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Detection and Mitigation of DDOS Attack in SDN Using Feature Based RF & MLP Approach

Detection and Mitigation of DDOS Attack in SDN Using Feature...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Saranraj K Sumugar M Anantha Moorthy R Gowthami N S. Tamilselvi Information Technology National Engineering College Kovilpatti India Electronics and Communication Engineering National Engineering College Kovilpatti India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Software Defined Networking (SDN) is an architecture that decouples the control plane from the data plane, allowing for centralized oversight and flexible configuration of network resources via software. However, this centralization of the controller makes it especially susceptible to Distributed Denial of Service (DDoS) attacks.. To effectively prevent and mitigate such attacks, deploying a robust machine learning model is crucial. In this project, the Genetic Algorithm and Chimp Optimization Algorithm were employed as optimal feature selection methods to identify and extract the most representative and distinguishing features from the SDN_DDoS_2020 dataset, which contains ICMP, TCP, and UDP attack traffic (sourced from Mendeley). The refined feature subset was then used to train and evaluate the performance of two machine learning algorithms commonly used are Random Forest (RF) and Multilayer Perceptron (MLP). Furthermore, a dedicated mitigation model was developed to block or redirect the flood requests from TCP, UDP, and ICMP attack traffic. Among the models tested, the combination of the Chimp Optimization Algorithm with the MLP classifier demonstrated superior performance, achieving an impressive accuracy of 98.68%. Notably, this model also exhibited a low false alarm rate of 0.74 compared to all other models, enhancing its reliability in real world scenarios where accurate attack detection and minimal false positives are essential.

关键词： Accuracy machine learning algorithms Prevention and mitigation Feature extraction Denial-of-service attack Software defined networking Computer crime Protection Optimization Classification tree analysis

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Offensive Message Spam Detection in Social Media Using Long Short-Term Memory

Offensive Message Spam Detection in Social Media Using Long ...

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Delhi Section conference (DELCON), IEEE

作者： Sivakumar M Abishek S. A Karthik N J. Vanitha Dept. of Electrical and Electronics Engineering National Institute of Technology Puducherry India Dept. of Computer Science Engineering National Institute of Technology Puducherry India National Institute of Technology Puducherry India E.G.S.Pillay Engineering College Nagapattinam India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

In this world, online communication has become an essential part in our daily lives. Almost everyone uses social media platforms to communicate with the whole world. But this also leads to a major problem: cyberbullying. This paper proposes a system to detect users who spam offensive messages repeatedly and ban them using natural language processing and machine learning. The system would be beneficial to service providers, social media platforms, and individuals, allowing us to strive for a world of harmony. The amalgamation of NLP, Big data, and ML approaches presents a robust and efficient solution for offensive message spam detection. The findings of this project will shed light on the potential of these technologies to curb the proliferation of offensive content, thereby fostering a healthier and more respectful online communication ecosystem. This paper delves on offensive spam detection using the LSTM model, and the obtained MSE of this model is 12.58.

关键词： Ecosystems Cyberbullying machine learning Big data Natural language processing Long short term memory

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The Usage of machine learning on Penetration Testing Automation

The Usage of Machine Learning on Penetration Testing Automat...

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Electronic and Electrical engineering and Intelligent System (ICE3IS), international conference on

作者： Clintswood Delbert Giovanni Lie Lexiandy Kuswandana Nadia Said Achmad Derwin Suhartono Computer Science Department Cyber Security Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Penetration testing is a method to assess the security within a network by performing or simulating a real-world cyber-attack on the network. It has been one of the best ways preferred by organizations to strengthen their network defenses against cyber threats. However, penetration testing proves that it has several drawbacks such as requiring a significant number of skills and time to perform. A survey made by The international Information System Security Certification Consortium also shows that the world is lacking cybersecurity workforces. To address this problem, this paper conducts research regarding the usage of machine learning to automate penetration testing activity. To start off, automated penetration testing tools will be created using machine learning algorithms, specifically reinforcement learning and deep reinforcement learning algorithms. The second step is to put the tools in a learning stage where it will be provided with the MITRE ATT&CK Framework. This framework will be the base for the attacks and exploitations used within the tools. The third step is to conduct a series of automated penetration testing over different target networks. The result will be then compared and analyzed with a manual penetration testing report to see how efficient and better it is compared to a manual penetration test. Lastly, the final data will then be collected for future developments and recommendations.

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Predicting Anemia with Precision: Employing machine learning Ensemble Modeling and Hyperparameter Tuning

Predicting Anemia with Precision: Employing Machine Learning...

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Advancement in Technology (ICONAT), international conference for

作者： Anmol Rana Nivedita Sharma Department of Mathematics Chandigarh University Mohali-Punjab India Department of Computer Science and Engineering Chandigarh University Mohali-Punjab India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Anemia is one of the diseases that can be easily acquired, but should not be underestimated, as it can be cured only after correct diagnosis and correct treatment. For this, in the present work, we propose a solid and robust approach based on ensemble modeling to machine learning that allows the accurate prediction of anemia. Merging Random Forest, Decision Tree, and logistic regression we enhance the model and its generality by providing more reliability and strengthening the methods used in this model by ensembling them. As for the choice of optimal model parameters, hyperparameter tuning is the key step that has to be conducted to maximize the effectiveness of each type of method. That is why our method achieves an accuracy of 96%, and the fine-tuning of all hyperparameters and ensemble structures shows the efficiency of the approach. The study proves that, in the context of medicine, machine learning may be quite effective, for example in early detection of anemia which can lead to better outcomes for the patient and more targeted therapies. This work presents a general approach to diagnosing anemia in several population groups, providing to the increasing literature area of prediction in health sciences.

关键词： Solid modeling Accuracy Merging Medical treatment Predictive models Solids Reliability Tuning Random forests Medical diagnostic imaging

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Hybrid Model for Sentiment Analysis of Whatsapp data

Hybrid Model for Sentiment Analysis of Whatsapp Data

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Advances in Computing, Communication, Embedded and Secure Systems (ACCESS), international conference on

作者： Royal Kaushal Raman Chadha Department of Computer Science and Engineering Chandigarh University Mohali India

Sentiment analysis, also called opinion mining (OM), is an approach to analyzing the sentiments expressed in data. This approach uses Natural Language Processing to classify the data based on emotions in different classes. Various Social media data are analyzed to determine sentiment, and machine learning (ML) techniques classify the data. This study utilizes ML models to analyze sentiment in WhatsApp data. The sentiment analysis process includes some steps, such as to pre-process the data, extract the features, and classify the data. The initial stage contributes to the clean-up of raw data and transforms it to make it suitable for analysis. Feature extraction is a stage to retrieve a relevant feature from the pre-processed data that contribute to determining sentiment. Finally, machine learning algorithms classify data to determine the sentiments expressed in the text. This work proposes a voting classifier which is hybrid architecture comprising SVM, KNN, and a Decision tree. Python is executed to simulate the suggested algorithm, and its performance is evaluated based on accuracy, precision, and recall metrics. These parameters are useful in measuring the efficiency of the algorithm in accurately classifying the sentiments existing in the data.

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A Combinatorial Approach to Hyperparameter Optimization

A Combinatorial Approach to Hyperparameter Optimization

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AI engineering - Software engineering for AI (WAIN), IEEE/ACM Workshop on

作者： Krishna Khadka Jaganmohan Chandrasekaran Yu Lei Raghu N. Kacker D. Richard Kuhn University of Texas at Arlington Arlington TX USA National Security Institute Virginia Tech Arlington VA USA Information Technology Laboratory National Institute of Standards and Technology Gaithersburg MD USA

ISBN: (数字)9798400705915

ISBN: (纸本)9798350352191

In machine learning, hyperparameter optimization (HPO) is essential for effective model training and significantly impacts model performance. Hyperparameters are predefined model settings which fine-tune the model’s behavior and are critical to modeling complex data patterns. Traditional HPO approaches such as Grid Search, Random Search, and Bayesian Optimization have been widely used in this field. However, as datasets grow and models increase in complexity, these approaches often require a significant amount of time and resources for HPO. This research introduces a novel approach using t-way testing—a combinatorial approach to software testing used for identifying faults with a test set that covers all t-way interactions—for HPO. -way testing substantially narrows the search space and effectively covers parameter interactions. Our experimental results show that our approach reduces the number of necessary model evaluations and significantly cuts computational expenses while still outperforming traditional HPO approaches for the models studied in our *** CONCEPTS•Computing methodologies → machine learning;•Software and its engineering → Software maintenance tools.

关键词： Training Software testing Fault diagnosis Software maintenance Computational modeling Hyperparameter optimization data models

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Real-time Crowd Detection of Vehicles: Approaches and Open Challenges

Real-time Crowd Detection of Vehicles: Approaches and Open C...

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Advances in engineering Science and Technology (AEST), international conference on

作者： Maad Issa AL Tameemi Saad Talib Hasson Hilal Al-Libawy Department of Networks College of Information Technology University of Babylon Babylon Iraq & University of Baghdad Baghdad Iraq Department of Networks College of Information Technology University of Babylon Babylon Iraq Department of Electrical Engineering College of Engineering University of Babylon Babylon Iraq

ISBN: (数字)9798331502690

ISBN: (纸本)9798331502706

Real-time vehicle crowd detection has become an urgent necessity technique for many applications like traffic control management, monitoring, and self-driving. Traffic crowds sometimes happen because many vehicles are located in the same place or take the same route without alternative routes. This leads to unwanted crowding, even though alternative paths can be taken. Many recent technologies have been suggested to solve crowd issues such as smart traffic lights and optimized GPS routes and others. However, in-vehicle crowding detection solutions are rarely based on embedded devices using machine learning algorithms. Smart embedded engineering systems with limited resources can challenge implementing efficient crowded vehicle detection algorithms. Recent machine learning technologies have tried to overcome these challenges. This review paper aims to provide a clear overview on embedded systems, applications, case studies, used datasets, online development platforms, machine learning approaches, and available frameworks for Lite deep and machine learning algorithms to be a valuable resource for researchers and practitioners alike allowing to suggest both create image dataset and smart embedded system of crowd vehicle detection. A list of an existing vehicle crowd detection approaches systems and technologies has been listed in a table for comparison. Various methodologies, open challenges, and future directions in these domains are also discussed to be utilized in future works.

关键词： Training Deep learning machine learning algorithms Embedded systems Reviews Vehicle detection Traffic control Real-time systems Monitoring Global Positioning System

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Agricultural AI: Integrating Federated learning and CNN in Papaya Leaf Disease Detection

Agricultural AI: Integrating Federated Learning and CNN in P...

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Innovation in Technology (INOCON), IEEE international conference for

作者： Sonal Malhotra Ashish Garg Vinay Kukreja Shiva Mehta Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

This study paper gives a new way to find and classify diseases in papaya leaves using federated learning with convolutional neural networks (CNN). The research is about finding four levels of lousy health in papaya leaves. It uses information from four clients to do this job well. Using federated learning helps turn local data understanding into a united global view, which is essential for proper disease diagnosis. They used federated averaging to join data findings from each client, creating an overall global model. The model's performance was closely checked using big, small, and weighted averages. Clients' results showed mixed but essential success in classifying diseases. In simple terms, the client named sv_1 had a big average score of 87.84 and smaller averages for each class at 87. It showed consistent performance in various classes equally well. Client SV_2 had slightly better numbers. Its average score was 90.31, and the weighted average shot up to 90.03, showing that it's getting better at finding diseases than others. Client sv_3 did very well in scoring, with a significant average of 94.98, a weighted score of 94.95 and then another measure called micro average, which had the same score as the overall one with nothing above all else like high tech classifications, leading it to be the best among its competitors by showing off such skills you can not ignore or In the end, client SV_4 showed strong results, with an average of 92.89 for all macro things and 92.74 for small micro parts connected to it. Finally, the study gives a detailed plan for correctly finding how serious papaya leaf diseases are using federated learning and CNN. The data and tests show that the model works well and can be trusted, helping to create better solutions in farm disease control that do not depend on one centre.

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Advanced Generative AI Methods for Academic Text Summarization

Advanced Generative AI Methods for Academic Text Summarizati...

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Computing and machine Intelligence (ICMI), international conference on

作者： Zaema Dar Muhammad Raheel Usman Bokhari Akhtar Jamil Esraa Mohammed Alazawi Alaa Ali Hameed Department of Computer Science National University of Computing and Emerging Sciences Islamabad Pakistan Department of Computer Engineering University of Baghdad Baghdad Iraq Department of Computer Engineering Istinye University Istanbul Tureky

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

The exponential growth of scientific literature emphasizes the need for employing advanced techniques for effective text summarization, which can significantly speed up the research process. This study tackles the challenge by advancing scientific text summarization through AI and deep learning methods. We delve into the integration and fine-tuning of cutting-edge models, including LED_Large, Pegasus variants, and BART, aiming to refine the summarization process. Unique combinations, such as SciBERT with LED_Large, were investigated to ensure the capture of critical details frequently missed by traditional methods. This novel approach led to notable improvements in summarization effectiveness. Our findings indicate that models like LED_Large excel in quickly adapting to training data, achieving impressive semantic understanding with fewer training epochs, evidenced by achieving a FRES score of 28.5852 and ROUGE scores, including a ROUGE-l F1-Score of 0.4991. However, while extensively trained models like BART _large and Pegasus displayed strong semantic capabilities, they also pointed to the necessity for refinements in readability and higher-order n-gram overlap in the produced summaries.

关键词： Training Deep learning Adaptation models Reviews Generative AI Semantics Natural languages

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An Enhanced Diagnostic Framework for Hepatitis C Prediction Using machine learning with Advanced Feature Selection Techniques

An Enhanced Diagnostic Framework for Hepatitis C Prediction ...

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Embedded Systems and Artificial Intelligence (ESAI), international conference on

作者： Asif Ur Rahman Adib Md. Asib Rahman Jahin Abdullah Al Mahmud Nafiz Asif Newaz Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur Bangladesh

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

Hepatitis C is a major global health concern and one of the leading causes of chronic liver disease, liver cirrhosis, and liver cancer worldwide. In this paper, an intelligent machine-learning algorithm-based Hepatitis C diagnosis system has been proposed. A publicly available Hepatitis C dataset has been utilized to develop the prediction framework. The dataset shows a significant imbalance between positive and negative cases, which complicates the classification process, a common challenge in healthcare datasets. Appropriate strategies have been undertaken to achieve a reliable decision framework. Two feature selection techniques, Sequential Feature Selection (SFS) and Recursive Feature Elimination (RFE), were incorporated to identify the most representative set of attributes and improve the system’s diagnostic performance. Six different classifiers were employed for prediction, among which Adaboost with the SFS algorithm achieved the highest MCC score of $\mathbf{9 2. 6 \%}$, providing the most effective prediction of Hepatitis C. This framework offers a robust approach for early diagnosis, potentially benefiting patient care and optimizing healthcare resources.

关键词： Accuracy machine learning algorithms Sensitivity Liver diseases Medical services machine learning Feature extraction Prediction algorithms Classification algorithms Reliability

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