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A Comparative Analysis of Machine Learning Prediction Algorithms for Detecting IoT Botnet Activities

A Comparative Analysis of Machine Learning Prediction Algori...

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2024 International Conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Happy Chhikara, Rita Kashyap, Neeti The NorthCap University Dept. of Computer Science and Engineering Haryana Gurugram India

ISBN: (纸本)9798350375237

As the prevalence of Internet of Things (IoT) products keeps increase, the need for robust security measures becomes increasingly vital. Our paper addresses this concern by conducting a detailed comparative analysis of various machine learning methods, assessing their effectiveness in detecting and predicting malicious activities associated with IoT botnets. This paper meticulously examines initial identification methods for IoT botnet operations using advanced machine learning (ML) prediction techniques. To achieve this, we utilize the CICIoT2023 Dataset, a real-world IoT dataset obtained from networks that captures diverse device interactions and communication patterns. This dataset acts as the framework for constructing and evaluating numerous machine learning techniques, including support vector machines (SVM), k-nearest neighbours (k-NN), Naive Bayes, random forest (RF), logistic regression (LR), and decision trees (DT) approaches. Performance metrics such as accuracy, precision, F1-score, ROC curve, confusion matrix, and recall are employed to gain insights into the algorithms' capabilities in botnet detection. Furthermore, this paper delves into an examination of the trade-offs between computational complexity and detection accuracy. This analysis aids in selecting the most suitable ML techniques tailored to specific IoT security scenarios. This comparative analysis lays the groundwork for the advancement of IoT botnet discovery strategies, providing essential insights to researchers, practitioners, and industry experts working to strengthen IoT ecosystems against growing cyber threats. We anticipate that our findings will spark more conversations and developments in the sector, promoting the establishment of more robust and adaptable security measures across the IoT landscape. © 2024 IEEE.

关键词： Internet of things

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Prompt-Based Domain Incremental Learning with Modular Classification Layer 27

Prompt-Based Domain Incremental Learning with Modular Classi...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Wang, Boyu Ma, Yue Qiu, Qinru Dept. of Electrical Engineering and Computer Science Syracuse University United States

ISBN: (纸本)9781643685489

Continual learning is referred to as machine learning model's ability to learn a sequence of tasks or data over time without forgetting previously learned knowledge. In particular, we focus on domain incremental learning where the model trained on one domain, e.g., real-life photos, has to be augmented to accommodate data from new domains, e.g., cartoon images. Typical incremental learning relies on rehearsal-based methods, which store trained samples in a buffer and replay them during training alongside new data. This results in significant memory overhead and raise concerns about data privacy. Recently, prompt-based methods address these challenges and outperform them by utilizing the pre-trained Vision Transformer (ViT) and replacing the replay buffer with a prompt pool. However, existing prompt-based models fail to capture domain-specific knowledge and perform poorly in domain incremental learning. In this paper, we propose "domain prompt incremental learning via dynamic neural network", which combines the advantages of architecture-based and prompt-based methods. Specifically, our framework maintains two types of prompt: a instance-level prompt that improves the model's generalization ability is shared across all input samples;and a domain prompt that encodes domain-specific knowledge is assigned for each task. Furthermore, a separated classification head is trained for each domain so that the model has a pre-trained ViT and an ensemble of classification layers, one for each domain. The experimental results shows that our approach outperforms state-of-the-art methods by 2.3% in average on two domain incremental learning (DIL) benchmarks. © 2024 The Authors.

关键词： Contrastive Learning

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Impact of Human-in-the-Loop Paradigm in AI Labeling and Annotation 8

Impact of Human-in-the-Loop Paradigm in AI Labeling and Anno...

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8th IEEE International Conference on Computational system and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Davda, Meeth Jitendra Sindhu, D.V. R V College of Engineering Dept. of Computer Science Bangalore India

ISBN: (纸本)9798331505462

Incorporating human expertise directly into artifi-cial intelligence workflows, a concept known as the Human-in-the-Loop (HITL) paradigm, has proven to be a crucial approach in improving the processes of AI data labeling and annotation. This survey paper delves into the impact of the HITL paradigm on the quality and efficiency of data annotation in AI systems, emphasizing the synergistic benefits of combining human insights with automated techniques. This paper reviews a variety of recent methodologies that integrate human evaluative input with automated systems, showcasing the enhancement of data accu-racy and system reliability across several applications, including healthcare, autonomous vehicles, and language processing. It classifies various HITL techniques, evaluates the applicability of each and offers potential solutions. © 2024 IEEE.

关键词： Active learning

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Deploying NLP techniques in Twitch application to comprehend online user behaviour 1

Deploying NLP techniques in Twitch application to comprehend...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Sethurajan, Monikka Reshmi Natarajan, K. Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

Sentiment analysis of emotion entails identifying and analyzing subjective information from language, such as views and attitudes, and helps to improve data visualization by employing a variety of strategies, tactics, and tools. New media channels have significantly changed how people interact, exchange ideas, and share information. Numerous businesses have begun to mine this data, concentrating on social media since it is a popular platform for customers to voice their ideas about various brands or goods and because it gives users an audience, enhancing the visibility and potential effect of this input. So far, as the internet expands and modern technology advances, new avenues have emerged with a higher ability to offer businesses pertinent feedback on their goods. The goal of this study is to investigate the many forms of online behaviour by analyzing chat interactions from the well-known streaming service Twitch. Emotes were occasionally employed in place of letters, to get attention, or to communicate emotions. We propose a system that may take in chat logs from a certain stream, use a sentiment analysis algorithm to classify each message, and then display the data in a way that might permit users to analyze the results according to its polarity (positive message, negative message, or neutral message). This application must be sufficiently versatile to be used with any platform broadcast type and to handle the datasets at very huge level. © 2023 IEEE.

关键词： Sentiment analysis

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SQL Injection Detection using Machine Learning and Convolutional Neural Networks 5

SQL Injection Detection using Machine Learning and Convoluti...

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

作者： Misquitta, Jason Asha, S. Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474672

SQL Injection Attacks are a concern to databasedriven online applications because they can compromise the security and integrity of information in databases. It gives hackers the potential to read, delete, modify or copy data. Exploiting the flaws in the validation of user inputs provided through login pages is the simplest technique to conduct this attack. Each login page a user accesses can help to reveal more about who they are. Feedbacks sent by the server while running a SQL command can provide details on weaknesses in the database server's validation procedure. This research paper analyses code that focuses on detecting SQL injections. Different supervised Machine Learning algorithms are used to evaluate the model. A CNN model is also created for real-time identification of whether an input is a potential attack or not. © 2023 IEEE.

关键词： Personal computing

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Multilingual Approaches to Named Entity Recognition 3

Multilingual Approaches to Named Entity Recognition

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Savaram, Padmaja Tabassum, Suhana Bandu, Sasidhar Lakshitha, B. College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Dept. of Computer Science KSA Al-kharj Saudi Arabia University College of Engineering Osmania University Dept. of Computer Science and Engineering Hyderabad India Prince Sattam Bin Abdulaziz University Dept. of English Al-Kharj Saudi Arabia Vnrvjiet Jntu Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331518592

Named Entity Recognition (NER) represents a fundamental operation within Natural Language Processing (NLP), focused on the extraction and classification of specific entities embedded in textual data. Given the rising linguistic diversity present in digital communications, there is an escalating need for NER systems to be proficient in identifying and categorizing entities across a spectrum of languages. However, developing NER models for resource-poor languages presents significant challenges due to limited labeled data and linguistic resources. This paper examines methodologies for enhancing the ability of NLP models to perform NER across diverse languages by transferring knowledge from high-resource languages to low-resource languages. We delve into advanced approaches such as cross-lingual transfer learning, multilingual embeddings, and cross-lingual model adaptation. Cross-lingual transfer learning utilizes pre-trained models from high-resource languages to initialize NER systems for low-resource languages, thereby facilitating the effective transfer of linguistic knowledge across language boundaries. Multilingual embeddings provide a shared representation space for words across languages, facilitating the transfer of linguistic knowledge. Additionally, cross-lingual model adaptation techniques aim to adapt existing NER models to new languages through fine-tuning or domain adaptation. By enhancing the generalizability of NER models through cross-lingual knowledge transfer, we enable these models to perform effectively across diverse linguistic contexts, including both resource-rich and resource-poor languages. These advancements contribute to broader accessibility and applicability of NER technology across languages and cultures, facilitating more inclusive and comprehensive language processing applications. © 2024 IEEE.

关键词： Embeddings

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Unveiling Predictive Factors in Apple Quality: Leveraging LIME, SHAP, and the Synergy of Machine Learning Models and Artificial Neural Networks 6

Unveiling Predictive Factors in Apple Quality: Leveraging LI...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Chowdhury, Rituparna Das, Rudra Faruk Ananna, Fowzia Binta Saha, Arnob Nawar, Sadia Hosen, Md. Hamid East Delta University Dept. of Computer Science & Engineering Chittagong Bangladesh

ISBN: (纸本)9798350385779

In the realm of fruit quality assessment, the demand for accurate identification methods has grown significantly. Traditional measures, such as physical and chemical analyses, prove impractical due to their time-consuming nature and potential sample damage. To address this imperative need for precise and timely apple quality detection, this research explores the application of machine learning algorithms. Recognizing the drawbacks of traditional methods reliant on physical and chemical measures for predicting fruit maturity, non-destructive techniques, such as hyperspectral imaging and machine learning algorithms, are meticulously investigated. The study goes on to evaluate and compare the performance of various machine learning approaches, including Random Forest Classifier (RF), K-Nearest Neighbors Classifier (KNN), Support Vector Classifier (SVC), Extreme Gradient Boosting Classifier (XGB), Light Gradient Boosting Machine Classifier (LGBM), Artificial Neural Network (ANN), and Voting Classifier. Among these, the Artificial Neural Network emerges as the top performer, achieving an impressive accuracy rate of 92.87%. Following the thorough evaluation of the models, Explainable Artificial Intelligence (XAI) techniques, specifically LIME and SHAP, are employed. These techniques playa crucial role in interpreting and visualizing the intricate decision- making process of the models. The insights gained underscore the models' robustness while providing valuable information on the decision-making processes, contributing significantly to advancements in apple quality detection methodologies. © 2024 IEEE.

关键词： Lime

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Multi-Objective Task Scheduling in Cloud Environments using Dynamic Velocity-Adaptive Particle Swarm Optimization

Multi-Objective Task Scheduling in Cloud Environments using ...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Kumar, Ravi Vardhan, Manu Natinal Institute of Technology Dept. of Computer Science and Engineering Raipur India

ISBN: (纸本)9798350373783

In the realm of cloud computing, tackling the multi-objective task scheduling problem means finding the best way to assign tasks to virtual machines, taking into account conflicting goals like minimizing execution time, load balance and cost. This intricate task demands smart algorithms to strike a balance between these competing objectives, ensuring the most efficient use of resources and meeting user expectations in the cloud. Different optimization techniques, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), have been explored to handle this challenge and optimize task scheduling in the cloud environment. In classical PSO, particles can move at varying speeds in each iteration. In this study, we apply Velocity Stagnation Particle Swarm Optimization (VSPSO) variants where velocity stagnation applied during particles movement. This approach provides particles with a third movement option, enabling them to maintain the same velocity as in the previous iteration. VSPSO has a greater capacity to balance the exploration and exploitation. VSPSO adjusts the initial term of the PSO velocity equation to prevent premature convergence. Furthermore, the population of VSPSO is split into two swarms to ensure *** the inherent capabilities of PSO, VSPSO algorithm dynamically updates particle velocities to attain 5-6% total execution time 6-10% reduced cost and optimal balance load. Through meticulous comparisons with other algorithms, including Genetic Algorithm and Ant Colony Optimization, this study scrutinizes the effectiveness of our enhanced PSO approach. The overarching goal is to push the boundaries of multi-objective task scheduling in cloud computing by attaining minimal execution time, reduce cost and optimal balance load on each virtual machines. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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New Passive Damped-LCL-Filter Based Three-Phase Four-Leg Shunt Active Power Filter

New Passive Damped-LCL-Filter Based Three-Phase Four-Leg Shu...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Wang, Houqing Zhao, Yue Cao, Hui Peyman, Darvish Han, Dongfang Ma, Zhuxuan Zhang, Jinlai Wang, Zhenqi University of Arkansas Dept. of Electrical Engineering and Computer Science FayettevilleAR United States Xinjiang University Dept. of Computer Science and Technology Urumqi China Changsha University of Science and Technology Dept. of Mechanical and Electrical Engineering Changsha China

ISBN: (纸本)9798350376067

To suppress the resonance in an LCL filter, the passive damping method is often favored over the active damping due to its simplicity and robustness. However, the passive damping suffers from decreasing LCL filter's effectiveness in suppressing high-frequency harmonics and introducing power losses. In this paper, a new passive damping method is proposed for the three-phase four-leg shunt active power filter (SAPF) system, by adding resistors between the neutral line and the connection point of the three-phase lines. The selection of the damping resistor is guided by criteria that aim to minimize power losses while ensuring good stability characteristics. The SAPF system with the proposed passive damping demonstrates excellent attenuation of switching frequency harmonics and significant mitigation of interphase switching interference, enhancing harmonic compensation for nonlinear loads. The mathematical model based on single-phase equivalent circuit and control strategy of three-phase four-leg SAPF are analyzed respectively. Simulations and experiments with imbalanced loads are constructed to validate the theoretical analysis. © 2024 IEEE.

关键词： Equivalent circuits

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ConfigMaster: An Interactive Solution for system Management 8

ConfigMaster: An Interactive Solution for System Management

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8th IEEE International Conference on Computational system and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Ganapathi, Ananya Sivakumar, Parikshith Divya, K.V. Elango, Aditya Gupta, Hardik Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331505462

The 'ConfigMaster: An interactive solution for system management' is a simple utility written using Bash scripting. It is designed for Unix-like systems. The tool lets users easily configure system settings and do administrative tasks without having to remember complex commands. The tool minimizes user's work by automating tasks. The tool can be used even by users with very little technical knowledge, as it has a simple menu-based interface to manage things like network settings, software packages, and wireless connections directly from the terminal. By automating common tasks, the tool makes system management more efficient and accessible. By automating common tasks, the tool makes system management more efficient and accessible. It's very useful to simplify system configuration on Unix-like systems using scripting languages like Bash. 'ConfigMaster' aims to offer a smooth user experience with the incorporation of core management principles from an operating system, such as disk scheduling, process synchronization, and memory management etc. Being flexible itself, this tool will bring further improvements to make effective responses to emerging user needs. The framework being flexible, it can be expanded to add new features to configure under different scenarios. By integrating essentials operating system operations, 'ConfigMaster' enables users to efficiently perform system configurations along with promoting ease of use. © 2024 IEEE.

关键词： Wi-Fi

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