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A Machine Learning-Based Approach for Anomaly Detection for Secure cloud computing Environments

A Machine Learning-Based Approach for Anomaly Detection for ...

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2023 international conference on Intelligent Data communication Technologies and Internet of Things, IDCIoT 2023

作者： Parameswarappa, Priya Shah, Taral Lanke, Govinda Rajulu University of the Cumberlands School of Technology United States Pace Univerty School of Information System United States Birla Institute of Technology and Science Data Science and Engineering India

ISBN: (纸本)9781665474511

The concept of "cloud computing"has been presented as a promising strategy for providing online service hosting and distribution. Despite the widespread adoption of cloud computing, security remains a top priority. Several secure methods have been devised to safeguard communication in such scenarios, with the majority of these solutions based on attack signatures. Unfortunately, these technologies cannot always detect every possible danger. A machine learning method was recently outlined. The judgment could be inaccurate if the training set is missing examples from a certain category. In this research, an innovative firewall strategy for safe cloud-based computing is presented using machine learning system. The proposed methods estimate the final assault category categorization by combining the judgments of the nodes from the past with the decision of the machine learning algorithm in the present, a technique termed most frequent decision. Both learning efficiency and system precision are improved by this method. Our results are based on UNSW-NB-15, a publicly available dataset. According to the evidence provided by our data, it improves anomaly detection by 97.68 percent. A Machine Learning-Based Approach for Anomaly Detection for Secure cloud computing Environments © 2023 IEEE.

关键词： Anomaly detection

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Naive Bayes-based Autonomous Illumination System for Urban Green Spaces with cloud Assistance 4

Naive Bayes-based Autonomous Illumination System for Urban G...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Tamilselvi, M. Neelima, A. Amarnath, J. Jegan Lalitha, K. Ganesh, E.N. Thamizhamuthu, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Sri Sairam Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India R&d Department of Electronics and Communication Engineering Avadi Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Improving urban cities' quality of life is one of the primary functions of urban green spaces. Efficient lighting solutions in these areas enhance safety and ambiance. This research introduces a new method for autonomously controlling the lighting in urban parks with the help of a Naive Bayes classifier and cloud computing. The system uses information gathered from several sensors to adapt the brightness of the lights in response to changes in the surrounding environment and the presence or absence of people. The system can generate accurate choices in real-time using machine learning methods, including the Naive Bayes algorithm. With the help of the cloud, remote monitoring and administration are made easier, which improves efficiency and scalability. The experimental findings prove that the proposed approach is reliable and efficient in enhancing urban green areas' energy efficiency and user enjoyment. Intelligent algorithms combined with cloud computing provide a viable option for smart city lighting control, opening the door to more responsive and environmentally friendly cityscapes. © 2024 IEEE.

关键词： Ambiance Energy efficiency Environmental conditions Lighting management Real-time control Remote monitoring Safety

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Enhancing cloud Security through Log Aggregation and Real-Time Alerts 8

Enhancing Cloud Security through Log Aggregation and Real-Ti...

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8th international conference on Electronics, communication and Aerospace Technology, ICECA 2024

作者： Sujan, Gontla Venkat Neha, Hothur Ranga Shankar, Cheekati Mani Kavitha, C.R. Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350367904

cloud computing is widely adopted by the IT sector in many industry applications, and the immensity of the risk proportion inclines with its adoption. The number and irregularities in malicious cyber-attacks has multiplied gradually demanding a robust security solution. The project aims to cope with the growing number and complexity of cyber threats by resolving the immediate need for dependable cloud security solutions amid cloud computing's rapid adoption. In the era of the networked and interconnectivity, promptly responding to the cyberthreats and having embracing data visualization in the cybersecurity program would make it possible for the business to prevent the cyberattacks, control the data flow, and avoid compliance violations, and safeguard the on-going company activities. The proposed approach implements industry standard applications such as Elasticsearch, AWS cloudWatch, and Kibana applications to act proactively by detecting and mitigating cyber threats. By processing and analyzing real-time log data in a cloud environment, they can minimize the effect of cyber-attacks and keep their cloud systems intact. The competence of the system to conduct effective forensic analysis and proactive threat hunting expands further with integration of ELK stack. This enhances security teams' ability to go on par with changing threats and operate strong cyber defenses. The proposed methodology aims to deploy and implement a secure file upload system utilizing AWS services. © 2024 IEEE.

关键词： cloud security

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ListenBot: Augmented Reality Based Speech To Sign Language Conversion 14

ListenBot: Augmented Reality Based Speech To Sign Language C...

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14th international conference on cloud computing, Data Science and engineering, Confluence 2024

作者： Wyawahare, Medha Adbale, Tanmay Jorvekar, Vaibhavi Vishwakarma Institute of Technology Department of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798350344837

Hearing impairment poses substantial challenges for individuals, hindering their effective communication and interaction within society. This paper addresses these challenges through the development of an application tailored for people with hearing disabilities. The application serves as a pivotal tool by retrieving audio input and transforming it into dynamic sign language animations, thereby facilitating seamless communication. The system captures spoken language and translates it into expressive sign language animations. This transformative approach not only enhances the communication abilities of individuals with hearing impairments but also fosters a more inclusive environment for them. The underlying technology incorporates Convolutional Neural Networks (CNN) for precise speech classification, ensuring accurate recognition of spoken words. In addition, the model employs Recurrent Neural Networks (RNN) to capture contextual speech, enhancing the authenticity and clarity of the sign language representation. The paper compares the results of different models generated using LSTM, GRU and machine learning methods consisting HMM and GMM. The final application is crafted using Unity using AR applications, guaranteeing a user-friendly interface that promotes accessibility. By seamlessly translating spoken language into visually comprehensible sign language, this application empowers individuals with hearing impairments, fostering a more inclusive and connected society. © 2024 IEEE.

关键词： Long short-term memory

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Data Aggregation in Internet of Things (DAIT) with Edge computing 5

Data Aggregation in Internet of Things (DAIT) with Edge Comp...

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5th international conference on Electronics and Sustainable communication Systems, ICESC 2024

作者： Yaragal, Achyut Bendigeri, Kirankumar Basaveshwara Engineering College Department of Electronics & Communication Karnataka Bagalkote India Vijayapura Karnataka Belagavi590018 India Department of Electronics & Communication Karnataka Belagavi590018 India

ISBN: (纸本)9798350379945

Data plays a crucial role in the field of computation. The aggregation of data facilitates the processing of data in order to obtain refined information, which can then be utilized for performing analysis and computation, leading to valuable outcomes. Numerous techniques exist for data aggregation in WSN, and currently this study investigates the aggregation of data in the domain of the Internet of Things. This investigation involves exploring various computation methods such as cloud, fog and edge computing. The combination of wireless sensor networks (WSNs) and the Internet of Things (IoT) has become increasingly popular, because it provides real-time application solutions by facilitating communication between physical objects and sensor nodes in an integrated technology environment. Data aggregation with edge computing, by decentralizing data processing and bringing it closer to data sources, offers a promising solution. This study investigates the implementation of data aggregation in WSN and IoT for any applications using Edge computing, aiming to enhance data processing efficiency and reduce latency thereby extending the operational lifespan of the network. © 2024 IEEE.

关键词： Data aggregation

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Enhanced Honey Badger Algorithm for Resource Allocation and Task Scheduling in cloud Environment 4

Enhanced Honey Badger Algorithm for Resource Allocation and ...

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4th international conference on Smart Electronics and communication, ICOSEC 2023

作者： Rajagopal, R. Arunarani, A.R. Arivarasi, A. Ingle, Anup Ravichandran, T. Prakash, R. Vijaya Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India School of Computing College of Engineering and Technology Srm Instutite of Science and Technology Department of Compuational Intelligence Tamilnadu Chennai India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Karnataka Bengaluru India Vishwakarma Institute of Information Technology Department of Electronics and Telecommunications Maharashtra Pune India Karpagam College of Engineering Department of Computer Science and Engineering Tamilnadu Coimbatore India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350300888

cloud computing is a technology that offers dynamic resources to the users with enhanced scalability and flexibility. The major concerns in cloud environment that has direct impact on the throughput of cloud system and contentment of cloud users is the problem of task scheduling and resource allocation. The time taken to execute the tasks and cost incurred for the computation are the significant objectives that affect the performance of the cloud system. This work proposes a multi-objective task scheduling and resource allocation technique using meta-heuristic optimization algorithm. Enhanced Honey Badger algorithm (EHBA) is employed to schedule the tasks and allocate computing resources effectively while minimizing the time and cost objectives. The performance of the proposed technique is assessed in a simulation environment, cloudSim, which mimics the settings of real cloud computing system. Various measures such as Time-to-Execute, Cost-to-Compute, Task-to-Resource utilization and Time-to-Respond are used to assess the performance of the suggested EHBA method for efficient task scheduling and resource allocation. The experimental results produced by the proposed method is also compared against the state-of-the-art studies that employ meta-heuristic optimization algorithms. The outcomes revealed that EHBA outperformed other methods by executing the tasks in a minimum time with reduced cost and maximum utilization of the computing resources. © 2023 IEEE.

关键词： Resource allocation

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Gaining Perspective into cloud computing: A Business-Oriented Approach 4

Gaining Perspective into Cloud Computing: A Business-Oriente...

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4th IEEE international conference on computing, communication, and Intelligent Systems, ICCCIS 2023

作者： Ahmad, Javed Ahmad, Shahnawaj Arif, Mohd. Nazim, Mohd Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida201110 India Bennett University Department of Computer Science and Engineering Uttar Pradesh Greater Noida201310 India School of Computing Science and Engineering Department of Computer Applications Uttar Pradesh Greater Noida203201 India Noida Institute of Engineering and Technology Department of CSE Uttar Pradesh Greater Noida201306 India

ISBN: (纸本)9798350306118

computing may be a profoundly examined subject within the technological and financial world, and numerous huge players in the software industry are working for the improvement of computing services. With the help of a literature review, this work has identified that the classification of cloud computing (CC) does not specify how to classify CC based on industry-driven professional articles, as most of this study has been based on academic distributions. This may limit the ability of the research study to provide a consolidated view of the most current advances in this topic. CC has recently captured the attention of scholars, financial professionals, and specialists. Many of these experts have proposed a variety of applications based on various cloud-based techniques, frameworks, and essentials, which have led to different interpretations, specifications, deployment models, and service models. Therefore, a complete characterization and classification of CC are needed to address the above issue. The ultimate purpose of this research is to classify CC in the literature, including the set of security concerns, history, concepts, technologies, and challenges. © 2023 IEEE.

关键词： cloud security

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Optimising Makespan Minimization and Performance at the Same Time with a Dynamic cloud Task Scheduling Comparative Study of MOSOS and PSO 5

Optimising Makespan Minimization and Performance at the Same...

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5th IEEE international conference on computing, Power, and communication Technologies, IC2PCT 2024

作者： Jain, Anegha Nath, Megha Rama Parvathy, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

The practice of allocating available resources, such as personnel, money, time, or materials, to different tasks, projects, or activities is known as resource allocation. Effective resource allocation is crucial for optimizing productivity, achieving goals, and maximizing efficiency within an organization or project. Resource allocation in cloud computing can face several challenges. These include over-allocation, where resources are provisioned excessively, leading to increased cost, and under-allocation, which can result in performance bottlenecks. cloud environments often have to support a variety of different workloads, each with its own resource requirements. This can make it difficult to allocate resources efficiently whilst maintaining the Quality of Service (QoS). Tackling such issues, our paper demonstrates the workings of the MOSOS (multi-objective symbiotic organism search) algorithm to optimize the performance and cut down on makespan and cost. We simulate MOSOS alongside the Particle Swarm Optimization Algorithm (PSO) to understand whether PSO outperforms MOSOS or whether MOSOS accomplishes similar results to PSO. The results back up MOSOS in terms of makespan but fail when compared against PSO's execution times. We find that PSO uses a lesser number of parameters and makes fewer function calls as opposed to MOSOS. Thus, we can argue that in dynamic cloud environments where time is essential, PSO may perform better as compared to MOSOS. © 2024 IEEE.

关键词： cloud computing Evolutionary algorithms makespan optimization resource scheduling

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Using SDN to Enhance Load Balancing in cloud computing: An Overview and Future Directions 6

Using SDN to Enhance Load Balancing in Cloud Computing: An O...

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6th international conference on Advanced communication Technologies and Networking, CommNet 2023

作者： Yzzogh, Hicham Benaboud, Hafssa Mohammed v University in Rabat Ipss Faculty of Sciences Rabat Morocco

ISBN: (纸本)9798350329391

Software-Defined Networking (SDN) is a networking paradigm that separates the network's control plane from the data plane, providing centralized and programmable management of network resources. SDN has been increasingly used to enhance load balancing in cloud computing environments, as it allows for dynamic, efficient, and automated traffic distribution across the network. SDN controllers can make real-time load balancing decisions based on network conditions and application requirements. This paper focuses on recent research concerning the utilization of SDN to enhance load balancing in cloud computing. The paper categorizes these contributions according to various properties of SDN, notably including Dynamic Traffic engineering, Centralized Load Balancing Decisions, Application-Aware Load Balancing, Multi-Path Load Balancing, Load Balancing for Virtualized Environments as well as QoS-Based Load Balancing. © 2023 IEEE.

关键词： cloud computing

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On Detecting Malicious Code Injection by Monitoring Multi-Level Container Activities 14

On Detecting Malicious Code Injection by Monitoring Multi-Le...

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14th international conference on cloud computing and Services Science, CLOSER 2024

作者： Bhuiyan, Md. Olid Hasan Das, Souvik Majumder, Shafayat Hossain Majumdar, Suryadipta Hossain, Md. Shohrab Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh Concordia Institute for Information Systems Engineering Concordia University Canada

ISBN: (纸本)9789897587016

In recent years, cloud-native applications have been widely hosted and managed in containerized environments due to their unique benefits, such as being lightweight, portable, and cost-efficient. Their growing popularity makes them a common subject of cyberthreats, as evidenced by recent attacks. Many of those attacks take place due to malicious code injection to breach systems and steal sensitive data from a containerized environment. However, existing solutions fail to classify malicious code injection attacks that impact multiple levels (e.g., application and orchestrator). In this paper, we fill in this gap and propose a multi-level monitoring-based approach where we monitor container activities at both the system call level and the container orchestrator (e.g., Kubernetes) level. Specifically, our approach can distinguish between the expected and unexpected behavior of a container from various system call characteristics (e.g., sequence, frequency, etc.) along with the activities through event logs at the orchestrator level to detect malicious code injection attacks. We implement our approach for Kubernetes, a major container orchestrator, and evaluate it against various attack paths outlined by the cloud Native computing Foundation (CNCF), an open-source foundation for cloud native computing. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Network security

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