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User Behavior Analytics for Anomaly Detection Using LSTM Autoencoder-Insider threat Detection 20

User Behavior Analytics for Anomaly Detection Using LSTM Aut...

引用

11th international conference on Advances in Information Technology, IAIT 2020

作者： Sharma, Balaram Pokharel, Prabhat Joshi, Basanta LogPoint Lalitpur Nepal Institute of Engineering Lalitpur Nepal

ISBN: (纸本)9781450377591

Identifying anomalies from log data for insider threat detection is practically a very challenging task for security analysts. User behavior modeling is very important for the identification of these anomalies. this paper presents unsupervised user behavior modeling for anomaly detection. the proposed approach uses LSTM based Autoencoder to model user behavior based on session activities and thus identify the anomalous data points. the proposed method follows a two-step process. First, it calculates the reconstruction error using the autoencoder on the non-anomalous dataset, and then it is used to define the threshold to separate the outliers from the normal data points. the identified outliers are then classified as anomalies. the CERT insider threat dataset has been used for the research work. For each user, the feature vectors are prepared by extracting key information from corresponding raw events and aggregating the data points based on users' actions within respective users' sessions. LSTM Autoencoder has been implemented for behavior learning and anomaly detection. For any unseen behavior or anomaly pattern, the model produces high reconstruction error which is an indication of an anomaly. the experimental results show that in the best case, the model produced an Accuracy of 90.17%, True Positives 91.03%, and False Positives 9.84%. thus, the results suggest that the proposed approach can be effectively used in automatic anomaly detection. © 2020 ACM.

关键词： Anomaly detection

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Kannada Character Recognition Using Multi-Class SVM Method

Kannada Character Recognition Using Multi-Class SVM Method

引用

international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Kusumika Krori Dutta Sunny Arokia Swamy Anushua Banerjee Divya Rashi B Chandan R Deepak Vaprani M.S. Ramaiah Institute of Technology Bangalore India Engineer/Scientist II Electric Power Research Institute North Carolina USA

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Character recognition also truncated to OCR (Optical Character Recognition), is the translation of images: hand-written, typewritten (either mechanically or electronically), or even just a simple text printed into a machine-editable text. Among them, one of the most relevant types is handwritten character recognition. Each handwritten content is composed of symbols, alphabets, etc. that are very syllabic with a distinct font style. Character recognition is achieved through segmentation, feature ex-traction, and classification, by using any one the machine learning methods thereby making tremendous advancements. this paper keenly focuses on the recognition of handwritten Kannada alpha-bets. Different machine learning classification strategies have known to be applied to achieve this recognition. Although, in this paper, we mostly center around the procedures dependency on the solution provided by Support Vector Machine(SVM) classifiers using Python. For simplicity, we focus on only four Kannada alpha-bets in this paper.

关键词： Support vector machines Support vector machine classification Machine learning Optical imaging Classification algorithms Character recognition Optical character recognition software

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Adversarial Edit Attacks for Tree data 20th

Adversarial Edit Attacks for Tree Data

引用

20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Paassen, Benjamin Bielefeld Univ CITEC Inspirat 1 D-33619 Bielefeld Germany

ISBN: (纸本)9783030336073;9783030336066

Many machine learning models can be attacked with adversarial examples, i.e. inputs close to correctly classified examples that are classified incorrectly. However, most research on adversarial attacks to date is limited to vectorial data, in particular image data. In this contribution, we extend the field by introducing adversarial edit attacks for tree-structured data with potential applications in medicine and automated program analysis. Our approach solely relies on the tree edit distance and a logarithmic number of black-box queries to the attacked classifier without any need for gradient information. We evaluate our approach on two programming and two biomedical data sets and show that many established tree classifiers, like tree-kernel-SVMs and recursive neural networks, can be attacked effectively.

关键词： Adversarial attacks Tree edit distance Structured data Tree kernels Recursive neural networks Tree echo state networks

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A Blockchain-based Solution for Reputation Management in IoV

A Blockchain-based Solution for Reputation Management in IoV

引用

international Wireless Communications and Mobile Computing conference, IWCMC

作者： Slim Abbes Slim Rekhis University of Carthage Higher School of Communication Engineering (SUP'COM) LR11TIC04 Communication Networks and Security Research Lab Tunisia

Internet of Vehicles (IoV) is an emerging paradigm in intelligent Transportation Systems (ITS). It represents a promised solution to handle the massive amount of exchanged information and the continuous demand for services and applications. Nevertheless, the IoV systems showed several vulnerabilities to numerous attacks due to high mobility and fast network topology changing. Hence, the security of IoV systems remains an inevitable concern for researchers and automotive manufacturers. In this paper, we provide a Blockchain-based solution for reputation management in cloud-based IoV system. It ensures the monitoring of vehicles in the network through the computation of routing-based metrics by watchdog nodes, and the continuous update of their reputations, while calling for the use of blockchain technology. the computed reputation will be used to issue certificates and guarantee trust in the vehicular system. the whole computations are achieved in distributed and automated manner using smart contracts. Moreover, homomorphic encryption is used over the metrics to ensure data confidentiality.

关键词： Measurement Wireless communication Cloud computing Network topology Smart contracts Machine learning Blockchains

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A Novel Approach for data Collection and Network Attack Warning 11

A Novel Approach for Data Collection and Network Attack Warn...

引用

11th international conference on Knowledge and Systems engineering (KSE)

作者： Van Kha Nguyen Minh Nhat Quang Truong Van Lam Le Quyet thang Le thanh Hai Nguyen Can Tho Dept Sci & Technol Can Tho Vietnam Can Tho Univ Technol Coll Informat Technol Can Tho Vietnam Can Tho Univ Dept Int Relat Can Tho Vietnam Can Tho Univ Coll Informat & Commun Technol Can Tho Vietnam

ISBN: (纸本)9781728130033

Network security in general, research on detecting and finding attacks in computer networks in particular, has become a very hot topic. there are a variety of studies on machine learning models to attempt to detect network attacks, but these studies only focused on the models for prediction while the details of collecting data and the steps of processing and extracting information from network packets are not presented. In this research, we have employed and installed an active framework for collecting data using Honeynet and leveraging artificial intelligence algorithms, such as machine learning and deep learning, to detect attacks in computer networks. We have proposed to use only header information of the network packets for network traffic classification. Our results from the experiments prove that the framework of collecting network packets and detecting attacks in computer networks can be implemented and employed efficiently in practical cases. In addition, DARPA29F extracted from the proposed method with 29 features is a promising dataset to validate the learning algorithms.

关键词： Network attacks Detecting network attacks Collecting and processing network packets Machine learning Deep learning

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Forecasting with Recurrent Neural Network in Intermittent Demand data

Forecasting with Recurrent Neural Network in Intermittent De...

引用

international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Amri Muhaimin Dedy Dwi Prastyo Henry Horng-Shing Lu Institut Teknologi Sepuluh Nopember Institute of Statistics National Chiao Tung University Surabaya Indonesia Institut Teknologi Sepuluh Nopember Surabaya Indonesia Institute of Statistics National Chiao Tung University Hsinchu ROC Taiwan

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Intermittent demand data is one of the data types with a very random pattern, for example demand data. the data will have value (not zero) if there is a demand. If there is no demand, the data is zero. Intermittent demand data is usually called customer demand data or sales data for an item that is not sold every time. the general problem is that demand is not always continuous but intermittent. this natural fact makes intermittent data not easy to predict. Standard methods used to predict intermittent demand data are Croston. Single exponential smoothing (SES) is also commonly used in practice. the Croston and exponential smoothing generally produce static forecasts. this study proposes a deep learning method i.e. recurrent neural network (RNN) and deep neural network (DNN) to predict intermittent data. the simulation study was carried out by generating data with 72 different design parameters and doing 50 repetitions. Also, the empirical study uses M5 competition data from the Kaggle website. this study aims to measure the performance of RNN and DNN, compared to Croston and SES as the benchmark methods, in predicting intermittent demand data. the performance measurement uses mean absolute error (MAE) and root mean squared scaled error (RMSSE). Deep learning performance in simulation studies successfully outperformed Croston and SES in several design parameters. In empirical studies, only the RNN method outperformed the benchmark methods. this study also found other information that the measurement of MAE is more robust than RMSSE.

关键词： Deep learning Recurrent neural networks Smoothing methods Benchmark testing Forecasting Tuning Standards

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Workload Prediction over Cloud Server using Time Series data

Workload Prediction over Cloud Server using Time Series Data

引用

international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Mahendra Pratap Yadav Nisha Pal Dharmendar Kumar Yadav MNNIT Allahabad Prayagraj India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Analyzing and interpreting the real time data is a challenging task for cloud analysts (e.g. cloud providers) in cur-rent scenario for allocating computing resources in applications. Availability of a massive amount of data for processing over a server, cloud providers use the time series analysis models to analyze it. Based on the analysis, cloud providers allocate cloud resources (e.g., container machines) to manage the workload. Predictive analysis of data is important to identify the future trends and it also enables cloud organizations to act as per the demand of workload. It can be applied in different areas such as stock prices prediction, weather forecasting, and traffic load over the server (cloud computing). Cloud providers use this predictive analysis to avoid different types of losses such as services unavailability, maximum energy consumption and customer's loss. One of the methods to do predictive analysis using time series data is long short-term memory (LSTM). this paper presents a predictive analysis of time series forecasting using deep learning method (LSTM) to predict the future load over servers. the prediction accuracy of LSTM has been measured using three metrics -RMSE, MSE and MAE.

关键词： Cloud computing Time series analysis Weather forecasting Telecommunication traffic Servers Predictive analytics Task analysis

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Research on training-mode of intelligent technical talents in the local application-oriented university 2021

Research on training-mode of intelligent technical talents i...

引用

2nd international conference on Computers, Information Processing and Advanced Education, CIPAE 2021

作者： Chen, Chunlei Wang, Chengduan Hou, Jinkui Zhang, Peng Wang, Lei Dai, Jiangyan School of Computer Engineering Weifang University Weifang China

ISBN: (纸本)9781450389969

Weifang is an emerging electronic-information-industry city in China. the fast development of local industries raise huge demands on application-oriented technical talents of various fields, among which-artificial intelligence (AI) takes the leading position. However, it is a challenging task to train AI talents due to the complexity of AI. We investigate on training AI talents in application-oriented universities like Weifang University. We propose to integrate theoretical knowledge and hands-on ability to support the growth of comprehensive ability. Subsequently, we factorize the knowledge and ability structure into small pieces to lower the learning barrier. Based on these two solutions, we try to cultivate the engineering thinking and ability of students. Our practice in technical competition and research projects show the effectiveness of the solutions. © 2021 ACM.

关键词： intelligent Technology Talent Cultivation Training Mode

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Classification of Brain MRI Images Using Convolution Neural Network and Transfer learning

Classification of Brain MRI Images Using Convolution Neural ...

引用

international conference on Computing and Networking Technology (ICCNT)

作者： N Ambily K Suresh Government Engineering College Barton Hill Thiruvananthapuram

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Magnetic Resonance Image (MRI) is a non intrusive procedure for obtaining high resolution images of living tissues. Varieties of tissue contrast images that MRI modalities produce provide important structural information, enabling diagnosis and segmentation of tumors. In this paper we come up with an integrated model of Convolutional Neural Network and Transfer learning for automated binary classification of brain MRI images. We use a sixteen layer pretrained network, to distinguish normal and abnormal images. Our experiments suggest good accuracy in classification on test data, and thus will turn out to be useful for efficient automated diagnosis.

关键词： Feature extraction Magnetic resonance imaging Tumors Training Biological neural networks Convolution Support vector machines

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Physiological-Based Smart Stress Detector using Machine learning Algorithms 11

Physiological-Based Smart Stress Detector using Machine Lear...

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11th IEEE international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM )

作者： Rosales, Marife A. Bandala, Argel A. Vicerra, Ryan Rhay Dadios, Elmer P. De La Salle Univ Elect & Commun Engn Dept Manila Philippines De La Salle Univ Mfg Engn Amd Management Dept Manila Philippines

ISBN: (纸本)9781728130446

this paper is focused on the development of an intelligent system to identify if one person is stress or not stress using physiological parameters through machine learning. In this study, the dataset was acquired from three hundred (300) male and female participants ages 18 to 25. the gathered dataset is composed of five (5) features (i.e. heart rate, systolic blood pressure, diastolic blood pressure, galvanic skin response and gender). An intelligent system was developed using machine learning algorithms for classification such as Linear Regression (LR), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) using Python IDE with sci-kit learn machine learning libraries. Google Colaboratory (Colab) was utilized to perform optimization using Gridsearch to identify the best parameters of each algorithm. Feature selection methods are implemented to identify the most significant features related to stress condition of one person. After optimization, the results showed that SVM has the best performance to classify if one person is stress or not stress with optimized training-testing accuracy score of 95.00% - 96.67%.

关键词： stress machine learning optimization feature selection google colaboratory

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