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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Chauhan, Raj Ahmad, Shahnawaz Onyarin, Ogala Justin Kumar, Rajendra Noida Institute of Engineering and Technology Department of CSE-AIML Greater Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India University of Delta Cyber Security Department Delta State Nigeria Jamia Millia Islamia Department of Computer Science New Delhi India

ISBN: (纸本)9798350380583

Cloud computing has revolutionized data content stock and administration but concurrently it has increased the vulnerability factors especially Key Management System (KMS), which is crucial since it is used to protect cypher key. Typically, there are several limitations to anomaly detection methods used currently: poor precision and insufficient speed to detect threats. This research presents an intelligent model that integrated deep learning technique into existing Cloud security Framework (CSF) to conduct proactive identification on the deviation from the baseline behavior of interactions between the applications and the KMS. This is achieved through continually accruing KMS log data, defining behavioral profiles for the applications and using clustering to categorize them appropriately. The model also continuously monitors the system for any deviations, making anomalies easily discernible for quick action either through raising alerts to stakeholders or when using the KMS API to automatically respond to the flagged issues. Studies in the multitenant cloud scenario indicate that implementation of LSTMbased model is more effective than conventional approaches, enhancing the CSF's capability in threat detection thus making it possible to quickly mount defensive measures to safeguard crucial data and cryptographic keys. © 2024 IEEE.

关键词： Cloud security

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An Enhanced Product Quality Evaluation using Hybrid LSTM-FCNN Model and GWO Algorithm for Social Media Sentiment Analysis 8

An Enhanced Product Quality Evaluation using Hybrid LSTM-FCN...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Raj Kumar, V.S. Kumaresan, T. Kanna, P. Rajesh Jagadeesan, S. Showmiya, P. Nithin, P. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India Nandha Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Technology Department of Computer Science - Cyber Security Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India

ISBN: (纸本)9798350367904

Sentiment analysis is still in its developing era, and sometimes, struggles are faced in evaluating user sentiment due to the use of traditional models to analyze the relationship structures in social media interactions. To tackle the issue, we proposed an innovative approach Hybrid LSTM neural network and a fully connected neural network (FCNN) integrated with the Grey Wolf Optimization (GWO) algorithm. The objective of this proposed method is to improve sentiment analysis by employing the LSTM to capture the sequence of data and improve the ability of FCNNs to improve feature extraction and the analysis process. The proposed system process initiated with the data preprocessing of input Twitter sentiment dataset transformed into a suitable input to the Hybrid LSTM-FCNN neural network. Then, temporal dependency of the data captured by LSTM and FCNN is used to extract the features. The GWO method mimics the grey wolf hunting behaviour, for optimizing the proposed Hybrid LSTM-FCNN model's parameters to enhance the classification accuracy. The system models start with the initialization of the model, parameters, and training the models using the dataset to reduce the cross-entropy loss, estimating the testing dataset from unseen data and fine-tuning using GWO as necessary. Results demonstrate significant improvements in sentiment categorization compared to traditional methods. The Hybrid LSTM-FCNN model, enhanced with GWO, effectively captures nuanced sentiment patterns, leading to more accurate classification. Additionally, GWO's computational efficiency contributes to scalability. In conclusion, our proposed approach offers a promising solution for enhancing product quality evaluation through advanced neural network architectures and optimization algorithms applied to social media sentiment analysis. © 2024 IEEE.

关键词： Tweets

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Fake News Detection Using Machine Learning Algorithm 2

Fake News Detection Using Machine Learning Algorithm

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2nd IEEE International Conference on Communication, security and Artificial Intelligence, ICCSAI 2023

作者： Narkhede, Apurva Patharkar, Devyani Chavan, Nekita Agrawal, Rahul Dhule, Chetan Iot Cyber Security G H Raisoni College of Engineering Department of Data Science Nagpur India

ISBN: (纸本)9798350369960

Fake news is a big concern since it spreads widely over social media and other media channels, creating significant social and national damage with devastating consequences. In order to address this issue, substantial research how to be recognized on fake news detection has been done. The purpose of this study is to analyze the existing research on fake news detection and select the best conventional machine learning frameworks to develop an algorithm for supervised machine learning. The algorithm will be able to classify false reports as true or fraudulent using textual analysis methods such as NLP. The proposed process involves data preprocessing and vectorization, where the NLP library will be used to perform tokenization and feature extraction of text data, utilizing tools such as Count Vectorizer and Tiff Vectorizer. Further, feature selection methods will be employed to evaluate and determine the most fitting features to achieve the highest precision based on confusion matrix results. The outcome of the decision tree classifier algorithm gives the accuracy of 90%. © 2023 IEEE.

关键词： data preprocessing Fake news feature extraction Machine learning algorithm vectorization

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Smart contract token-based privacy-preserving access control system for industrial Internet of Things

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Digital Communications and Networks 2023年第2期9卷 337-346页

作者： Weizheng Wang Huakun Huang Zhimeng Yin Thippa Reddy Gadekallu Mamoun Alazab Chunhua Su Department of Computer Science City University of Hong KongHong Kong SAR999077China Division of Computer Science University of AizuFukushima965-8580Japan School of Information Technology and Engineering Vellore Institute of TechnologyTamil NaduIndia College of Engineering IT and EnvironmentCharles Darwin UniversityAustralia School of Computer Science and Cyber Engineering Guangzhou UniversityChina

Due to mobile Internet technology's rapid popularization,the Industrial Internet of Things(IIoT)can be seen everywhere in our daily *** IIoT brings us much convenience,a series of security and scalability issues related to permission operations rise to the surface during device ***,at present,a reliable and dynamic access control management system for IIoT is in urgent *** till now,numerous access control architectures have been proposed for ***,owing to centralized models and heterogeneous devices,security and scalability requirements still cannot be *** this paper,we offer a smart contract token-based solution for decentralized access control in IIoT ***,there are three smart contracts in our system,including the Token Issue Contract(TIC),User Register Contract(URC),and Manage Contract(MC).These three contracts collaboratively supervise and manage various events in IIoT *** also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units(NTRU)to preserve user privacy during the registration ***,to evaluate our proposed architecture's performance,we build a prototype platform that connects to the local ***,experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.

关键词： Blockchain Privacy preservation Smart contract Industrial IoT

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Demand Response in Smart Grids: Challenges, Solutions, and security Implications 2

Demand Response in Smart Grids: Challenges, Solutions, and S...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Abdelraouf, Hussien Gogolkiewicz, Ryker Kim, Nathaniel McCaughan, Nicholas Alanes, Joel Nadhaif, Ali Fouda, Mostafa M. Ibrahem, Mohamed I. Augusta University School of Computer and Cyber Sciences AugustaGA30912 United States George Mason University Cyber Security Engineering Department FairfaxVA United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States

ISBN: (纸本)9798331529529

This paper discusses demand response (DR) in the context of smart grids (SGs). DR is a method of controlling electricity consumption and production, usually for financial and security reasons. The topics covered include security in DR, solutions to related attacks, and the implementation of one of these solutions. Attacks on DR involve various methods of injecting perturbations into the system to cause damage to the grid. Understanding these attacks is crucial for protecting future systems. It is also important to learn more about cyber-physical systems and how they operate. Another significant topic discussed in this paper is energy management in SGs, specifically how to produce zero marginal cost renewable energies efficiently and cleanly. The methods for achieving this outcome, including various formulas and systems, are discussed in the two referenced papers and will be explained further in this paper. These energy-saving methods also contribute to the cybersecurity aspect of this paper to encompass DR. © 2024 IEEE.

关键词： Demand response

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AI-driven Dynamic Workload Balancing for Real-time Applications on Cloud Infrastructure 7

AI-driven Dynamic Workload Balancing for Real-time Applicati...

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

作者： Sreeramulu, Madhusudhan Dasari Mohammed, Abdul Sajid Kalla, Dinesh Boddapati, Nagesh Natarajan, Yuvaraj American Express Department of Cyber Security United States University of the Cumberlands Department of School of Computer and Information Sciences KY United States Colorado Technical University Department of Computer Science CharlotteNC United States Microsoft Department of Computer Science CharlotteNC United States Sri Shakthi Institute of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350350067

The specification provides a new, resource-efficient method of Dynamic Workload Balancing in AI-driven Real-time Applications over Cloud Infrastructure. The real-time application keeps processing data at high speeds, and it is too difficult to get or make this type of arrangement using our traditional cloud setups as the system employs artificial intelligence methods responsively reallocate resources between virtual machines in accordion with demanded quality of services slabs in behalf-to-capacity ratio. It is scheduling jobs to resources efficiently and delivering results in a timely fashion, which is necessary for real-time applications like video processing or stock trading. It uses machine learning models based on historical data to predict the resources required for upcoming tasks. These predictions are subsequently utilized to smartly assign resources across VMs, factoring in network latencies and inter-dependence. It allows the system to modify queues in real-time according to changes in workload patterns, performing a dynamic load balancing. © 2024 IEEE.

关键词： Adversarial machine learning

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Review of permission-based malware detection in Android

Review of permission-based malware detection in Android

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International Conference on Artificial Intelligence, Blockchain, Computing and security, ICABCS 2023

作者： Rawat, Nishant Amrita Singh, Avjeet Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Center for Cyber Security and Cryptology Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

A market study showed that an average of 70% of smartphone users use an android-based smartphone. The Android operating system draws numerous malware threats as a result of its popularity. The statistic reveals that 97% of malware prey on android due to its lack of security. The malware enters the system and after installing the malware application they ask permission from the user to run. Permissions like Read_Phone, Access_Internet, Write_Log, etc give full control of the victim’s phone to the hacker. In recent years more efforts were made to analyze android permissions to detect malware. In this paper, we review some of the already published work in detail which is in the area of malware detection using static analysis. © 2024 The Author(s).

关键词： Machine learning

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Design of Smart Irrigation System Based on MLA

Design of Smart Irrigation System Based on MLA

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Bobade, Gunjan Dhule, Chetan Khadilkar, Ritika Agrawal, Rahul Morris, Nekita Chavhan G H Rasioni College of Engineering IOT Cyber Security Department of Data Science Nagpur India

ISBN: (纸本)9798350310061

Agriculture has always been a fundamental aspectof Indian culture, making it the country's most efficient andimportant occupation. Over the years, smart and advanced techniques have been developed to optimize farming practices, and one such development is the smart irrigation system. This system has become increasingly popular among farmers as it helps them reduce water usage while maximizing profits. This has not only helped farmers increase their profits but has also made farming a more sustainable and eco-friendly occupation. The proposed Model is a AI Based Smart irrigation System, which utilizes machine learning algorithms (ML) to predict soil moisture levels based on parameters such as temperature, humidity, and soil moisture. This system is developed through cloud computing, allowing for real-time data to be extracted from (IoT) sensors. The decision tree, a supervised learning algorithm, is applied to the data to classify or predict values. The result obtained from the decision tree helps predict the appropriate amount of water needed for the crops, gives the accuracy of 90% It allowing for efficient water usage and promoting yield growth. © 2023 IEEE.

关键词： Decision trees

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Detecting cross-site scripting attacks using machine learning: A systematic review

Detecting cross-site scripting attacks using machine learnin...

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International Conference on Artificial Intelligence, Blockchain, Computing and security, ICABCS 2023

作者： Baniya, D. Amrita Chaudhary, A. Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Center for Cyber Security and Cryptology Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

In today s age, many daily tasks are performed through the Internet using various web applications. While using the web application, the information and data are stored in the database of the network which can easily be attacked by the attacker. The attacker used different types of attack patterns to steal the victim s information or any details like their bank information, and business details. Among the different types of attacks over the web application vulnerabilities, the most prominent category of attack is a cross-site scripting (XSS) attack. In the XSS attack, an attacker transmits the script to the victim s web Browser. As the victims are unaware that they were being attacked leading to stealing the valuable information like personal details, bank details, and cookies. Using the cookies of the victim, the attacker can easily access their account without using a username and password. Early, different types of approaches have been done to shoot out the vulnerabilities related to XSS. This paper purports to be a survey on XSS. The primary contribution of this study to the current state-of-the-art XSS is a systematic review of various types of research done to detect XSS vulnerabilities using Machine Learning. This paper also provides the background related to XSS, techniques, difficulties and future guidelines in the field of XSS to provide researchers with significant knowledge for further work. © 2024 The Author(s).

关键词： Machine learning

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An efficient data aggregation scheme with local differential privacy in smart grid

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Digital Communications and Networks 2022年第3期8卷 333-342页

作者： Na Gai Kaiping Xue Bin Zhu Jiayu Yang Jianqing Liu Debiao He School of Cyber Science and Technology University of Science and Technology of ChinaHefeiAnhui230027China Department of Electronic Engineering and Information Science University of Science and Technology of ChinaHefeiAnhui230027China Department of Electrical and Computer Engineering University of Alabama in HuntsvilleHuntsvilleAL35899USA Key Laboratory of Aerospace Information Security and Trusted Computing of Ministry of Education School of Cyber Science and EngineeringWuhan UniversityWuhanBeihu430072China

By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the smart grid collect the users' power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition. The control center can evaluate the supply and demand of the power grid through aggregated data from users and then dynamically adjust the power supply and price, etc. However, since the grid data collected from users may disclose the user's electricity usage habits and daily activities, privacy concern has become a critical issue in smart grid data aggregation. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.

关键词： Local differential privacy Data aggregation Smart grid Privacy preserving

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