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

作者： Vignesh, R. Thenmalar, S. Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India

ISBN: (纸本)9798350367904

The recent proliferation of cloud computing services brings forth the focal issues of energy consumption in data centers from an environmental and financial standpoint. This paper describes a monitoring system focused on energy consumption reduction in the cloud by dynamically regulating loads in servers through constrained optimization techniques. The system employs the Gated Recurrent Unit algorithm in order to analyze the patterns of server usage over time and predict future demand, thereby allowing for proactive adjustments in workload distribution. The system maintains an optimal balance between performance and energy efficiency by continuously monitoring server utilization and power consumption, taking into consideration others like actual computation abilities, temperature limits, and SLAs. Our approach intensely reduces the power consumption while keeping a similar service quality level. Our testing results have proven to work well in real-world cloud environments, thus forming a basis that will help enhance sustainable practices in the cloud. © 2024 IEEE.

关键词： Cloud Computing Constrained Optimization Data Centers Energy Consumption Green Computing Load Balancing Server Load Optimization

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Revolutionizing Global IT Supply Chains: A Decentralized Framework for Cost-Effective and Streamlined Operations in Critical Events 1

Revolutionizing Global IT Supply Chains: A Decentralized Fra...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Sai Haree Ram, S. Uppala Durga Samirth, S. Pandimurugan, V. Srm Institute of Science & Technology Department of Networking and Communications Chennai India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

In the dynamic realm of global IT supply chains, businesses confront formidable challenges, particularly during critical junctures like product launches. Traditional Enterprise Resource Planning (ERP) systems, while effective, often entail substantial implementation costs. The supply chain department, indispensable for ensuring seamless operations within product-based enterprises, assumes heightened significance during expansions and pivotal moments. Yet, achieving optimal goods supply remains a persistent challenge without a resilient and well-connected supply chain. Centralized frameworks present inherent limitations, including single points of failure, security vulnerabilities and many more, all of which impede operational efficiency and jeopardize critical information. To address financial constraints and enhance operational efficiency, this research proposes decoupling the supply chain from traditional ERP systems while integrating with shipment management systems. The methodology leverages open-source solutions such as the Inter Planetary File System and the Ethereum blockchain to develop a Blockchain-Enabled Shipment Management System. This system seamlessly integrates with traditional ERP systems, acting as a hybrid solution that optimizes costs, ensures transparency, traceability, and security while harnessing the decentralized advantages of blockchain technology. The strategic approach empowers businesses with unparalleled capabilities to navigate the complexities of the dynamic global IT supply chain, especially during critical events like product launches. By leveraging blockchain technology and decoupling supply chain management from traditional ERP systems, this research contributes to paving the way for enhanced coordination, efficiency, and resilience in the face of evolving market demands. © 2024 IEEE.

关键词： Blockchain

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An Efficient AI Model for Classifier Tweets During Disaster Using Recurrent Neural Networks (RNN) 2nd

An Efficient AI Model for Classifier Tweets During Disaster ...

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2nd International Conference on Intelligent Computing Systems and Applications, ICICSA 2023

作者： Lakshmi Narayanan, K. Naresh, R. Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai603 203 India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai603 203 India

ISBN: (纸本)9789819754113

During any significant event, particularly during natural catastrophes, social media is an essential information source. It is claimed that data generated by social networking sites would enable regular people to become more situationally aware during disasters and coordinate to help themselves because it is ubiquitous, quick, and accessible. However, as the amount of social media data has grown exponentially, so has the amount of data that are unrelated to a crisis, making it harder for individuals to find the information they need to organise relief efforts, seek assistance, and perhaps even save lives. In this essay, we offer a method for identifying. During times of tragedy, informative messages are posted on social media. Our strategy is to implement this using recurrent neural networks and demonstrate substantial progress. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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DDoS Attack Detection System for IoT Enabled Smart City Applications with Correlation Analysis 4

DDoS Attack Detection System for IoT Enabled Smart City Appl...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Nirosha, Veeramachaneni Hemamalini, V. Srm Institute of Science and Technology Department of Networking and Communication Kattankulathur India

ISBN: (纸本)9798350343670

The topic of cybersecurity remains a significant concern for industries involved in digital operations, as seen by the recurring increase in security incidents. The increasing use of Internet of Things (IoT) devices in various settings such as homes, offices, transportation, and healthcare has resulted in a corresponding rise in the frequency of malicious assaults. Machine Learning (ML) is frequently utilized for the purpose of detecting attacks on IoT devices. This study presents a novel approach to cybersecurity-focused network traffic correlation analysis and the detection of Distributed Denial of Service (DDoS) attacks using artificial intelligence techniques in the context of an IoT-enabled smart city, with the aim of promoting sustainability. A traffic analysis was conducted by utilizing a wireshark network analyser tool, which effectively improves data transmission. The experimental investigation was conducted to evaluate various performance metrics, including accuracy, precision, recall, attack detection rate and time. The proposed technique achieved anaccuracy rate of 96%, precision and recall rates are 93.6% and 91% respectively. © 2024 IEEE.

关键词： correlation analysis Cyber security DDoS attack smart city

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Machine Learning Techniques for Effective Content- Based Approach in the Detection of Fake News 4

Machine Learning Techniques for Effective Content- Based App...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Jammula, Roshini Devi Srinivas, Nukala Bhargav Sandeep, K. Parimala, G. Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350360165

All functionalities required in the process of fake news detection, from data preparation to model building, have been done within the project, smoothly integrated into Flask for user interaction. The first step of data processing requires the import of the packages and the merging of real and fake news datasets. Data cleaning includes the removal of duplicates, text cleaning, and insightful visualization of characteristics using Seaborn and Matplotlib. Tokenization via TF-IDF Vectorization, Feature Selection, and Data Splitting paves the way for an extensive suite of Classification Models from Logistic Regression and SVC to KNN, Random Forest, Stacking, and even Voting Classifiers. Additionally, Flask provides functions for user registration, signing in, and an inbuilt SQLite database to store the user data. User input text is set to be translated and preprocessed for prediction using the best-fit pre-trained model. The outcome will then be shown to the user on the frontend. The novelty with the extension here is the models like the Voting Classifier (XGB + PA + Boosting) that have been introduced, which gives a 100% accuracy. More importantly, it extends the usability feature by allowing the user to provide text at the front end and get real-time predictions, thus enriching the overall user experience. In more ways than one, this project, from its approach toward fake news detection to its diversity of methodology and user-centered design, is going to be the most apropos project in the coming days to the emerging landscape of information integrity. © 2024 IEEE.

关键词： Inverse problems

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Email Phishing Detection Using AI and ML 2nd

Email Phishing Detection Using AI and ML

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Sarkar, Sohan Yadav, Ankit Balachander, T. Department of Networking and Communications SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031689048

Phishing is the act of attackers sending malicious emails to receivers in an effort to trick them into falling for a con. Normally, the intention is to persuade users to provide private information, such as system logins or financial data. Our project investigates such email phishing attacks using AI and ML to get a reasonable conclusion about which algorithm can spot these attacks the most successfully. In terms of cybersecurity, a phishing attack is a cybercrime that aims to obtain a user’s personal information in order to carry out some destructive actions. This attack’s effects could lead to account takeover, privilege escalation, and other issues. And in order to lessen it, this study provides information on how to spot phishing emails so that businesses can correctly deal with them. Natural Language Processing (NLP) [26], logistic regression, and fundamental AI ideas like CNN [27] are used, along with machine learning algorithms like KNN, Naive Bayers, and these. This study aims to identify the optimal machine learning algorithm that would provide the highest level of accuracy when it comes to phishing email detection. "To improve the accuracy of detecting phishing emails we have implemented CNN which is an effective approach to fulfill the target. CNN [27] can learn to recognize tiny patterns and traits that may be challenging for humans to notice, they can be a great tool for detecting phishing emails. Handling this technique is very important, hence LSTM is introduced". The CNN model’s LSTM [27] can be thought of as its brain. LSTM (Long Short-Term Memory) [27] is a form of recurrent neural network (RNN) that is frequently employed in tasks involving sequence prediction and natural language processing (NLP) [26]. It is intended to solve the vanishing gradients issue, which can arise in conventional RNNs when the gradients are extremely small and make the network struggle to learn long-term dependencies. By understanding and executing everything we have obse

关键词： Phishing

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Forecasting sales data using time series models and LSTM model 2

Forecasting sales data using time series models and LSTM mod...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Vishwanath, Anirudh Basheeruddin, Mohammed Saveetha, D. Department of Networking and Communications SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9798350383867

In the framework of sales forecasting, the project investigates the field of time series forecasting, with a particular emphasis on analyzing and comparing the forecasting precision of ARIMA, SARIMA, and LSTM models. Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) are some of the performance metrics that are utilized in this procedure. It's a thorough workflow that includes data preparation, rigorous model training, and diligent assessment. To facilitate a comparative comparison of the forecasting capabilities of the models, the visualization component uses a variety of graphical representations, such as incisive line graphs and instructive bar charts. Determining which model is the most capable of providing accurate sales forecasts is the key objective. This has significant implications for improving decision-making processes in the areas of sales and inventory management. This research program helps companies to design flexible and proactive strategies that are customized to changing market landscapes. As a result, enterprises can enhance their performance and strategic resilience. This is accomplished by providing businesses with data-driven insights and rigorous forecasting tools. © 2024 IEEE.

关键词： Sales

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Optimising Intrusion Detection Through Long Short-Term Memory Model

Optimising Intrusion Detection Through Long Short-Term Memor...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Tharun, Anjali Kondapalli, Amogh Srivathsa Prabakeran, S. Srm Institute Of Science And Technology Department Of Networking And Communication Chennai India

ISBN: (纸本)9798350371567

This research paper presents an innovative approach to enhancing Intrusion Detection Systems (IDS) using a Long Short-Term Memory (LSTM) model and transfer learning. The study begins with the loading of a pretrained LSTM model, followed by defining the architecture of the LSTM model. The weights from the pretrained model are transmitted to the new model, and the LSTM layer weights are frozen to preserve foundational knowledge. A classification layer is added to classify between normal network behavior and potentially malicious activities. The model is compiled and fine-tuned on an intrusion detection dataset to enhance its performance. The model's performance is continuously assessed and updated with fresh data to ensure accuracy. The system includes various modules such as Network Detection, Network Logging, IDS Protection, Attack Event Handler, Transfer Learning, and False Alarm Event Handler modules. The evaluation of the LSTM-based intrusion detection system on both training and testing datasets yielded promising results, demonstrating the model's robustness and generalization capabilities. The model exhibits high precision, recall, and F1-scores, along with consistent accuracy. A comparative analysis was conducted using a Decision Tree and Logistic Regression model alongside the proposed LSTM-based model, demonstrating the advantages of employing LSTMs for intrusion detection systems. The results affirm the robustness and reliability of the LSTM model in safeguarding network security against a wide range of cyber threats. © 2024 IEEE.

关键词： Long short-term memory

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Anomaly-Based Intrusion Detection in Network Traffic using Machine Learning: A Comparative Study of Decision Trees and Random Forests 2

Anomaly-Based Intrusion Detection in Network Traffic using M...

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2nd IEEE International Conference on networking and Communications, ICNWC 2024

作者： Sah, Anil Kumar Venkatesh, K. SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350365269

With advancements in information and virtualization technologies, the volume and growth of security threats from cyber attacks targeting networked systems are increasing. Protecting these networked systems is crucial in today's interconnected digital world. Detecting anomalies in network behavior is crucial to preventing fraud or unauthorized access and ensuring the integrity of data transmission over the internet. This highlights the crucial role of intrusion detection systems (IDS) in network security by identifying malicious attacks in network traffic. However, relying solely on data encryption, authentication, and firewalls isn't always sufficient, especially when dealing with fragmented packets that evade traditional security measures. Moreover, attackers are adept at adapting their tactics, making it increasingly challenging to stay ahead of potential threats. This study examines Anomaly-Based Intrusion Detection in Network Traffic utilizing machine learning methods such as Decision Trees and Random Forests leveraging the MachineLearningCSV data of the CICIDS-2017 dataset from ISCX Consortium to test and compare how well these two multiclass classifier algorithms work. Out of 79 features, including one feature as a label, 50 were obtained from the feature engineering step. The detection accuracy and F1 score of more than 99% were achieved using both Decision Tree and Random forest algorithms at a split ratio of 80:20. The results of two algorithms are compared, and it is observed from the ROC that the Random Forest algorithm is more effective than the Decision Tree for the multiclass classification. Additionally, the Decision Tree classifier achieved an accuracy score of 0.99867 with an execution time of 44.3 seconds, while the Random Forest classifier achieved an accuracy score of 0.99888 with an execution time of 8 minutes and 35 seconds. These results demonstrate the effectiveness of both algorithms in achieving high accuracy in intrusion detection tasks, w

关键词： Decision trees

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Video captioning – a survey

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Multimedia Tools and Applications 2025年第2期84卷 947-978页

作者： Vaishnavi, J. Narmatha, V. Department of Computer Applications SRM Institute of Science and Technology Trichy India Department of Computer and Information Science Annamalai University Annamalai Nagar Tamilnadu 608002 India

The explosion of the novel phenomenon of the combination of computer vision and Natural language processing is playing a vital role in converting the ordinary world into a more technological pool. Natural language processing and computer vision are the vanguard of Artificial intelligence with the enormous potential currently ruling more fields. The trending Accelerated research of the NLP and CV combo are cancer screening, surgical simulation, visual properties description, visual retrieval, visual description, etc. Video captioning is a task of localizing the event and various features are extracted from the particular event and then the description of the entire event is performed with Natural languages using various techniques. The task of video captioning has some evolution of various techniques from Traditional methods to Deep learning. Currently, Deep learning techniques are ruling the field. Several models are being proposed for the better enhancement of the video captioning task with the default Encoder-Decoder framework and some models with the variance of Attention mechanism and Transfer learning etc., The Attainment of every technique and the accuracy of the generated results are highly dependent on the nature of the problem, diversity, performance of chosen dataset and construction of the technique with various layers, and the amount of data split for train, validate, and test. This survey discusses the captioning types, real-time applications of video captioning, evolution of various methods in video captioning, advanced techniques, different datasets, metrics that are used to evaluate the results, and analysis of the results of the existing models for video captioning. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： computer vision

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