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Research on Intrusion Detection Technology Based on Ensemble Learning and Decision Tree Algorithm

Research on Intrusion Detection Technology Based on Ensemble...

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Signal Processing and Intelligent computing (SPIC), international conference on

作者： Lei Shang Aihua Li School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

With the development and progress of network technology, network played an increasingly important role in modern life, but with the wide application of network, network security incidents also come, and the overall number of each year has maintained a continuous rising trend. In the face of increasing network intrusion threats, how to effectively identify malicious traffic from network traffic became more and more important, and the research in the field of intrusion detection has developed from the traditional single machine learning algorithm to the combination of multiple algorithms. In this paper, Tree-ensemble, which is a combination of Ensemble learning and decision Tree algorithm, was proposed to carry out the research on intrusion detection technology. KDD99 was selected as the intrusion detection data set, the eigenvalue selection algorithm based on random forest is used to sort the importance of features, and 44 eigenvalues are selected as the classification features of normal traffic and malicious traffic. Decision tree, Random forest, Extreme tree, AdaBoosting Adaptive Boosting, Gradient Boosting Decision Tree, XGBoost, Ensemble Voting (soft-voting),Ensemble Stacking (Logistic Regression as meta-classifier) were used to build a classification model for security analysis of data KDD99. Among the constructed classification models, the XGBoost algorithm model performed best, and the models constructed by the collective voting (soft voting) and set superposition (logistic regression as a meta-classifier) methods performed well.

关键词： Adaptation models Logistic regression Machine learning algorithms Accuracy Stacking Intrusion detection Signal processing algorithms Classification algorithms Decision trees Random forests

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FAIRMod: Detecting & Restricting Harmful Content using Machine Learning

FAIRMod: Detecting & Restricting Harmful Content using Machi...

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Networking and Communications (ICNWC), international conference on

作者： Aryan Singh Jadon Vanusha D Delsi Robinsha S Dept. of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

The Content Restriction System (CRS) harnesses the power of machine learning to mitigate the proliferation of harmful content in digital spaces. The current systems are mostly made by private social media giants and there is a lack of a wholesome all-round CRS for all. This can be useful to remove bad comments in the comment section, removing it from the reach of children as well. It detects profanity, harmful and violent behavior. We are creating this using Long Short Term Memory (LSTM) and the algorithm is modified such that it detects the comment as quickly as possible. This is an improved version of this algorithm that can give us faster results. For this we have implemented several filters before sending a text object for analysis minimizing the need for analyzing all the data objects. After these filters are applied, we send the data objects for analysis and even this stage takes very less time. This algorithm is meant to work for large datasets with over a million data points. In the future, we can use it as a scanner tool to check the client’s database for such harmful content and mark it up for them. FAIRMod will increase the overall health of the internet and its accessibility. Our goal is to achieve 90% accuracy while having faster detection speed.

关键词： Sentiment analysis Machine learning algorithms Filters Recurrent neural networks soft sensors Semantics Virtual environments

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A CEEMD-NARX Model of Sino-US Bilateral Trade Forecast and the Empirical Analysis Based on Public data 2021

A CEEMD-NARX Model of Sino-US Bilateral Trade Forecast and t...

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2nd international conference on Artificial Intelligence and Information Systems (ICAIIS )

作者： Zhu, Yanming Zhang, Zhenhua Guangdong Univ Foreign Studies Sch Math & Stat Lab Language Engn & Comp Guangzhou 510006 Peoples R China

ISBN: (纸本)9781450390200

Based on the gravity model, this paper presents a combinational algorithm with high-precision for the forecast of Sino-US bilateral trade. Taking into account the non-stationarity of trade data, we propose a combined model based on complementary ensemble empirical mode decomposition (CEEMD) and NARX neural network (NARX) to forecast Sino-US trade volume. The CEEMD-NARX model can effectively solve the problem of non-stationary of the original data, and has higher prediction accuracy than the traditional statistical model. The simulation results show that this model is superior to NARX model, SVM model, BPNN model, Multiple Regression, etc. Hence, it is one of the most effective methods in the field of economic forecast with non-stationary data.

关键词： CEEMD NARX Combined forecast Sino-US bilateral trade

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Quantifying the Impact of Climatic Factors on Dengue Incidence Using Generalized Linear Mixed Model with Spatio-Temporal Bayesian Poisson Random Effects Approach 6th

Quantifying the Impact of Climatic Factors on Dengue Inciden...

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6th international conference on soft computing in data science (scds)

作者： Sapri, Nik Nur Fatin Fatihah Yaacob, Wan Fairos Wan Yap, Bee Wah Univ Teknol MARA Fac Comp & Math Sci Shah Alam 40450 Selangor Malaysia Univ Teknol MARA Cawangan Kelantan Fac Comp & Math Sci Kota Baharu 15050 Kelantan Malaysia Univ Teknol MARA Inst Big Data Analyt & Artificial Intelligence IB Kompleks Al Khawarizmi Shah Alam 40450 Selangor Malaysia

ISBN: (纸本)9789811673344;9789811673337

Dengue fever is a global life-threatening vector-borne disease which is mainly distributed by the vector Aedes Aegypti mosquito. It is known that the development and survivorship of this vector depends on surrounding climate. The dengue outbreak in Kelantan, Malaysia is alarming. The aim of the study was to compare the fixed effects Negative Binomial GLM and Bayesian Poisson GLMM in prediction of dengue incidence in Kelantan. The data involved daily number of reported dengue cases (1st January 2013-31st December 2017) in ten districts of Kelantan which was collected from Ministry of Health Malaysia. The climate variables, average daily temperature, relative humidity and rainfall (climatic data) were obtained from NASA's Global Climate Change website, while the population data were from Department of Statistics Malaysia. Statistical modeling results revealed that the fixed effects Negative Binomial GLM failed to fit the daily dengue incidence when serious epidemic occurred. The spatio-temporal Bayesian Poisson GLMM model improved the prediction of dengue incidence. Relative humidity at lag 7 days and 21 days and average temperature at lag 21 days were found to be significant contributing factors of dengue incidence in Kelantan. The findings of the study are significant to respective local authorities in providing vital information for early dengue warning systems in a particular area. This is important for authorities to monitor and reduce dengue incidence in endemic areas and to safeguard the community from dengue outbreak.

关键词： Dengue Generalized Linear Model Generalized Linear Mixed Model Spatio-Temporal Bayesian Poisson Model Climate

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A Comparative Study for Predicting Burned Areas of a Forest Fire Using soft computing Techniques 2nd

A Comparative Study for Predicting Burned Areas of a Forest ...

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2nd international conference on data science, Machine Learning and Applications, ICDSMLA 2020

作者： Al-Shourbaji, Ibrahim Alhameed, Mohammed Katrawi, Anwer Jeribi, Fathe Alim, Sophia Department of Computer and Network Engineering Computer Science and Information Technology College Jazan University Jazan82822-6649 Saudi Arabia Department of Computer Science Computer Science and Information Technology College Jazan University Jazan82822-6649 Saudi Arabia Faculty Member Academic Development Deanship Jazan University Jazan82822-6649 Saudi Arabia Department of Information Technology and Security Computer Science and Information Technology College Jazan University Jazan82822-6649 Saudi Arabia Independent Researcher Bradford United Kingdom

ISBN: (纸本)9789811636899

Forest fires is an important environmental disasters that have many consequences in our life. As a result, early forecasting and rapid action of fires are needed to control such a phenomenon and saving lives. In this paper, Montesinho Natural Park (MNP) dataset is used to determine the burned areas of a forest fire, three prediction techniques were analyzed;namely Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN). These algorithms are implemented to specify the technique that would provide best prediction results. The performance of these algorithms was assessed based on three statistical measures, the Mean Absolute Error (MAE), the Mean Squared Error (MSE) and the Root Mean Squared Error (RMSE). From the Statistical measures, it is inferred that MARS algorithm provides the best results in term of performance compared with other methods. The obtained results confirm that MARS improves the forecasting accuracy and has the capability to forecast forest fires effectively with a comparable computational cost. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Enhancing Cloud Security with Deep Learning-Based Intrusion Detection in Cloud computing Environments

Enhancing Cloud Security with Deep Learning-Based Intrusion ...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： T. Aswini Devi Arpit Jain Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India GRIET Hyderabad

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

Cloud computing (CC) offers a wide range of on-demand resources and services over the internet. However, due to the inherent vulnerability of the cloud’s dispersed architecture, guaranteeing the privacy and security of cloud assets and resources remains a tough task. data and service protection in cloud systems is a continuous and serious concern. This article presents a fresh solution to this problem by utilizing Deep Learning (DL) techniques for intrusion detection in cloud computing systems. The present research analyses the strategies used by several Intrusion Detection Systems (IDS). As network infrastructures expand, so do security risks, increasing the need for dependable intrusion detection. Developers have created numerous intrusion detection systems (IDS) in response to the expanding security and privacy issues that saturate modern computer networks. Improving the datasets used for training and testing these security solutions requires equal focus as building defense systems. Improved datasets significantly enhance the detection capabilities of both offline and online intrusion detection models. This essay contributes to the growing corpus of research on CC security by conducting a thorough examination of publicly available network-based IDS datasets. It emphasizes the need to incorporate cutting-edge DL approaches into IDS for increased security. As cloud computing continues to transform the digital world, the findings of this study have major implications for safeguarding sensitive data and key services in cloud-based ecosystems. Furthermore, they provide solutions to the current anomaly detection issues caused by insufficient normal patterns in training data. The detection of accuracy information for two, five, and twenty-three classes and soft-max regression (SMR) feature learning perform similarly for 5-class and 23-class categorization, and it was also found that STL completed all categorization categories with above 98% accuracy.

关键词： Training Privacy Reviews Computational modeling Cloud computing security Biological system modeling Intrusion detection

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Chaotic Particle Swarm Optimization with Spiral-shaped Update Mechanism 23

Chaotic Particle Swarm Optimization with Spiral-shaped Updat...

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2nd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2023

作者： Tian, Dongping Li, Bingchun Liu, Chen Wang, Yani School of Computer Science and Technology Kashi University Xinjiang Kashi City China

ISBN: (纸本)9798400707629

Particle swarm optimization (PSO), as one of the metaheuristic optimization methods, has the merits of fast convergence, few adjustable parameters and simple searching process, but it also has the disadvantages of easily falling into local optimal and low convergence precision. Hence, a chaotic PSO with spiral-shaped update strategy (CPSOS) is presented in this work. First, the chaotic mapping method is used to generate the initial swarm to enhance the quality of initial particles. Second, a method based on time-varying acceleration coefficients is employed to trade off the exploration and exploitation through the analysis of velocity and position changes for particle. In addition, an adaptive position updating strategy based on spiral-shaped mechanism is adopted in different evolutionary states to get better global convergence. Finally, simulation experiment of the CPSOS algorithm is carried out based on 12 classical benchmark functions, and the convergence analysis under 2-dimensional environment is also detailed. It can be concluded from the convergence figures that the CPSOS has much better robustness and stability by improving the shortcomings of standard particle swarm optimization. CCS CONCEPTS •computing methodologies •Artificial intelligence •Search methodologies © 2023 ACM.

关键词： Economic and social effects

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2nd international conference on soft computing and its Engineering Applications, icsoftComp 2020

2nd International Conference on Soft Computing and its Engin...

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2nd international conference on soft computing and its Engineering Applications, icsoftComp 2020

ISBN: (纸本)9789811607073

The proceedings contain 28 papers. The special focus in this conference is on soft computing and its Engineering Applications. The topics include: Rethinking the Limits of Optimization Economic Order Quantity (EOQ) Using Self Generating Training Model by Adaptive-Neuro Fuzzy Inference System;Hybrid POS Tagger for Gujarati Text;Optimization of ICT Street Infrastructure in Smart Cities;transfer Learning-Based Image Tagging Using Word Embedding Technique for Image Retrieval Applications;comparison of Deep Learning Models for Cancer Metastases Detection: An Experimental Study;a Lightweight Hybrid Majority Vote Classifier Using Top-k dataset;twitter Emotion Analysis for Brand Comparison Using Naive Bayes Classifier;a Smart Card Based Lightweight Multi Server Encryption Scheme;Firmware Attack Detection on Gadgets Using Ridge Regression (FAD-RR);Automatic Image Colorization Using GANs;automatic Text Extraction from Digital Brochures: Achieving Competitiveness for Mauritius Supermarkets;improved Predictive System for Soil Test Fertility Performance Using Fuzzy Rule Approach;Artificial Intelligence Applications to Tackle COVID-19;throat Inflammation Based Mass Screening of Covid-19 on Embedded Platform;predicting Novel CoronaVirus 2019 with Machine Learning Algorithms;evaluation of Image Filtering Parameters for Plant Biometrics Improvement Using Machine Learning;Glaucoma Detection Using Features of Optic Nerve Head, CDR and ISNT from Fundus Image of Eye;crop Yield Estimation Using Machine Learning;embedded Linux Based Smart Secure IoT Intruder Alarm System Implemented on BeagleBone Black;gujarati Task Oriented Dialogue Slot Tagging Using Deep Neural Network Models;energy Efficient Aspects of Federated Learning – Mechanisms and Opportunities;classification of UrbanSound8k: A Study Using Convolutional Neural Network and Multiple data Augmentation Techniques.

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Stakeholder-centred Identification of data Quality Issues: Knowledge that Can Save Your Business 2

Stakeholder-centred Identification of Data Quality Issues: K...

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2nd international conference on Intelligent data science Technologies and Applications (IDSTA)

作者： Nikiforova, Anastasija Kozmina, Natalija Univ Latvia Fac Comp Programming Dept Innovat Informat Technol Lab Riga Latvia

ISBN: (纸本)9781665421805

The paper presents a study aimed at identifying the most widely occurring data quality issues that affect users' experience with data and their reuse, and presence of which may not only disrupt the willingness to work with data but also cause losses for businesses. The list of defects is intended to be identified as a result of the following activities: first, the list of the most widely occurring data quality requirements and/ or dimensions should be established by means of literature analysis. Second, given the diversity and quantity of different data quality requirements and dimensions, this list should be reduced by means of the brainstorming session and DELPHI analysis, which involves 12 experts. The validity of the resulting list should then be verified by applying these requirements to real-world data, more precisely, open government data, which are freely available to every stakeholder. This activity involves 30 users with advanced data quality knowledge. This allows us to define a list of key data quality issues. Both the data holder and the data user with higher degree of confidence can make use of it to make sure that the data are error-free and are a trusted source to be used without losses for business. These requirements serve as part of a specification for the web-based data quality analysis tool to be developed.

关键词： data quality stakeholder Delphi brainstorming open government data open data

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Matching with Externalities for Device Assignment in IoT Sharding Blockchain 2nd

Matching with Externalities for Device Assignment in IoT ...

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2nd EAI international conference on Edge computing and IoT: Systems, Management and Security, ICECI 2021

作者： Wu, Jingrou Zhang, Jin Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783031042300

Blockchain technology provides a powerful platform to record and manage Internet-of-Things (IoT) data. To support massive amounts of IoT data, sharding protocols are applied to enlarge the blockchain system scalability and increase efficiency. It divides the IoT devices into several committees (also called shards) so that data of different IoT devices can be processed at the same time in various shards. Random device assignment is the most popular way in IoT sharding blockchain system. Such assignment does not consider the features of different IoT devices and relationship among each IoT device. Hence, the random device assignment might lower the data processing speed. In this paper, we address this issue by modeling the IoT device assignment problem as a many-to-one matching model. Devices are assigned to only one shard while a shard can house many devices. Due to dynamic preference lists of devices, we consider the matching model with externalities. We propose an algorithm to find a stable matching and prove its convergence and stability. The simulation results indicate that the algorithm converges efficiently. Besides, the proposed algorithm has better performance than random assignment. © 2022, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

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