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检索条件"机构=Cyber Security in Computer Science and Engineering"

共 2596 条记录，以下是431-440 订阅

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Optimizing Real-time Task Scheduling in Cloud-based AI Systems using Genetic Algorithms 7

Optimizing Real-time Task Scheduling in Cloud-based AI Syste...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Mohammed, Abdul Sajid Mallikarjunaradhya, Vinay Sreeramulu, Madhusudhan Dasari Boddapati, Nagesh Jiwani, Nasmin Natarajan, Yuvaraj University of the Cumberlands Department of School of Computer and Information Sciences KY United States Thomson Reuters Toronto Canada American Express Department of Cyber Security United States Microsoft Department of Computer Science CharlotteNC United States University of The Cumberlands Department of Information Technology WilliamsburgKY United States Sri Shakthi Institute of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350350067

Artificial Intelligence (AI) has also started to be regularly introduced in cloud-based systems as it can help improve performance and skyrocket efficiency. It is a challenge for such systems to schedule tasks in real time, and computing sources need to be allocated promptly and efficiently. The current scheduling mechanisms being relatively static under varying system conditions leads to inefficient allocation with a slight (to severe) degradation in performance. This project suggests that Genetic Algorithms (GA) be employed for real-time task scheduling optimization in AI services on the cloud. GAs are robust optimization methods based on fundamental natural selection and survival of the fittest principles, thus well suited for solving complex issues like task scheduling in dynamic environments. The LC scheduling uses a GA-based approach to form a group of shuttle schedules (candidate emergency transportation solution) where the fittest one is selected and executed using genetic operators like selection, crossover, mutation, etc. It allows the system to adjust resources more effectively for its current state, leading to better performance and scalability. © 2024 IEEE.

关键词： Genetic algorithms

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Design of Chatbot to Prevent cyberbullying from Social Media

Design of Chatbot to Prevent Cyberbullying from Social Media

引用

Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Erin Sanu Medha Mummigatti Mohana Computer Science and Engineering (Data Science) RV College of Engineering® Bengaluru India Computer Science and Engineering (Cyber Security) RV College of Engineering® Bengaluru India

In the present world, social media has greatly influenced our lives but with the increase in usage of social media, people get exposed to its adverse impacts as well. The common and major impact is cyberbullying, which tends to have tremendous negative impacts on the victim's life. Hence, detecting cyberbullying has now become a crucial task. This research study proposes a novel chatbot design to identify the occurrences of cyberbullying based on user's input and provide emotive support or guidance as required. It also offers data on various aspects and domains of cyberbullying, thus helping to create a safer online environment.

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IoT-Driven Robotic System for Enhanced Nighttime Perimeter security and Real-Time Surveillance in Smart Environments

IoT-Driven Robotic System for Enhanced Nighttime Perimeter S...

引用

Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： J. Jayachitra A. Aslam Khan V. Yuvaraj Computer Science Engineering (Cyber Security) IFET College of Engineering Villupuram Tamil Nadu Information Technology IFET College of Engineering Villupuram Tamil Nadu

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

As a result of the growing demand for automated security, this research study proposes the design and development of a night patrol robot with Raspberry Pi Zero 2 W. The main purpose of the robot is to monitor the areas chosen on its own protecting and checking out safety measures on its own with little or no help from humans. The system comprises of an ultrasonic obstacle detector, an infrared navigational sensor, a monitoring camera and noise harassment detector. Employing AI design, the robot first listens to the sounds happening around and identifies some suspicious actions, and then turns the video on. High-speed data transfer is through Wi-Fi allows real time monitoring and controlling of the robot from a distance. The system's hardware components include an IR sensor, Arduino Uno, Raspberry Pi Zero 2 W, an L298N motor driver, and a specific camera. The paper provides an overview of the system and its design, the implemented algorithms, and some first experimental results of the proposed system, which can be seen as a major further development of the field of autonomous security for both homes and business.

关键词： Navigation Robot vision systems Detectors Robot sensing systems Real-time systems Acoustics security Object recognition Robots Wireless fidelity

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cyber security Testbed: Keyloggers and Data Visualization on Keyloggers - A Case Study

Cyber Security Testbed: Keyloggers and Data Visualization on...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Prasiddha Bhat Namratha H J Mohana Computer Science and Engineering (Data Science) RV College of Engineering® Bengaluru India Computer Science and Engineering (Cyber Security) RV College of Engineering® Bengaluru India

A cyber security Testbed (CST) is used to simulate cyber-attacks, test and evaluate existing and new strategies. It is a safe space to fix vulnerabilities and enhance security. Keyloggers are malicious tools that record keystrokes. Proposed work implemented a case study on the cybersecurity testbeds available, their significance and aims to describe the essential features of an effective cybersecurity testbed. And also contracts with a study on keyloggers, a simulation and analysis of its working. Further entails a data visualization report on two distinct keylogger datasets to gain a meaningful understanding of keyloggers.

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End-to-End Transformer Architecture with Novel Ensemble Learning Method Integrating CT Scans and Clinical Narratives for Brain Stroke Diagnosis 1

引用

41st computer Graphics International Conference, CGI 2024

作者： Abdul Wahid, Junaid Ayoub, Muhammad Xu, Mingliang Jiang, Xiaoheng Lei, Shi Li, Lifeng Hussain, Shabir Khawaja, Ashfaque Gao, Yufei School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Electronic Information and Electrical Engineering Shanghai Jiaotong University Shanghai200240 China School of Computer Science and Engineering Central South University Hunan Changsha336017 China School of Cyber Space and Security Zhengzhou University Zhengzhou450002 China Institute of Biopharmaceutical and Health Engineering Tsinghua Shenzhen International Graduate School Shenzhen100084 China

ISBN: (数字)9783031820243

ISBN: (纸本)9783031820236

This study is an extension of our previously published manuscript [2]. Building upon the foundations laid in our earlier work, this research further refines and extends the transformer-based approach to enhance its diagnostic capabilities. The focus remains on the integration of multi-modal information, combining both textual clinical narratives and imaging data from multi slice CT scans, to provide a more comprehensive and accurate diagnosis of brain strokes. In addition, we introduce a new ensemble learning approach based on logic gates. This innovative method combines information from clinical narratives and CT scans to enhance the diagnostic capabilities of our framework, marking a significant evolution in our approach to advancing precision in brain stroke detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computerized tomography

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Leveraging OpenStack-based Private Cloud for Intrusion Detection Using Deep Learning Techniques

Leveraging OpenStack-based Private Cloud for Intrusion Detec...

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Women in Computing (InCoWoCo), International Conference for

作者： Rahul Roshan G Vishal S Salanke Nagasundari S Department of Computer Science and Engineering Research Center for Information Security Forensics and Cyber Resilience PES University Bangalore India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

OpenStack is a cloud platform that offers infrastructure services, providing access to computing, storage, and network resources. However, due to its open-source nature, the cloud environment can be vulnerable to security threats. OpenStack security measures are generally applied to specific services, rather than being integrated comprehensively across the entire cloud environment. Despite offering flexibility and scalability, the widespread use of OpenStack calls for robust security solutions. Current intrusion detection systems often struggle to adapt to constantly evolving traffic patterns and attack vectors in the OpenStack environment. This study introduces a two-stage deep learning framework to create a robust Intrusion Detection System (IDS) for OpenStack-based private clouds. The proposed framework efficiently analyzes network traffic data in these clouds by implementing dimensionality reduction and anomaly detection techniques. It utilizes a network-based IDS, trained on two publicly available datasets, which enables a broader detection range of known attack patterns. The results indicate that the OpenStack IDS significantly enhances OpenStack's security, addressing the limitations of generic IDS solutions.

关键词： Deep learning Dimensionality reduction Cloud computing Scalability Current measurement Intrusion detection Telecommunication traffic Traffic control Vectors security

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BadTTS: Identifying Vulnerabilities in Neural Text-to-Speech Models

BadTTS: Identifying Vulnerabilities in Neural Text-to-Speech...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Rui Zhang Hongwei Li Wenbo Jiang Ran Zhang Jiaming He School of Computer Science and Engineering University of Electronic Science and Technology of China College of Computer Science and Cyber Security (Oxford Brookes College) Chengdu University of Technology China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

With the widespread use of deep learning systems in many applications, adversaries have strong incentives to perform attacks against these systems for their adversarial purposes. Reports have indicated that backdoor attacks on deep neural networks represent a novel form of threat. In this attack, the adversary will inject backdoors into the benign model and then mislead the model to classify the input containing backdoor triggers as a target label specified by the adversary. Existing research mainly focuses on backdoor attacks in image and text models, little attention has been paid to the backdoor attacks on text-to-speech (TTS) models. We conduct a systematic investigation of backdoor attacks on text-to-speech models and propose BadTTS, the first backdoor attack against TTS models, which is a general backdoor attack framework that tampers with input texts in three semantic levels to generate malicious output speech, including Char-Backdoor, Word-Backdoor, and Sentence-Backdoor. Our method not only efficiently injects backdoors into a TTS model but is also stealthy and has little impact on the synthesized speech quality. We implement the backdoor attack in a black-box fine-tuning setting, where the adversary has no knowledge of model architectures except for a small amount of training data. We perform empirical experiments on three representative and widely studied TTS models, indicating that backdoors can be injected into TTS models within a few fine-tuning steps. Additionally, we conduct experiments to explore the impact of different types of triggers, as well as the intermediate outputs of models, which provide insights for potential defenses against backdoor attacks.

关键词： Training Deep learning Systematics Semantics Training data Closed box Artificial neural networks Data models Text to speech Global communication

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Fuzzy-Based Weighted Federated Machine Learning Approach for Sustainable Energy Management with IoE Integration

Fuzzy-Based Weighted Federated Machine Learning Approach for...

引用

2024 Systems and Information engineering Design Symposium, SIEDS 2024

作者： Ghazal, Taher M. Iqbal Janjua, Jamshaid Abbas, Sagheer Fatima, Areej Saleem, Muhammad Khan, Muhammad Adnan Alqarafi, Abdulrahman Khalifa University Centre for Cyber Physical Systems Computer Science Department Abu Dhabi United Arab Emirates Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Selangor Malaysia National College of Business Administration and Economics School of Computer Sciences Lahore Pakistan Al-Khawarizmi Institute of Computer Science Lahore Pakistan Bahria University Department of Computer Science Lahore Pakistan LGU Department of Computer Science Lahore Pakistan Minhaj University School of Computer Science Lahore Pakistan Skyline University College School of Computing Sharjah United Arab Emirates Riphah International University Riphah School of Computing and Innovation Lahore Pakistan Taibah University College of Computer Science and Engineering Medina42353 Saudi Arabia

ISBN: (纸本)9798350385144

The considerable surge in energy consumption due to population growth and the use of new technology has posed significant challenges to energy security and the environment. Managing energy consumption on the consumer side is crucial as the demand for energy continues to increase. Consumers need to monitor their daily usage and understand consumption standards to prepare themselves for energy efficiency and cost savings. To address these challenges, a Smart Energy Management System (SEMS) based on the Internet of Energy (IoE) has been developed. This system uses day-Ahead planning and accurate energy availability forecasting to manage energy demand in a smart environment with high sustainable energy penetration. The Fuzzy based Weighted Federated Learning (FWFML) approach is integrated into the system to optimize energy management. The SEMS system monitors energy usage at the user level, and a review of management plans has been introduced to improve energy efficiency in terms of 93.5% accuracy, and 6.5% miss-rate. This research work also highlights future challenges in developing SEMS to increase energy efficiency in a more robust manner. © 2024 IEEE.

关键词： Energy management systems

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Three-dimensional dynamic uncertainty semantic SLAM method for a production workshop

Three-dimensional dynamic uncertainty semantic SLAM method f...

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作者： Gou, Rongsong Chen, Guangzhu Yan, Chengliang Pu, Xin Wu, Yuanyuan Tang, Yuan College of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu China College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China

Simultaneous localization and mapping (SLAM) is the basis for intelligent navigation and intelligent security of mobile robots in a workshop. However, there are numerous dynamic objects in a workshop, such as robots and operators, which can decrease the accuracy of robot localization and mapping. To solve this problem, this paper proposes a three-dimensional dynamic semantic system named the PW_SLAM. First, the production workshop-oriented lightweight semantic segmentation network (PWnet) is integrated with the SLAM system by a robot operating system(ROS) to provide semantic information. Next, a dynamic uncertainty keypoint classifier(DUKC) is proposed to filter the dynamic keypoints and improve the localization accuracy. Then, the map is stored in the form of an octree, and a dynamic object map filter are designed to filter the dynamic objects in the octree map. The proposed system is evaluated on the TUM RGB-D public datasets and the results show that the RMSE of absolute trajectory error in PW_SLAM is decreased by 92.80%, 98.85%, 87.21% compared to that of ORB_SLAM3 on the TUM highly dynamic datasets "fr3/walking_xyz", "fr3/walking_static", and "fr3/walking_rpy", respectively. In addition, the PW_SLAM is also applied to the real workshop environment, and the results show that it can effectively eliminate dynamic objects and build a static octree map of a workshop environment effectively. © 2022 Elsevier Ltd

关键词： Robot Operating System

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

Advancing Android Malware Detection with BioSentinel Neural ...

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International Conference on Computing for Sustainable Global Development (INDIACom)

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

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.

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