 India Malla Reddy University Dept of Cyber Security Hyderabad India

ISBN: (纸本)9798350381887

Lung cancer that is made up of small cells is called small cell lung cancer (SCLC). Smoking tobacco in any form-cigarettes, cigars, or pipes-is by far the most significant risk factor for developing lung cancer, though it is only one of many. Cancer of the lungs is known as lung cancer. When cells in the body multiply uncontrollably, cancer develops. If cancer cells enter the lymphatic system or the bloodstream, they can travel to other organs and spread the disease. Cheap and fast cancer diagnostics could save millions. Latestage cancer treatment is hard. Lung cancer is diagnosed non-invasively. Biopsy requires careful ANN architecture selection. This study proposes a diagnosis ANN model. ANN research is 'hot' in cardiology, cancer, and radiology. Medical research uses ANNs. Lung cancer diagnosis was tested. MR images assess lung health for the neural network. MATLAB created ANNs and processed images. Training data was extracted from MR images using Harlick texture features. MATLAB made ANNs. Elman Network exceeds ANNs with 90.30 %. Lung cancer can develop. MRIs detect lung cancer. MRIs show lung development. Many lung tumour detection research use machine learning and deep learning. MRI algorithms swiftly detect lung malignancies, improving patient therapy. Radiologists use these forecasts. The study evaluates a self-defined ANN for lung tumour identification. The radiologist needs time to find and remove the cancer from the MR. Technology improves performance and segmentation. Machines always predict and detect. Neural networks outperformed SVM, Random forest, regression, and others. © 2024 IEEE.

关键词： Lung cancer

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A Real-Time IoT-Enabled Biometric Attendance System

A Real-Time IoT-Enabled Biometric Attendance System

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International Conference on. Cryptology and Network security with Machine Learning, ICCNSML 2023

作者： Tripathi, Ashish Choudhary, Arjun Srivastava, Arun Kumar Kharbas, Vikash Kumar Shukla, Varun Department of CSE SCSE Galgotias University Greater Noida India Centre for Cyber Security Sardar Patel University of Police Security and Criminal Justice Jodhpur India Department of Computer Science and Engineering Lloyd Institute of Engineering & Technology Greater Noida India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Kanpur India

ISBN: (纸本)9789819706402

Nowadays, biometric attendance system is applicable in various organizations to ensure the person's identity. The system takes parameters such as person's voice, fingerprint, retina, and face to authenticate the person. In order to track students’ daily attendance when they enroll in college, this article provides a biometric attendance system that uses blockchain technology. Both the daily and cumulative attendance of each student over the course of a chosen period of time, or month, is displayed by this system. The proposed system is designed to provide the cost-effective, systematic, and feasible solution to keep real-time attendance of the students against the existing manual system. Also, a webpage is designed for the same to provide a user-friendly and flexible platform to the students and faculty both to check the student's attendance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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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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