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Improving distributed denial of service attack detection using supervised machine learning

measurement: Sensors 2023年 30卷

作者： Fathima, Afrah Devi, G. Shree Faizaanuddin, mohd Dept.of Computer Applications BSAR Crescent Institute of Science and Technology Chennai India Dept. Of CS & IT Maulana Azad National Urdu University Hyderabad India Dept. of AI & Data Science M.Tech Chaitanya Bharathi Institute of Technology Hyderabad India

Distributed denial-of-service (DDoS) attacks are a big problem for cyber security because they can cause a lot of damage to both people and companies. Distributed Denial of Service (DDoS) attacks have been seen to do a lot of damage to the networks and devices they are aimed at. These hacks slow down networks and use up buffer space, which makes resources unavailable. To solve this problem, "Supervised machine Learning models" have been used. Several machine learning techniques, such as Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression, were used to figure out what was normal and what was an attack. This study used a sample of the CSE-CICIDS2018, CSE-CICIDS2017, and CICDoS datasets. The dataset was divided into two parts in which three fourth of the data was used for training and one fourth of the data for testing purpose. The proposed research attempt to classify the DDoS attack by using supervised machine learning classifiers. This approach employs three machine learning classifiers such as Random Forest, KNN and Logistic regression. Then we perform Feature Scaling by using Standard Scaler. Finally, the system was evaluated. Random forest classifier outperformed other classifiers with an accuracy of 97.6 % whereas KNN and Logistic regression achieved 97 % and 91.1 %. The study employed several Supervised machine Learning techniques, including Random Forest, KNN, and Logistic Regression to identify the most effective algorithm for the test. Results demonstrate that Random Forest outperformed the other models. © The Authors

关键词： Denial-of-service attack

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A Brief Review on Accuracy Level of Smart and micro Grid Systems Use of mL and DL Algorithms Through Hybrid Integration

A Brief Review on Accuracy Level of Smart and Micro Grid Sys...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Pitamber Adhikari Febin Prakash Brajehdra Sharma Department of Information Technology & M.Tech Integrated Noida Institute Of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Computer Science and Engineering Tula's Institute Dehradun India

Forecasting of energy is a crucial component in overcoming the challenges of smart grid mechanisms, which include functions such as demand-side management, load reduction, and desirable allocation. The most significant obstacle battling today's grid has to do with efficient forecasting management for restricted predictive inconsistencies. This paper provides a comprehensive and application-focused review of advanced prediction strategies for smart grid systems, as well as the latest advances in the use of machine Learning (mL) and Deep Learning (DL) approaches. It also discusses the significance of hybridized techniques in improving the accuracy of forecasts. A study of comparison using an hourly energy consumption database has been carried out to evaluate the effectiveness of mL and DL techniques. The analysis' findings highlight hybrid methods' superior efficiency when compared to individual mL, DL, and standard statistical methods.

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A Detailed Observation on Association Rule mining 2

A Detailed Observation on Association Rule Mining

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2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020

作者： Sujatha, Pothula Elavarasan, G. Nithya, P. Kadhiravan, N. Gosh, Arindam Adhikary, Asmita Pondicherry University M.Tech-CSE Department of Computer Science and Engineering Kalapet Puducherry India

ISBN: (纸本)9781728153742

Association rule mining is utilized to take care of various types of area issues. These days' affiliation rule mining has been stretched out to huge information. As a result of the gigantic measure of information, a solitary way the sequential procedure isn't adequate subsequently. The most basic piece of ARm is discovering a potential relationship between things in an enormous exchange based dataset. The well-known algorithms in particular apriori and FP Growth has been identified implemented those algorithms using the WEKA (version 3.7.10) tool. © 2020 IEEE.

关键词： Association rules

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A machine Learning method for Detecting Sarcasm in Negative Sentiment Tweets using Hyperboles

A Machine Learning Method for Detecting Sarcasm in Negative ...

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Preetha, V. Sathishkumar, P. Janani, m. manohari, B. Ida, S. Jhansi Suganyadevi, S. Sri Ramakrishna Institute of Technology Department of CSE Coimbatore India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Department of CSE Chennai India KPR Institute of Engineering and Technology Department of CSE Coimbatore India Sri Krishna College of Engineering and Technology Department of M.Tech Computer Science and Engineering Coimbatore India R.M.K Engineering Colleg Department of Computer Science and Business Systems Kavaraipettai India KPR Institute of Engineering and Technology Department of ECE Coimbatore India

The use of sarcasm as an expression is a common literary device that involves someone purposefully expressing the opposite of what is being meant. The accurate identification of sarcasm in a text may boost other natural language processing activities, particularly ones involving sentiment analysis, and make it easier to comprehend the genuine intents of the speakers in the text. It is difficult to determine if someone is being sarcastic only based on their written words since sarcasm is a kind of implicitly expressing one's feelings and sarcastic speakers purposefully confound their audiences. Existing methods based on machine learning and deep learning obtained an insufficient level of performance when processing sarcastic material that included complicated expressions or required a certain set of prior knowledge to comprehend. The purpose of this research is to examine how sarcasm may be detected in tweets that have a negative mood and include hyperboles. For the purpose of sarcasm identification, the hashtags #Chinesevirus, #Kungflu, #COVID19, #Hantavirus, and #Coronavirus were included in the collection of 6,600 pre-processed negative sentiment tweets. The three well-known machine learning algorithms known as Support Vector machine, Random Forest, and Random Forest with Bagging were utilised in order to conduct an analysis of the five hyperbole characteristics known as interjection, intensifier, capital letter, punctuation mark, and elongated word. The suggested model with the extended term attained an accuracy of 78.74% and an F-score of 71%, respectively, despite the inclusion of hyperbolic words in the tweets included in an unbiased dataset. It was discovered that intensifier was the exaggeration with the highest level of significance (p. 0001). The results of the experiments and analyses carried out for this study led the researchers to the conclusion that hyperboles may be found in unbiased datasets, which helps improve sarcasm identification as well. © 202

关键词： Sentiment analysis

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Sitting posture Analysis using CNN and RCNN

Sitting posture Analysis using CNN and RCNN

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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： Nishitha R T. Sudhakar J Bethanney Janney N. R. Krishnamoorthy K Dhanalakshmi S Vigneshwaran Biomedical Engg. Department Sathyabama Institute of Science and Technology Chennai Dept. of ECE Sathyabama Institute of Science & Technology Chennai Dept. of Computer Science Engg Sathyabama Institute of Science and Technology Chennai M.Tech-Medical Instrumentation Sathyabama Institute of Science and Technology Chennai

Based to statistics, about 80 percent of the population follows incorrect sitting posture. From the year 2020, due to the Covid-19 curfew, the traditional work office of many professionals turned into Work from home and remote office lifestyle. many of the working professionals has no proper ergonomic workplace setup in their house leading to improper sitting position. monitoring the sitting posture is needed to avoid chronic complication of mSD, neck, and spine related injuries. many techniques were emerged for correcting and monitoring the posture. Neural Network based posture detection is being into the field of research as the come with more range of accuracy. This paper discusses about the various Neural Networks used and their accuracies.

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The Brief Assessment of Performances of Electrical Components Grids

The Brief Assessment of Performances of Electrical Component...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Ritesh Rastogi A Kannagi manish Srivastava Department of IT and M.Tech Integrated Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Department of Computer Science Jain (Deemed to be University) Bangalore India Department of Electrical Engineering Vivekananda Global University Jaipur India

In this study, we explore the vital importance of electrical measurements in the smart grid setting, especially for data-driven applications. We highlight the close connection between data quality and smart grid data mining and provide a novel fuzzy evaluation technique for multivariate electrical measurement quality evaluation in smart grids. This approach entails determining relevant quality dimensions for smart grid data and providing novel membership functions designed to assess data quality according to certain desired attributes linked to each dimension. Using actual power and current data from the IEEE 14-bus power flow analysis, we perform assessments to verify the effectiveness of the suggested membership functions. Especially, our method is adaptable as it can assess the integrity of a single smart grid dataset including electrical readings of different power system parameters, including binary parameters like the state of the interrupter. It distinguishes itself from previous techniques by assessing measurement consistency. Additionally, our proposed method demonstrates sensitivity regarding the false data distribution in a given database.

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Developments In Hybrid Electric Vehicles: Investigating Plans, management techniques, and Power Resources for Sustainable Travel

Developments In Hybrid Electric Vehicles: Investigating Plan...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Aditee mattoo Bhuvana J Vijay Kumar Pandey Department of IT and M.Tech Integrated Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Department of Computer Science Jain (Deemed to be University) Bangalore India Department of Mechanical Engineering Vivekananda Global University Jaipur India

technological developments in energy production and use are crucial to the shift to a future with fewer emissions and better health. Vehicle technology is impacted by a number of variables, including pollution-induced climate change, stricter environmental rules for autos, depletion of petrol and diesel supplies, and price volatility for transit systems. One of the most notable possibilities for clean automobile technology is the hybrid electric vehicle (HEV). This research explores several HEV designs and approaches, clarifying development guidelines and ways to enhance control algorithms. We also look at the various battery-powered drives that HEVs employ. Performance is the main area of study since control methods are essential to improving the fuel economy and lowering pollutants of hybrid engine systems.

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Design and Fabrication of multiband Planar Antenna for 5G and Next-Generation mobile Handset Applications

Design and Fabrication of Multiband Planar Antenna for 5G an...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： mahendrakumar, S. Kiruthika, S. Vijayakumar, P. Karthikeyan, G. Bharathi, V. Vanitha, K. Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode India Erode Sengunthar Engineering College Department of M.Tech - Computer Science and Engineering Erode Thudupathi638057 India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy Thottiyam India Velalar College of Engineering and Technology Department of Medical Electronics Erode India

ISBN: (纸本)9798350313987

microstrip patch antennas, also known as planar antennas, have been widely used for several decades. To boost the efficacy of wireless technology, multi-resonance antennas must be used. Patch antennas suffer from a relatively narrow bandwidth. The proposed technology makes use of a small strip-patch antenna with a wide range of resonant frequencies. The proposed device can detect and capture signals at frequencies ranging from 1.6 to 3.2 gigahertz. This research study describes a small multi-broadband planar antenna that is suited for WLAN/WimAX and LTF/GS m/UmTS mobile device integration. GSm850/900, WLAN/WimAX, DCSIPCSIUmTS, and LTE2300/2500 frequency bands are covered by the aforementioned antenna. A monopole is present on an individual surface of the thin dielectric substrate, manifesting in a configuration that resembles the alphabets C and U. On the other side, there is a F-shaped stretch that is devoid of lumped parts. The antenna measures 15 mm long, 68 mm wide, and 0.8 mm thick, making it ideal for insertion into ultra-thin mobile devices. HFSS was used to simulate multiple parameters during antenna building, including radiation pattern, S-parameter, efficiency, peak gain, and return loss. The recommended antenna was built and tested using a vector network analyzer. The proposed antenna has the lowest return loss across all resonance frequencies, uses fewer phone antennae, and takes up less space. © 2023 IEEE.

关键词： 5G microstrip Patch Antenna mobile Handset Applications WLAN/WimAX

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Theoretical study and analysis of advanced wireless sensor network techniques in Internet of Things (IoT)

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measurement: Sensors 2024年 33卷

作者： Yamini, B. G, Pradeep D, Kalaiyarasi m, Jayaprakash G, Janani G S, Uthayakumar India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology India Department of Electronics and Communication Engineering Panimalar Engineering College India Department of Information Technology R.M.K. Engineering College India Department of Information Technology R.M.D Engineering College India Department of Electronics and Communication Engineering St.Joseph's Institute of Technology India

Wireless sensor networks those based on the Internet of Things (IoT) have evolved fast in numerous areas in recent years. Devices and sensors may interact with each other without human involvement in the IoT network. An IoT-based system where people and things are connected by wired and wireless technology including Wireless Sensor Networks (WSNs), ZigBee, NFC, RFID, GPRS, LTE, and Bluetooth is now one of the most popular technical ideas. The potential of the Internet of Things (IoT) has received enormous attention from the corporate and scientific communities during the past 10 years. The IoT can provide a number of significant benefits to application spaces. Researchers may be able to show crucial facts about IoT-based smart environmental monitoring systems. IoT has faced a variety of challenges, including authentication, differentiating evidence, accessibility and security and protection. Sociotechnical trust systems have also been examined in the past in various studies (STTS). Existing smart settings are now dealing with serious IoT security, protection, and STTS challenges. Trust-the-board will be highlighted and characterized, as well as how it should be utilized in the Internet of Things. As a result, the potential benefits of data aggregation solutions that use less energy while increasing the organization's lifespan are enormous. IoT-WSN data aggregation methods and computations have been reimagined. Specifically, this article examines the writing with reference to the views of sensor systems administrators in order to preserve energy and gather data. © 2024

关键词： Internet of things

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Utilizing Artificial Neural Networks and multivariate Patient Data for Anemia Detection using WEKA based Approach for Diagnosis

Utilizing Artificial Neural Networks and Multivariate Patien...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Prof Neeraj Bhargava Pramod Singh Rathore Parul Vaishnav manish Rai Professor School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India M.Tech Research Scholar MDS University Ajmer Department of Computer Science and Engineering (AI& ML) Manipal University Jaipur India

This research study describes a method that uses neural networks to figure out if a woman has anemia. An artificial neural network is a type of computer network that is based on the real neural networks that make up the structure of the human brain. In this paper, the WEKA tool is used for the classification process carried out using an ANN structure, which is referred to as one of the data mining approaches and is based on multiple perceptions. The network has seven inputs (RBC, HCT, mCV, mCH, mCHC, RDW, and WBC) and an output (decision). Simulations on 567 patients demonstrate that the Artificial Neural Network (ANN) accurately diagnose the disease. This study investigates the utilization of Artificial Neural Network (ANN)-supervised machine learning techniques for predicting anemia by using the data from Kaggle.

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