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Proceedings of the 2022 European Symposium on Software engineering

作者： Oluyinka Iyabo Omotosho Johnson Oyeleke Department of Cyber Security Science Ladoke Akintola University of Technology Nigeria Department of Computer Science Ladoke Akintola University of Technology Nigeria

ISBN: (纸本)9781450397308

The ability to understand facial expressions is an important part of nonverbal communication. The value in understanding facial expressions is to gather information about how the other person is feeling and guide our interaction accordingly. A person's ability to interpret emotions is very important for Effective communication. Recent researches show that emotional states and motivation directly or indirectly influences of student's learning process. This work is however a plunge into how systems can correctly detect recognize and classify human (Students) facial emotional expression through various image sensors, using Convolutional Neural Network (CNN). Dataset containing 28821 Face images were acquired. All images were used for training and testing using Convolutional Neural Network algorithm implemented in MATLAB software. 80% of the image dataset were used in training the system, while 20% were used for testing the system. The Trained CNN classifier classify image emotions using the Adam optimizer for higher accuracy.

关键词： Facial emotion Facial expression Convoluted neural network

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Towards an Automated Dissemination Process of cyber Threat Intelligence Data using STIX 6

Towards an Automated Dissemination Process of Cyber Threat I...

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6th International Workshop on Big Data and Information security, IWBIS 2021

作者： Briliyant, Obrina Candra Tirsa, Nusranto Pratama Hasditama, Maulana Andika Politeknik Siber Dan Sandi Negara Cyber Security Engineering Program Department of Cyber Security Bogor Indonesia National Cyber and Crypto Agency Depok Indonesia Faculty of Electrical Engineering and Informatics Budapest Computer Engineering Budapest Hungary

ISBN: (数字)9781665424516

ISBN: (纸本)9781665424516

cyber Threat Intelligence (CTI) is an actionable, evidence-based knowledge management system of cyber threats data. CTI consists of 3 (three) major parts, namely information gathering, analysis, and dissemination. To carry out dissemination in CTI, Structured Threat Information Expression (STIX) and Trusted Automated Exchange of Intelligence Information (TAXII) have been developed as open and interoperable standards for the CTI dissemination. An effective CTI is a CTI that can be integrated into the organization's security operations processes. Many organizations today runs CTI with a mechanism for reporting and distributing information in the dissemination process or known as cyber threat information sharing which has significant effects on security operations performance. In this study, user requirements analysis and design of cyber threat information sharing processes were carried out. From the results of the requirement analysis, a suitable business process re-engineering were proposed to accelerate the automation of CTI, especially in the information sharing part. Lastly, the designed processes were tested by simulating the processes using STIX and TAXII-based platform. The results of this study indicate that the designed processes can be run on STIX and TAXII-based platforms, ergo a step toward to a fully automated CTI. © 2021 IEEE.

关键词： Information dissemination

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PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound

arXiv

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arXiv 2024年

作者： Zhou, Man Su, Shuao Wang, Qian Li, Qi Zhou, Yuting Ma, Xiaojing Li, Zhengxiong School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China Institute for Network Sciences and Cyberspace Tsinghua University China Department of Computer Science and Engineering University of Colorado Denver United States Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

Fingerprint authentication has been extensively employed in contemporary identity verification systems owing to its rapidity and cost-effectiveness. Due to its widespread use, fingerprint leakage may cause sensitive information theft, enormous economic and personnel losses, and even a potential compromise of national security. As a fingerprint that can coincidentally match a specific proportion of the overall fingerprint population, MasterPrint rings the alarm bells for the security of fingerprint authentication. In this paper, we propose a new side-channel attack on the minutiae-based Automatic Fingerprint Identification System (AFIS), called PrintListener, which leverages users’ fingertip swiping actions on the screen to extract fingerprint pattern features (the first-level features) and synthesizes a stronger targeted PatternMasterPrint with potential second-level features. The attack scenario of PrintListener is extensive and covert. It only needs to record users’ fingertip friction sound and can be launched by leveraging a large number of social media platforms. Extensive experimental results in real-world scenarios show that Printlistener can significantly improve the attack potency of MasterPrint. © 2024, CC0.

关键词： Friction

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A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm 18th

A Cloud Computing User Experience Focused Load Balancing Met...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Luo, Jihai Dong, Chen Chen, Zhenyi Xu, Li Chen, Tianci College of Computer and Data Science - Fuzhou University Xueyuan Road Fujian Fuzhou360108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Department of Computer Science and Engineering University of South Florida TampaFL33620 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China

ISBN: (纸本)9789819998951

The development of the software and hardware has brought about the abundance and overflow of computing resources. Many Internet companies can lease idle computing resources based on the peak and valley cycles of usage load to provide IaaS service. After the surging development, more and more companies are gradually paying attention to the specific user experience in cloud computing. But there are few works about that aspect in load balance problem. Therefore, we consider the load balancing resource allocation problem, from the perspective of user experience, mainly based on geographical distance and regional Equilibrium, combined with user usage habits, in multiple service periods. A detailed mathematical definition has been established for this problem. This article proposes a mathematical model for the problem, along with an modified CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm, called WS-ESS-CMA-ES algorithm, to allocate computing resources and solve the above problem. The process of using the proposed algorithm to solve the problem of cloud computing resource allocation in real scenarios is simulated, and compared with some other algorithms. The experiment results show that our algorithm performs well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Cloud computing

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Diabetic Condition Classification using Supervised and Unsupervised Machine Learning Techniques: A Use Case for HealthTech Industry 4

Diabetic Condition Classification using Supervised and Unsup...

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4th IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Bongale, Anupkumar M. Biradar, Santoshkumar Pise, Priya Suke, Amita Priyadarshan Uke, Nilesh Dharrao, Deepak S. Symbiosis Institute Of Technology Department Of Aiml Pune Campus Pune India G H Raisoni College Of Engineering And Management Department Of Cyber Security And Data Science Pune Wagholi India Indira College Of Engineering And Management Department Of Ai And Ds Pune India Trinity Academy Of Engineering Pune India Symbiosis Institute Of Technology Department Of Cse Pune Campus Pune India

ISBN: (纸本)9798350384628

Diabetes is a very underestimated and, in many cases, an ignored disease. People do not understand the seriousness of diabetes. Many people think diabetes is just something because of which one cannot consume sweets. This, however, is not true. Diabetes is a very serious problem where symptoms can range from being very thirsty all the time to even death. There are many cases where diabetic people had to get their leg, or some other limb amputated because of it. Thus, people have to be made cognizant of diabetes' seriousness. This article dives deep into this disease and lists the different types of diabetes, and the way people can know if they are diabetic. Also, there is an in-depth literature review of research papers ranging from 2002-2020. Furthermore, a deep dive in the project of diabetes prediction is present. The techniques used to classify into 'diabetic' or 'non-diabetic' are Random Forest, Logistic Regression and K Nearest Neighbor algorithms which all belong to the supervised machine learning algorithms. Logistic Regression uses sigmoid function to classify into different classes, Random Forest approach uses multiple Decision Trees and the majority decision of them all for the final output. Moreover, K Nearest Neighbor technique uses the concept of Euclidean Distance to decide which class is suitable for any new coming datapoint. © 2023 IEEE.

关键词： Decision trees

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Securing Edge Computing Environments with a Novel Chaos Theory-Based Lightweight Encryption Algorithm for Real-Time Data Protection

Securing Edge Computing Environments with a Novel Chaos Theo...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： V. Vanitha G. Saritha K. Hemalatha P. Sathish P. Gayathri Devi Department of of Computer Science and Engineering Kumaraguru College of Technology Coimbatore India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Information Technology Sona College of Technology Salem India Department of Computer Science and Science (Cyber Security) Nandha Engineering College Erode India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

In the era of edge computing, securing data transmission has become paramount, especially given the real-time requirements and limited computational resources of edge devices. This study introduces a novel Chaos Theory-Based Lightweight Encryption Algorithm (CTBLEA) designed to enhance data protection in edge computing environments. The proposed algorithm leverages the inherent unpredictability of chaotic maps to secure data streams without imposing significant computational burdens. By adopting a lightweight encryption mechanism, CTBLEA ensures minimal latency and energy consumption, making it ideal for resource-constrained edge devices. Extensive experiments were conducted to evaluate the algorithm's performance. CTBLEA achieved an encryption and decryption time of 0.25 ms per kilobyte, showing a 35% reduction in latency compared to traditional AES encryption in similar environments. Furthermore, the algorithm demonstrated an energy consumption reduction of 40% while maintaining high security standards with an average key space of 10^l5, making brute-force attacks infeasible. security tests showed a correlation coefficient close to zero (0.003) between the original and encrypted data, indicating high randomness and minimal data correlation. Additionally, the algorithm's resistance to statistical and differential attacks was validated, with a 99.7% randomness score from the NIST suite tests. These results confirm the potential of CTBLEA for safeguarding real-time data in edge computing, offering a secure, efficient solution for next-generation loT and edge environments.

关键词： Chaos Wireless sensor networks Energy consumption Data protection Real-time systems Encryption Internet of Things Standards Edge computing Radiofrequency identification

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New Secure Sparse Inner Product with Applications to Machine Learning

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Xu, Guowen Xu, Shengmin Ning, Jianting Huang, Xinyi Li, Hongwei Lu, Rongxing The school of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Guangzhou511458 China The College of Computer and Cyber Security Fujian Normal University Fuzhou China The school of Computer Science University of New Brunswick Canada

Sparse inner product (SIP) has the attractive property of overhead being dominated by the intersection of inputs between parties, independent of the actual input size. It has intriguing prospects, especially for boosting machine learning on large-scale data, which are tangled with sparse data. In this paper, we investigate privacy-preserving SIP problems that have rarely been explored before. Specifically, we propose two concrete constructions, one requiring offline linear communication, which can be amortized across queries, while the other has sublinear overhead but relies on the more computationally expensive tool. Our approach exploits state-of-the-art cryptography tools including garbled Bloom filters (GBF) and Private Information Retrieval (PIR) as the cornerstone but carefully fuses them to obtain nontrivial overhead reductions. We provide formal security analysis of the proposed constructions and implement them into representative machine learning algorithms including k-nearest neighbors, naive Bayes classification, and logistic regression. Compared to existing efforts, our method achieves 2-50× speedup in runtime and up to 10× reduction in communication. © 2004-2012 IEEE. All rights reserved.

关键词： Adversarial machine learning

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An Untraceable Monitoring for Patients Using Wireless Medical Sensor Networks 4

An Untraceable Monitoring for Patients Using Wireless Medica...

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4th International Conference on Communication Technologies, ComTech 2023

作者： Khan, Muhammad Sibtain Ahmed Chaudhry, Shehzad Ashraf NUST Military College of Signals Department of Information Security Rawalpindi Pakistan Abu Dhabi University College of Engineering Department of Computer Science and Information Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350337211

Medical sensor network consists of an architecture in which medical sensor devices are implanted in human body to collect different types of sensitive data and share it on a network. These networks are connected with each other via wireless technology. Wireless medical sensor network (WMSN) came into existence with these medical sensor networks. Transmitted data from these WMSN's is then further processed in remotely located medical units. In the meanwhile, a registered medical professional of this network can access this information to rectify the patient's conditions via internet. This transmitted information is critical for the patient. It should be secured and private. It is very important to provide secure and private access to patient's data. Internet has an open infrastructure for every one as well as hackers whom can intrude such kind of sensitive information for their own benefits. It is a challenging issue for WMSNs to resist such kind of abrupt activity. A smaller number of protocols are being introduced in the literature, about WMSN, providing security and user authentication. Some of the protocols regarding WMSN are not standardized enough to provide user anonymity. This study is proposed to provide a protocol with potential to preserve user anonymity as well as mutual authentication for the mobile users. Precisely, this protocol is not only providing sufficient complexity but also robustness to security against unwanted threats. Moreover, it is providing a login phase, mutual authentication characteristics, and a phase to change the old password into a new one. © 2023 IEEE.

关键词： Sensitive data

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Integration of Hybrid Energy Storage System and Photovoltaic with Improving Microgrid Performance using Supervised Attention Multi-Scale Temporal Convolutional Network 3

Integration of Hybrid Energy Storage System and Photovoltaic...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Ramkumar, M. Siva Gopan, Gokul Arun, M. Giri, Jayant Al-Qawasmi, Khaled Dept of ECE SNS College of Technology India Saveetha University Chennai602105 India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Saveetha University TN Chennai602105 India Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331532420

Integrating Battery Energy Storage Systems (BESSs), Supercapacitor (SCs) and Photovoltaic (PV) panels into Microgrid (MGs) increases reliability through energy regulation and stabilization. However, the acquisition cost of the device is rather high, and it has constant maintenance costs, which can lead to a higher total annual cost. Furthermore, the deterioration of these storage components over time affects both the ability of the systems and the overall cost. In an attempt to address these drawbacks, this manuscript presents an optimal operating strategy of MG system with HESS and PV. The proposed method is Supervised Attention Multi-Scale Temporal Convolutional Network or SAMTCN for short. The general objective of the proposed method is to minimize the TAC of the MG system and to enhance the overall performance of the system. The SAMTCN that is proposed in this paper is employed to forecast the energy demand and supply trends prevailing in the MG, in order to fully exploit the HESS and PV panels used. Through the precise projection of energy variations, SAMTCN minimizes wastage of energy, energy storage losses are avoided, and general system efficiency is enhanced. Then, the proposed technique is with implemented in MATLAB and compared with other techniques that have been put forward. The proposed method outperforms all existing approaches such as Artificial Neural Network (ANN), Improved Deep Neural Network (IDNN), Fuzzy Logic Control (FLC), Artificial Rabbits Optimized Neural Network (ARONN), and Multi-objective Artificial Hummingbird Algorithm (MOAHA). The results demonstrate that SAMTCN outperforms these methods, achieving a TAC of $132,270.8 and an efficiency of 99.7%. These outcomes confirm the superiority of SAMTCN in reducing costs and maximizing the efficiency of MG systems with HESS and PV panels. © 2024 IEEE.

关键词： Costs

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cyberbullying Detection in Hinglish Language

Cyberbullying Detection in Hinglish Language

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Rajiv Kumar Vipin Rai Shivani Joshi Daksh Raj Amrita Amrita Department of Computer Science & Engineering Galgotias University Greater Noida U.P. India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida U.P. India Center for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

cyberbullying represents one of the most recent challenges stemming from the widespread adoption of social media platforms. With the surge in social media usage, the right to express oneself is being misused. Statistics reveal that approximately 36.7% of individuals believe that they have encountered cyberbullying at some point in their lives. These figures have more than doubled since 2007, and a noticeable increase occurred from 2018 to 2019, indicating an unfavorable trend. While there are existing solutions in the market aimed at mitigating this issue, many of them come with usage limitations or employ fewer effective algorithms. The primary objective of this work is to explore innovative methods for comprehending and automatically identifying instances of cyberbullying across online text messages and comments on different social media platforms. For achieving this goal, real-time data are gathered, including comments, headlines and text messages from trending posts on twitter, and devised a labelling study for categorizing cyberbullying. An analysis is presented of this labelled data, which includes an examination of the relationships between various features and both cyberbullying and cyberaggression. The core focus of this work is the early detection of cyberbullying as it originates, while being drafted in real-time. The aim of this work is to enhance the efficiency of cyberbullying detection by using various machine learning techniques and natural language processing.

关键词： Training Support vector machines Accuracy Reviews cyberbullying Feature extraction Real-time systems Classification algorithms Object recognition Surges

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