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Artificial Neural Network Technique for DDOS Attack Prediction in Cyber-IOT System 3

Artificial Neural Network Technique for DDOS Attack Predicti...

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Asad, Md Tiwari, Rakesh Kumar Technocrats Institute of Technology & Science Dept. of Computer Science Engineering Bhopal India

ISBN: (纸本)9798350342338

An intrusion-detection IoT framework is designed to analyze unauthorized access or security breaches in IoT networks. These systems typically use a combination of hardware and software to analyze network traffic, identify anomalies, and alert security personnel when a potential threat is detected. An intrusion-detection system (IDS) is a one type of security checking model that may be used to monitor a set of connections or devices in order to check for telltale indicators of possible infiltration. During the course of the last several years, there has been a general rise in the number as well as the intensity of cyber attacks. As a consequence of this, there is a need for study of security in cyber world and the prevention of assaults, such as the implementation of intrusion-detection. Businesses that rely on the internet absolutely have to make protecting their data online one of their top responsibilities. Within the scope of this study, this paper proposes a neural network technique as a means of anticipating assaults on IDS. Python Spyder is the software that make use of for this simulation. © 2023 IEEE.

关键词： Attack Cyber- IOT DDos IDS NN

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Deep Learning Model for Emotion Prediction from Speech, Facial Expression and Videos 5

Deep Learning Model for Emotion Prediction from Speech, Faci...

引用

5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Rajyalakshmi, Chepuri Lakshminadh, K. Reddy, M. Sathyam Dept. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665474672

The rapid development of computer vision and machine learning in recent years has led to fruitful accomplishments in a variety of tasks, including the classification of objects, the identification of actions, and the recognition of faces, among other things. Nevertheless, identifying human emotions remains one of the most difficult tasks to do. To find a solution to this issue, a significant amount of work has been put in. In order to achieve higher accuracy in this reactivity towards a variety of speeches and vocal -based methods, computer intelligence, natural language modelling systems, and other similar technologies have been used. The examination of the emotions has the potential to be useful in a number of different settings. Cooperation with human computers is one example of such a field. computers can help customers recognize emotions, make wiser decisions, and create more lifelike human-robot interactions. In recent times, there has been a lot of focus placed on the ability to forecast dynamic facial emotion expressions in videos. Therefore, this work proposes a deep convolutional neural networks (CNNs) model for emotion prediction from speech samples, facial expression images, and videos with enhanced prediction accuracy and reduced loss. In addition, the speech CNN model also utilizes mel-frequency Cepstrum coefficients (MFCC) as feature extraction from given speech samples. The proposed MFCC-CNN model resulted in superior performance than traditional models. © 2023 IEEE.

关键词： Neural network models

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Heart Disease Detection Using Machine Learning Models 2

Heart Disease Detection Using Machine Learning Models

引用

2nd International Conference on Machine Learning and data engineering, ICMLDE 2023

作者： Singh, Amrit Mahapatra, Harisankar Biswal, Anil Kumar Mahapatra, Madhumita Singh, Debabrata Samantaray, Milan Berhampur India Berhampur India Dept. of Computer Science Udayanath Autonomous College of Science and Technology Cuttack India Dept. of Computer Science and Engineering Delhi Technical Campus UP Greater Noida India Bhubaneswar India Department of Computer Science and Engineering Silicon Institute of Technology Bhubaneswar India

An enormous number of deaths occur every year as a result of heart disease, making it a major concern in world health. Improving patient outcomes and lowering death rates, early detection and correct diagnosis of cardiac disease play a key role. When the heart's arteries become blocked, oxygen-poor blood cannot reach the heart properly, resulting in coronary heart disease. Early detection of cardiac disease is viable because it reduces medical costs and potentially saves the patient's life. Recently presented methods have improved heart failure detection accuracy on testing data without sacrificing accuracy on training data, yet most of these algorithms are suffering from the issue of overfitting. Models that were created end up fitting the test data too well. In this study, we create a novel diagnostic system to address this issue, and the resulting system demonstrates high intelligence and excellent performance on both training and testing data. Machine learning (ML) algorithms have demonstrated promising potential in assisting healthcare professionals with timely and accurate diagnosis. In this paper, is based on supervised machine learning methods are decision tree (DT), random forest (RF), Support vector Machine (SVM), Principal Component Analysis(PCA). We compare their accuracy with each other by using bar plot. © 2024 Elsevier B.V.. All rights reserved.

关键词： Blood pressure

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Student Performance Analysis with Ensemble Progressive Prediction 5

Student Performance Analysis with Ensemble Progressive Predi...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Althaph, B. Sreenivasu, S.V.N. Reddy, D. Venkata Dept. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665474672

It is essential to have an accurate prediction of students' future performance in order to properly carry out the necessary pedagogical interventions that are required to assure students will graduate on time and with an acceptable degree. Even though there is a wealth of research on the topic of predicting student performance when it comes to finding solutions to problems or preparing for classes by utilizing datadriven methods, the topic of predicting student performance when it comes to completing degrees (for example, college programs) is much less researched and faces new challenges, there is a large amount of diversity among students in terms of their prior experiences and the courses they choose to take. The students' developing progress should be factored into the prediction. This study proposes a unique machine learning technique for forecasting student success in degree programs. This method may handle these important issues, and it is one of the main contributions of this research. The suggested technique is distinguished primarily by its two components. To begin, a structure with two layers, a bi-layered structure, is constructed for the purpose of creating predictions based on the changing performance states of students. Then, a strategy that is driven by data and is based on latent component models and Ensemble Progressive Prediction (EPP) based matrix factorization is suggested for the purpose of determining the relevance of the course, which is essential for the construction of effective base predictors. The proposed strategy achieves better performance than benchmark methods by conducting extensive simulations using a dataset of UCLA undergraduate student data that was gathered over the course of three years. © 2023 IEEE.

关键词： Forecasting

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Comparative Analysis of Multi-Model and Uni-Model Approaches using Time Distributed Bidirectional LSTM for Multidata Sentiment Analysis 14

Comparative Analysis of Multi-Model and Uni-Model Approaches...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Gupta, Krati Parmar, Mahesh Madhav Institute of Technology & Science Dept. of Computer Science and Engineering Gwalior India

ISBN: (纸本)9798350335095

The sentiment of the data contained in a text can be determined by analysis. This sentiment can be good, negative, or neutral. When carrying out this research, one may choose to employ either a uni-model or a multi-model technique. In the uni-model method, both the training and the analysis are performed using just a single model. The application of this strategy is uncomplicated and uncomplicated, but it might not be able to make sense of complicated or different facts, which would lead to wrong findings. Instead, the multi-model technique utilizes a number of different models, each of which is designed to examine a particular aspect of the data or a particular kind of attitude. This allows the multi-model strategy to produce more accurate results. The results of several different models might be combined in order to obtain a more in-dept. understanding of feelings. This method is more accurate overall and captures more complex expressions of emotion, but it requires more time and money to train personnel and put into practice. The evaluation of metrics was carried out using Multimodal and mono Model with Time Distributed Bidirectional LSTM in this research. Both the loss and the accuracy. The Multi-Model achieves the highest accuracy of 99.71 percent for the train, while the validation accuracy achieves 99.32 percent, and the Multi-Model achieves the least loss of 0.010 percent. © 2023 IEEE.

关键词： Sentiment analysis

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Decentralized Finance and Cross-Chain Interoperable Automated Market Maker - Using BlockChain 2

Decentralized Finance and Cross-Chain Interoperable Automate...

引用

2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Sarumathi, S. Saxena, Apoorv Raj Saxena, Yash Raj Sana, Mehreen S. Md Verma, Saritha Roy, Sneha Hkbk College of Engineering Dept. of Computer Science Engineering Bangalore India ClayStack Bangalore India

ISBN: (纸本)9798350328202

Decentralized Finance (DeFi) represents an alternative paradigm in financial infrastructure, operating a top the Ethereum Blockchain. DeFi leverages Automated Market Makers (AMMs) to facilitate the exchange of coins/tokens within Liquidity Pools (LPs). However, the current framework limits transactions to a single chain, resulting in prolonged processing times for cross-chain transactions. The principal drawbacks of existing AMMs include protracted transaction confirmation periods and constraints in single-chain functionality. This paper introduces Mixta, a web-based multi-chain interoperable AMM, aiming to address these limitations. Mixta empowers users to seamlessly exchange assets across different chains, ultimately reducing transaction confirmation times. © 2024 IEEE.

关键词： Finance

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Ensemble Approaches for Offensive Language Detection in Kannada 3

Ensemble Approaches for Offensive Language Detection in Kann...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Monish, M.N. Sooda, Kavitha B.M.S. College of Engineering Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350381931

In the realm of natural language processing, sentiment analysis remains a focal point due to its pivotal role in discerning and analyzing public sentiments. While past research has been dedicated to enhancing the performance of sentiment analysis models, this study delves into the potential benefits of ensemble techniques, with a special emphasis on Genetic Algorithm (GA) optimization, The main thrust of this research was to investigate whether merging multiple models and subsequently optimizing their weights via GA could elevate performance. Evaluations were thoughtfully conducted, focusing on the weighted precision, recall, and F1-scores of individual models. Additionally, the study compared the ensemble methods, both with and without GA optimization, The ensemble method, even without the GA optimization, secured an impressive F1-score of 0.73. Upon integrating the GA optimization, there was a gentle improvement in the ensemble's performance, achieving an F1-score of 0.75, In summing up, this study manifests a palpable enhancement in sentiment analysis outcomes when shifting from standalone models to a GA-optimized ensemble mechanism. The results, epitomized by an F1-score peaking at 0.75, underline the intrinsic merits of harnessing GA for weight optimization in ensemble constructs. © 2024 IEEE.

关键词： Genetic algorithms

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Diabetic Retinopathy Prediction and Analysis Using Ensemble Classifier in Deep Learning Technique

Diabetic Retinopathy Prediction and Analysis Using Ensemble ...

引用

2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Hemanth, S.V. Saravanan, A. Dept. of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9781665492942

The primary cause of vision impairment is diabetic retinopathy (DR). Diabetes can cause diabetic retinopathy, a disorder that damages the retinal blood vessels and affects the eyes. According to the analysis, 90% of sight-threatening cases may be cured if it is discovered early. We proposed an Ensemble algorithm to predict diabetes from retinal images. The primary goal of this work is to enhance the accuracy and predict the result with minimum time consumption. The proposed system is a two-phase work in which, the initial bootstrap resampling method is employed for extracting the subsamples. Next, the classified decision tree establishes a voting system for finalizing the prediction result. The performance accuracy achieved by the proposed Ensemble algorithm is compared with the logistic regression and Deep neural network. The observation shows proposed enhanced Ensemble mechanism attained 96% accuracy, which is more prominent than the other models. © 2023 IEEE.

关键词： Decision trees

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DivLog: Log Parsing with Prompt Enhanced In-Context Learning 24

DivLog: Log Parsing with Prompt Enhanced In-Context Learning

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Xu, Junjielong Yang, Ruichun Huo, Yintong Zhang, Chengyu He, Pinjia The Chinese University of Hong Kong Dept. of Computer Science & Engineering Hong Kong Eth Zurich Switzerland School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data China

ISBN: (纸本)9798400702174

Log parsing, which involves log template extraction from semi-structured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from limited effectiveness for two reasons. First, traditional data-driven log parsers solely rely on heuristics or handcrafted features designed by domain experts, which may not consistently perform well on logs from diverse systems. Second, existing super-vised log parsers require model tuning, which is often limited to fixed training samples and causes sub-optimal performance across the entire log source. To address this limitation, we propose Di-vLog, an effective log parsing framework based on the in-context learning (ICL) ability of large language models (LLMs). Specifically, before log parsing, DivLog samples a small amount of offline logs as candidates by maximizing their diversity. Then, during log parsing, DivLog selects five appropriate labeled candidates as examples for each target log and constructs them into a prompt. By mining the semantics of examples in the prompt, DivLog generates a target log template in a training-free manner. In addition, we design a straightforward yet effective prompt format to extract the output and enhance the quality of the generated log templates. We conducted experiments on 16 widely-used public datasets. The results show that DivLog achieves (1) 98.1% Parsing Accuracy, (2) 92.1% Precision Template Accuracy, and (3) 92.9% Recall Template Accuracy on average, exhibiting state-of-the-art performance. © 2024 ACM.

关键词： Semantics

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Parametric Cyber Defense: A Sophisticated Machine Learning Architecture for Advanced Intrusion Detection and Threat Classification 5

Parametric Cyber Defense: A Sophisticated Machine Learning A...

引用

5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Lakshmi, S. Maalan, M.R. Kishore Kumar, R. St. Joseph's College of Engineering Dept. of Computer Science Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

In contemporary network environments, effective countermeasures against cybersecurity threats require highly effective Intrusion Detection Systems (IDS). This paper proposes an advanced methodology to enhance IDS by employing parametric analysis on network traffic characteristics. Through careful data selection and preprocessing from a comprehensive dataset, data integrity is maintained, with features such as packet size and transfer duration being leveraged. Machine learning algorithms, including Decision Tree, Extra Tree, and xgBoost, are applied to detect and classify various cyberattack categories. In addition, advanced feature selection and dimensionality reduction techniques are utilized to improve model efficiency. Extensive experimentation demonstrates superior accuracy in identifying and classifying cyber threats while reducing the occurrence of false positives. The proposed framework strengthens cybersecurity infrastructures through the integration of advanced machine learning techniques to address evolving threats. © 2024 IEEE.

关键词： Adversarial machine learning

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