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2nd International Conference on emerging Trends in Information Technology and engineering, ic-eTITe 2024

作者： Vishali, m. mirudhula, A. Priya, A. Iswarya, m. Subramanian, Kavitha Panimalar Engineering College M.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Dept of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

Because of the widespread accessibility of the weband its ease of use, every organisation nowadays uses web apps to expand its company. With the growth of Due to growing online usage, attack riskhas grown as well. An effective monitoring tools that can recognise these threats Timely is necessary. Java script, PHP, and HTmL are utilisedfor website development and database use of SQL management in depth. most typical web server assaults include XSS, DOS (Denial of Service), and SQL injection Cascading Style Sheets. IDS with rules work with keywords and patterns but fail to recognise unidentified assaults. In this article, mL based model is used for leveraging web server logsto identify intrusions. It identifies the sort of assault as well as if a given log is normal or malicious. By establishing a private network with the aid of an Apache WAmP server, web server logs are produced and gathered. © 2024 Ieee.

关键词： machine learning

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Identification of Therapeutic Plants Using machine Learning 2

Identification of Therapeutic Plants Using Machine Learning

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2nd International Conference on emerging Trends in Information Technology and engineering, ic-eTITe 2024

作者： Rohini, S. Keerthana Shree, m.U. Jayalakshmi, R. malathi, m. Jabasheela, L. Panimalar Engineering College M.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College B.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Dept of Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

It takes a lot of work to develop a computerized method for classifying plants for medicinal purposes. There are countless kinds of plants found in India, and each one has its own unique set oftherapeutic advantages. Background information is necessary for manual identification and categorization because it is difficult for people to recall the proper titles of all plant species and their *** is crucial to preserve these medicinal plants since doing so will benefit several industries, including medicine, botanical research, and taxonomic studies, among others. Current technology cannot match thediversity of medicinal herb species that existin India. Utilizing textural traits that are crucial for recognizing and classifying leaves, the recom.ended technique makes itsimpler to categorize medicinal plants. The feature extraction, categorization, and picture enhancement stages are the three main components of the suggested method. The qualities that could be matched betweenthe leaf images were gathered using digital image processing methods after cell phones were used to snap the leaf photos. The CNN classifier is then used to create a machine learning classifier. © 2024 Ieee.

关键词： machine learning

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Study and Analyzing Propagation models for Wireless mesh Networks 4

Study and Analyzing Propagation Models for Wireless Mesh Net...

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4th International Conference for emerging Technology, INCeT 2023

作者： Sharma, Krishan Kant Pokharana, Anchal Sharma, Samiksha Chandigarh University Academic Unit-1 Department of M.E Punjab Gharuan India Chandigarh University Department of Computer Science Punjab Gharuan India

ISBN: (纸本)9798350335750

The invention of wireless mesh networks (WmN) is inside the center of attention of instructional research to offerings and Internets connectivity's. A WmN is also considered be a wireless multi hop community which consists of routers and clients in mesh pattern. The routers in the mesh pattern have minimal mobility and structure that like of a rear bone which presents entry to the mesh clients of the wireless mesh community. A num.er of out-doors propagation fashions are proposed for the lookup routing for this purpose. Furthermore, solely a few fashions are taken underneath consideration for the performances of routing metrics by means of thinking about special node densities. The simple propagation mechanisms are equal regarded in the overall performance of the conversation systems. Amongst major problems in routing is to supplying pleasant overall performance whilst scaling the wireless mesh network. © 2023 Ieee.

关键词： mesh generation

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Detection and Categorization of Conflict Flows Within SDN environments using machine Learning Approach 2

Detection and Categorization of Conflict Flows Within SDN En...

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2nd International Conference on emerging Trends in Information Technology and engineering, ic-eTITe 2024

作者： Kousalya, S. Sundararajan, Kanthimathi Vasudevan, I. Jaichitra Sheela, L. Jaba Thyagarajan, Sarunya Panimalar Engineering College M.E. Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College B.E. Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Dept of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

Software-Defined Networking (SDN) is known for its remarkable versatility and adaptability. SDN applications encompass a wide range of network management and control functionalities, including load balancing, access control, and routing. However, SDN can encounter issues stemming from conflicting traffic patterns that may degrade network performance in relation of efficiency and optimization. These conflicts arise when factors such as priority and action settings are adjusted. Furthermore, IP spoofing, commonly employed for anonymity and amplification, frequently leads to widespread distributed denial-of-service (DDoS) attacks. We've established a sufficient condition based on the duration and frequency of DDoS attacks. Notably, our sophisticated attack method generates legitimate traffic within the SIP network using reflection techniques. 'As a result, it has the ability to bypass security measures such as blacklists, IP blocking, rate limiting based on packet counts or session/transaction volumes, and automated systems designed to detect unnatural message generation. These security mechanisms are commonly employed in advanced security setups, but the system or technique in question can effectively avoid detection or restriction when interacting with digital environments.' © 2024 Ieee.

关键词： Network security

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Predicting Urban Vartur Lake Health: A multi-context machine Learning Approach to Water Quality Assessment

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Journal of The Institution of engineers (India): Series A 2025年第2期106卷 577-590页

作者： Teggi, Pralhad P. malakreddy, Bharathi Natarajan, Santhi Dhaduti, madhumati S. Harivinod, N. Department of Computer Science BMSIT & M Visvesvaraya Technological University Karnataka Belagavi590018 India Department of Computer Science Shiv Nadar University Tamil Nadu Chennai603110 India Department of Civil Engineering K.L.E. Institute of Technology Karnataka Hubballi580031 India Department of Computer Science St Joseph Engineering College Karnataka Mangaluru575028 India

This study introduces a novel multi-context approach to water quality assessment by examining the factors influencing Chemical Oxygen Demand in Varthur Lake, Bangalore. Integrating traditional regression techniques with advanced machine learning models, this research analyzes eight water quality parameters collected over two years (July 2022–Septem.er 2024). exploratory Data Analysis is employed to understand parameter distributions, relationships, and trends. Five distinct environmental contexts affecting COD are identified, and various machine learning models are evaluated for COD prediction within each context. Results indicate that Random Forest models yield superior accuracy, with Gradient Boosting offering a viable alternative. This multi-context framework provides a comprehensive understanding of Varthur Lake’s water health and offers a scalable approach for predictive water quality monitoring applicable to other urban water bodies. © The Institution of engineers (India) 2025.

关键词： Lake pollution

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Continuous monitoring of Web Server Assaults Using machine Learning

Continuous Monitoring of Web Server Assaults Using Machine L...

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emerging Trends in Information Technology and engineering (ic-eTITe), Conference on

作者： Vishali m mirudhula A Priya A m Iswarya Kavitha Subramanian M.E Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Dept of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

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Optimal Design of a Planning and Contracting Framework to enable Vehicle to Building and Grid Services via Demand Response

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Ieee Transactions on Transportation electrification 2025年第3期11卷 7542-7556页

作者： Abd elaziz elsayed, Ahmed Saxena, Shivam.essa Zidan Farag, Hany York University Lassonde School of Engineering Electrical Engineering and Computer Science Department Keele Campus TorontoONM3J 1P3 Canada University of New Brunswick Department of Electrical and Computer Engineering FrederictonNBE3B5A3 Canada

Bidirectional electric vehicle (eV) charging enables stored energy to reduce peak loads for vehicle to buildings (V2Bs) and the vehicle to grid (V2G). However, building owners investing in V2B infrastructure while generating revenue from V2G services face challenges in planning and coordinating with eV owners due to uncertainties in their schedules and profit-sharing expectations. Additionally, misaligned building and grid peak times can create conflicts between V2B and V2G goals, which may negatively impact the building electricity bill. Unlike previous studies that used a contract-free approach for aggregating V2B and V2G, resulting in inconsistent participation, this article proposes a novel planning and contracting framework that enables building owner to determine the optimal contract parameters with eV owners. These parameters include minimum participation time in DR events, minimum arrival state of charge (SoC), and permitted emergency departure hours. The framework supports V2B aggregation with on-site distributed energy resources (DeRs) for DR and V2G services, ensuring transparency and fairness through shared profits and performance-based penalties, while com.ensating building electricity bills due to V2G activities. The framework is composed of a tri-stage optimization process that uses monte Carlo simulations to generate eV owner profit assessments, select optimal eV candidates based on charger availability, estimate contract parameters with profit/penalty sharing, and assign contracts between power system operators (PSOs), building owners, and eV owners under multiple virtual scenarios. Simulation results validate the contract parameter assessment and its performance via a 3-day case study with real-world datasets that demonstrates net revenue generation of {\} 209 for each eV owner and {\} 950 for building owners during DR events with a return on investment (ROI) of 134.5% and 130.7% for the eVs and Building owners, respectively. © 2015 Ieee.

关键词： Vehicle-to-grid

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Blockchain: Secured Solution for Signature Transfer in Distributed Intrusion Detection System

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computer Systems science & engineering 2022年第1期40卷 37-51页

作者： Shraddha R.Khonde Venugopal Ulagamuthalvi Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Engineering M.E.S.College of EngineeringS.P.Pune UniversityPune411001India

exchange of data in networks necessitates provision of security and *** networks compromised by intruders are those where the exchange of data is at high *** main objective of this paper is to present a solution for secure exchange of attack signatures between the nodes of a distributed *** activities are monitored and detected by the Intrusion Detection System(IDS)that operates with nodes connected to a distributed *** IDS operates in two phases,where the first phase consists of detection of anomaly attacks using an ensemble of classifiers such as Random forest,Convolutional neural network,and XGBoost along with genetic algorithm to improve the performance of *** novel attacks detected in this phase are converted into signatures and exchanged further through the network using the blockchain framework in the second *** phase uses the cryptosystem as part of the blockchain to store data and secure it at a higher *** blockchain is implemented using the Hyperledger Fabric v1.0 and v2.0,to create a prototype for secure signature *** exchanges signatures in a much more secured manner using the blockchain architecture when implemented with version 2.0 of Hyperl-edger *** performance of the proposed blockchain system is evaluated on UNSW NB15 *** performance has been evaluated in terms of execution time,average latency,throughput and transaction processing *** evidence of the proposed IDS system demonstrates improved performance with accuracy,detection rate and false alarm rate(FAR)as key parameters *** and detection rate increase by 2%and 3%respectively whereas FAR reduces by 1.7%.

关键词： Blockchain intrusion detection system machine learning algorithms secured communication ensemble approach

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Detection of Botnet Traffic using Deep Learning Approach

Detection of Botnet Traffic using Deep Learning Approach

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Sustainable Computing and Data Communication Systems (ICSCDS), International Conference on

作者： K Srinarayani B. Padmavathi D. Kavitha M.E Computer Science and Engineering Easwari Engineering College (Autonomous) Chennai India Computer Science and Engineering Easwari Engineering College (Autonomous) Chennai India

Botnets pose a serious threat to network security and have become a significant concern for network administrators. In recent years, machine learning techniques, such as Long Short-Term memory (LSTm), have shown promise for detecting botnets. The CTU-13 dataset, which contains network traffic data from 13 different botnet scenarios, has become a popular dataset for evaluating the effectiveness of botnet detection methods. However, existing botnet detection systems face several challenges, including the high dimensionality and complexity of network traffic data, the diversity of botnet behaviors, and the presence of noisy and irrelevant features. To overcome these challenges, this project proposes a botnet detection method using LSTm on the CTU-13 dataset. Our objective is to develop a model that can accurately detect botnets in network traffic data. Specifically, we will preprocess the dataset to reduce noise and irrelevant features, train and validate our LSTm model on the preprocessed dataset, and evaluate the performance of our model using standard evaluation metrics. The proposed method achieves a high level of accuracy, outperforming existing botnet detection methods on the CTU-13 dataset. This study can contribute to the development of more effective and efficient botnet detection systems for network security. Overall, this study demonstrates the potential of using LSTm for botnet detection and provides insights into the challenges and opportunities of applying machine learning techniques to network security.

关键词： Botnet Phishing Government Telecommunication traffic Network security malware electronic mail

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Diagnosing Progressive Face Recognition from Face morphing Using ViT Technique Through DL Approach 1

Diagnosing Progressive Face Recognition from Face Morphing U...

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1st Ieee International Conference on Networking and Communications, ICNWC 2023

作者： mohamed Faizal, m.K. Geetha, S. Barveen, A. M.I.E.T. Engineering College Department of Computer Science and Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350336009

The face-morphing attack, which occurs in private, public, and governmental institutions, is one of the most well-known in today's world. Face recognition systems tend to be vulnerable if the face images are manipulated with duplicate images. manipulated images are combined with the original image so that the images look like legitimate ones. Several face recognition studies are being conducted to determine whether the face images are manipulated. Using the DL algorithm, the face image is trained to attain the original and morphed face images by recognizing the face images. DL algorithms determine the images by classifying whether they are morphs or not recognizable to humans. In this paper, the foremost emphasis is on diagnosing the face recognition from those face-morphed images using the different DL techniques. Different DL techniques are effectively compared, where the ViT transformer attains improved accuracy when compared to Resnet, RNN, and CNN, respectively. This paper provides an overview of the various deep learning algorithms for detecting those face recognition images that focus on challenges and issues in the facial datasets from Face Recognition Kaggle dataset with training and testing image dataset. It determines the higher contrast in image efficiency and the evaluation of the face recognized images with an improved image. © 2023 Ieee.

关键词： Face recognition

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