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ANFIS-based Multilayered Algorithm for Botnet Detection

ANFIS-based Multilayered Algorithm for Botnet Detection

2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2023

作者： Thamaraimanalan, T. Venkatesan, C. Ramkumar, M. Sivaramakrishnan, A. Marimuthu, M. Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Hkbk College of Engineering Department of Electronics and Communication Engineering Bangalore India Knowledge Institute of Technology Department of Computer Science and Business System Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra pradesh India Sona College of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350337426

In this study, a multilayered botnet detection system for network-based botnet detection based on ANFIS is suggested. Two layers make up the suggested system, one of which is utilized for feature extraction and the other for botnet detection. Using mean and variance, the feature extraction layer pulls features from network traffic data. The botnet detection layer employs ANFIS to identify botnet activity based on the collected features. Using the CICIDS2017 dataset, the suggested system's performance is assessed. The experimental findings demonstrate that the suggested algorithm works better than other current botnet detection techniques regarding accuracy, precision, and recall. The proposed system obtained 99.83% accuracy, 99.75% precision, and 99.91 % recall. The proposed multilayered ANFIS-based botnet detection system offers an excellent way to identify botnet activity in a network. It can be utilized in various network security applications, such as threat intelligence, malware detection, and intrusion detection. © 2023 IEEE.

关键词： Feature extraction

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Search in imperfect information games using online Monte Carlo counterfactual regret minimization 28

Search in imperfect information games using online Monte Car...

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28th AAAI Conference on Artificial Intelligence, AAAI 2014

作者： Lanctot, Marc Lis, Viliam Bowling, Michael Department of Knowledge Engineering Maastricht University Netherlands Department of Computer Science FEE Czech Technical University Prague Czech Republic Department of Computing Science University of Alberta Canada

ISBN: (纸本)9781577356653

Online search in games has always been a core interest of artif icial intel1igencc Advances made in search for perfect inform ation games (such as Chess. Checkers, Go. and Backgamm on) have led to Al capable of defeating the world's top hum an experts. Search in imperfect information games (such as Poker, Bridge, and Skat) is significantly more challengi ng due to the complexities introduced by hidden information. In this paper. we present Online Outcome Sampling (OOS). the first imperfect information search algorithm that is guara nteed to converge to an equilibrium strategy in two-player zero-sum games. We show that OOS avoids common probl ems encountered by existing search algorithms and we exp erimentally evaluate its convergence rate and practical perf ormance against benchmark strategies in Liar's Dice and a variant of Goofspiel. We show that unlike with Information Set Monte Carlo Tree Search (ISMCTS) the exploitabilily of the strategies produced by OOS decreases as the amount of search time increases. In practice. OOS performs as well as ISMCTS in head-to-head play while producing strategies with lower exploitability given the same search time. © Copyright 2014. Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Monte Carlo methods

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ECG Signal Feature Extraction and SVM Classifier Based Cardiac Arrhythmia Detection 2

ECG Signal Feature Extraction and SVM Classifier Based Cardi...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Venkatesan, C. Thamaraimanalan, T. Ramkumar, M. Sivaramakrishnan, A. Marimuthu, M. HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Knowledge Institute of Technology Department of Computer Science and Business Systems Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Sona College of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350397635

In a variety of medical applications, electrocardiogram (ECG) records are essential for the identification of cardiovascular disorders. Cardiovascular issues such cardiac arrhythmias and coronary heart disease (CHD) are among the most prevalent and can result in cardiac arrest or sudden cardiac death. This study employs ECG signal preprocessing and feature extraction to identify cardiac arrhythmias and evaluate CHD risk. This research stresses the use of a Support Vector Machine (SVM) classifier for cardiac arrhythmia identification after ECG signal preprocessing. The preprocessed ECG signal is then subjected to arrhythmic beat classification to find anomalies. Extracted R-peaks from the ECG signal are divided into normal and arrhythmic risk subjects using the SVM classification-based approach for abnormality identification. When compared to other similar classifiers, the K-Nearest Neighbor (KNN) classifier offers the highest classification accuracy of 97.5%. © 2023 IEEE.

关键词： Electrocardiograms

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A fast two-stage algorithm for computing SimRank and its extensions

A fast two-stage algorithm for computing SimRank and its ext...

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1st International Workshop on Graph Database, IWGD 2010, 1st International Workshop on Advanced Techniques on XML Data Management, XMLDM 2010, 2nd International Workshop on WCMT 2010, Held in Conj. with WAIM 2010

作者： Jia, Xu Liu, Hongyan Zou, Li He, Jun Du, Xiaoyong Key Labs. of Data Engineering and Knowledge Engineering Ministry of Education China Department of Computer Science Renmin University of China 100872 China Department of Management Science and Engineering Tsinghua University 100084 China

ISBN: (纸本)3642167195

Similarity estimation can be used in many applications such as recommender system, cluster analysis, information retrieval and link prediction. SimRank is a famous algorithm to measure objects' similarities based on link structure. We observe that if one node has no in-link, similarity score between this node and any of the others is always zero. Based on this observation, we propose a new algorithm, fast two-stage SimRank (F2S-SimRank), which can avoid storing unnecessary zeros and can accelerate the computation without accuracy loss. Under the circumstance of no accuracy loss, this algorithm uses less computation time and occupies less main memory. Experiments conducted on real and synthetic datasets demonstrate the effectiveness and efficiency of our F2S-SimRank. © 2010 Springer-Verlag.

关键词： Cluster analysis

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Quality aware privacy protection for location-based services

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12th International Conference on Database Systems for Advanced Applications, DASFAA 2007

作者： Zhen, Xiao Xiaofeng, Meng Jianliang, Xu School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering MOE Department of Computer Science Hong Kong Baptist University

ISBN: (纸本)9783540717027

Protection of users' privacy has been a central issue for location-based services (LBSs). In this paper, we classify two kinds of privacy protection requirements in LBS: location anonymity and identifier anonymity. While the location cloaking technique under the k-anonymity model can provide a good protection of users' privacy, it reduces the resolution of location information and, hence, may degrade the quality of service (QoS). To strike a balance between the location privacy and QoS, we present a quality-aware anonymity model for protecting location privacy while meeting user specified QoS requirements. In the model, a mobile user can specify the minimum anonymity level requirement upon location privacy as well as the maximum cloaking latency and the maximum cloaking region size requirements upon QoS. In accordance with the model, we develop an efficient directed-graph based cloaking algorithm to achieve both high-quality location anonymity and identifier anonymity. The performance objective is to maximize the cloaking success rate under the privacy and QoS constraints. Furthermore, we introduce an option of using dummy locations to achieve a 100% cloaking success rate at the cost of communication overhead. Experimental results show the effectiveness of our cloaking algorithm under various privacy and QoS requirements. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Tracking (position)

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Machine Learning-Based Predictive Models for Water Quality Index—An Analysis and Comparison 2nd

Machine Learning-Based Predictive Models for Water Quality I...

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2nd International Conference on Advances in Water Treatment and Management, ICAWTM 2023

作者： Shah, Vaishvi Ramanuj, Ashwini Patel, Krisha Jethloja, Shruti Swain, Debabrata Kumar, Manish Department of Computer Science and Engineering Pandit Deendayal Energy University Knowledge Corridor Gujarat Gandhinagar382007 India Department of Electronics and Communication Engineering Pandit Deendayal Energy University Knowledge Corridor Gujarat Gandhinagar382007 India

ISBN: (纸本)9789819759545

Water, being the most vital aspect of all living creatures and the environment, its availability and quality are paramount concerns for global sustainability. Water quality is a fundamental property that determines the suitability of water for a certain application based on its intended use and the required physical, chemical, and biological characteristics. Recent activities of humans has led to the concentrations of substances in natural water resources to exceed the ideal concentration values, resulting in a modification of the natural composition. This effect has led us to find an adequate solution for water quality index (WQI) calculation as well as the prediction of water quality classification (WQC) which is suitable for usage in everyday life, from publicly available data with ML-based predictive models being a driving force in the process. A number of preprocessing techniques were performed on the dataset, which includes the relevant imputation of some missing categorical and numeric data points, along with the use of unsupervised Non-Negative Matrix Factorization (NNMF) technique and balancing of the target data using SMOTE algorithm. Different machine learning models have been applied as a part of model training for WQC forecasting. The results obtained from these models were assessed, examined, and compared on the basis of their accuracy, recall, precision, as well as F1-score. Prediction results demonstrated that the Gradient Boosting Classifier achieved the best results (96.99%) for WQC prediction. Water Management resources can benefit greatly from this kind of promising research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Non-negative matrix factorization

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A Simple but Effective Approach for Unsupervised Few-Shot Graph Classification 24

A Simple but Effective Approach for Unsupervised Few-Shot Gr...

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33rd ACM Web Conference, WWW 2024

作者： Liu, Yonghao Huang, Lan Cao, Bowen Li, Ximing Giunchiglia, Fausto Feng, Xiaoyue Guan, Renchu College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education China

ISBN: (纸本)9798400701719

Graphs, as a fundamental data structure, have proven efficacy in modeling complex relationships between objects and are therefore found in wide web applications. Graph classification is an essential task in graph data analysis, which can effectively assist in extracting information and mining content from the web. Recently, few-shot graph classification, a more realistic and challenging task, has garnered great research interest. Existing few-shot graph classification models are all supervised, assuming abundant labeled data in base classes for meta-training. However, sufficient annotation is often challenging to obtain in practice due to high costs or demand for expertise. Moreover, they commonly adopt complicated meta-learning algorithms via episodic training to transfer prior knowledge from base classes. To break free from these constraints, in this paper, we propose a simple yet effective approach named SMART for unsupervised few-shot graph classification without using any labeled data. SMART employs transfer learning philosophy instead of the previously prevailing meta-learning paradigm, avoiding the need for sophisticated meta-learning algorithms. Additionally, we adopt a novel mixup strategy to augment the original graph data and leverage unsupervised pretraining on these data to obtain the expressive graph encoder. We also utilize the prompt tuning technique to alleviate the overfitting and low fine-tuning efficiency caused by the limited support samples of novel classes. Extensive experimental results demonstrate the superiority of our proposed approach, significantly surpassing even leading supervised few-shot graph classification models. Our code is available here. © 2024 ACM.

关键词： Graph neural networks

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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

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Data Intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

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Opinion mining in e-commerce: Evaluating machine learning approaches for sentiment analysis

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Results in Control and Optimization 2025年 19卷

作者： Lakshmi, L. Ali, Ali B.M. Devi, K Dhana Sree Rafiq, Muhammad Shernazarov, Iskandar Othman, Nashwan Adnan Khan, M. Ijaz Department of DS & AI Faculty of Science and Technology ICFAI Foundation for Higher Education (IFHE) Hyderabad -501203 India Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of CSE GSOT GITAM University Hyderabad India College of Business Administration Prince Mohammad Bin Fahd University Saudi Arabia Department of Chemistry and Its Teaching Methods Tashkent State Pedagogical University Tashkent Uzbekistan Department of Computer Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Computer Engineering Al-Kitab University Altun Kupri Iraq Department of Mechanical Engineering College of Engineering Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Department of Mechanical Engineering Lebanese American University Beirut Lebanon

In recent years, opinion mining has played a major role in analyzing text data from various sources such as Amazon, Capterra, Facebook, Google, GetApp, and Twitter. It enables companies to actively refine their business strategies. Sentiment analysis involves interpreting and classifying customer emotions (positive, neutral, and negative) expressed in reviews using sentiment analysis techniques such as BING and AFINN. This paper presents four approaches for customer review analysis and classification: the grade-based approach, content-based approach, content-based NRC-Emotion Lexicon approach, and collaborative approach. We employ three machine learning algorithms—stacking, random forest, and LogitBoost—to evaluate the performance of these approaches. A real-time dataset from Amazon product reviews is used for training and testing the model. Empirical results reveal that the collaborative approach outperforms the grade-based, content-based, and content-based NRC-Emotion Lexicon approaches across all three machine learning algorithms. Additionally, all approaches demonstrate outstanding performance when using the boosting algorithm for customer review classification. © 2025 The Author(s)

关键词： Machine learning NRC-lexicon Product reviews Sentiment analysis Text classification

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Performance Analysis of Health Care Expense Anticipation using Computational Intelligence 1

Performance Analysis of Health Care Expense Anticipation usi...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Gopalakrishnan, A. Kumarganesh, S. Divya Bharathi, G. Anthoniraj, S. Natarajan, Suresh Kumar Gochhait, Saikat Knowledge Institute of Technology Department of Artificial Intelligence and Data Science Salem India Knowledge Institute of Technology Department of Electronics and Communication Engineering Salem India Mahendra Institute of Technology Department of Computer Science and Engineering Namakkal India School of Computer Science and Engineering Ramanagara India Symbiosis International Deemed University Symbiosis Institute of Digital and Telecom Management Maharashtra Pune India

ISBN: (纸本)9798350348637

The escalating costs of healthcare globally necessitate the development of accurate prediction models to address the financial strain on individuals, families, businesses, and governments. This research employs linear regression models to forecast healthcare expenditures, analyzing the intricate relationship between various factors and costs. A comprehensive literature survey explores topics such as the impact of environmental pollution on healthcare spending, prediction of surgical expenses, budget impact analysis for cancer treatment, and the integration of big data in healthcare infrastructure investments. The methodology involves gathering a diverse dataset encompassing patient demographics, medical history, treatment methods, and associated expenditures. Exploratory data analysis employs Pearson's correlation coefficient to identify trends and correlations between variables. Two linear regression models are proposed and compared, with the second model excluding non-significant variables for enhanced accuracy and interpretability. Results indicate that the second model, with refined feature selection, outperforms the first in terms of mean squared error and R-squared values. A comparative analysis of different studies underscores the diverse findings in healthcare economics, ranging from the impact of CO2 emissions on health to predicting surgery costs and budgetary savings in cancer treatment. The research contributes to the growing body of knowledge in healthcare economics, providing decision-makers with a valuable tool for precise expenditure estimation. The emphasis on linear regression models aids stakeholders in understanding factors influencing healthcare costs, guiding resource allocation, policy creation, and financial planning for more sustainable and effective healthcare systems. © 2024 IEEE.

关键词： Budget control

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