scholarly journals Information - Theoretic Methods for Anomaly Detection

2019 ◽  
pp. 21-29
Author(s):  
Mariam Haroutunian ◽  
Tigran Badasyan
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Cyber Security ◽  
Digital Systems ◽  
Huge Amount ◽  
Information Theoretic ◽  
Normal Behavior ◽  
Network Methods ◽  
Pros And Cons ◽  
Information Theoretic Methods

Maintaining the security of digital systems with a huge amount of data is one of the main concerns of IT specialists in these times. Anomaly detection in systems is one of the solutions to overcome this challenge. Anomaly detection means ¯nding patterns that are not normal or deviate from normal behavior in a system. Anomaly detection has various applications in bio-informatics, image processing, cyber security, security for databases, etc. There are many groups of methods that are used for anomaly detection including statistical methods, neural network methods and information theoretic methods. In this paper we survey pros and cons of anomaly detection based on information theoretic techniques

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

A neural network approach to anomaly detection in spectra

1997 ◽  
Author(s):  
Daniel Benzing ◽  
Kevin Whitaker ◽  
Dedra Moore ◽  
Daniel Benzing ◽  
Kevin Whitaker ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Network Approach ◽  
Neural Network Approach

Pencarian Question-Answer Menggunakan Convolutional Neural Network Pada Topik Agama Berbahasa Indonesia

2018 ◽  
Vol 10 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Rizqa Raaiqa Bintana ◽  
Chastine Fatichah ◽  
Diana Purwitasari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Question Answering ◽  
Mean Average Precision ◽  
Average Precision ◽  
Community Based ◽  
Network Methods ◽  
The Mean ◽  
Index Terms ◽  
Search Information

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval

ИСПОЛЬЗОВАНИЕ НЕЙРОСЕТЕВЫХ АЛГОРИТМОВ В ЗАДАЧАХ ВЫЧИСЛИТЕЛЬНОЙ ЛИТОГРАФИИ

2020 ◽  
Vol 96 (3s) ◽  
pp. 543-548
Author(s):  
Н.Н. Балан ◽  
А.А. Березин ◽  
Е.С. Горнев ◽  
В.В. Иванов ◽  
Е.В. Ипатова ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
The Family ◽  
Pros And Cons

Работа посвящена вопросам применения нейросетевых алгоритмов в литографических расчетах. Дан обзор основного круга задач вычислительной литографии, допускающих целесообразность применения нейросетей для их решения. Описаны преимущества и недостатки нейросетевых решений, рекомендуемых для использования в рассматриваемых задачах. This paper is dedicated to the task of applying neural network-based algorithms to lithographic calculations. It reviews the family of problems in computational lithography to which neural networks are applicable. Pros and cons of such solutions have been discussed.

scholarly journals Bayesian Models Applied to Cyber Security Anomaly Detection Problems

2021 ◽  
Author(s):  
José A. Perusquía ◽  
Jim E. Griffin ◽  
Cristiano Villa
Keyword(s):  
Anomaly Detection ◽  
Cyber Security ◽  
Bayesian Models

Graph Neural Network Approach for Anomaly Detection

Measurement ◽  
2021 ◽  
pp. 109546
Author(s):  
Lingqiang Xie ◽  
Dechang Pi ◽  
Xiangyan Zhang ◽  
Junfu Chen ◽  
Yi Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Network Approach ◽  
Neural Network Approach

scholarly journals Anomaly Detection Using Deep Neural Network for IoT Architecture

2021 ◽  
Vol 11 (15) ◽  
pp. 7050
Author(s):  
Zeeshan Ahmad ◽  
Adnan Shahid Khan ◽  
Kashif Nisar ◽  
Iram Haider ◽  
Rosilah Hassan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Detection System ◽  
Traffic Monitoring ◽  
Detection Accuracy ◽  
Learning Models ◽  
Prime Concern ◽  
Entry Points

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

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