A Spectrogram image-based Network Anomaly Detection System using Deep Convolutional Neural Network

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.

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Network Anomaly Detection System: The State of Art of Network Behaviour Analysis

2008 International Conference on Convergence and Hybrid Information Technology ◽

10.1109/ichit.2008.249 ◽

2008 ◽

Cited By ~ 19

Author(s):

Shu Yun Lim ◽

Andy Jones

Keyword(s):

Anomaly Detection ◽

Detection System ◽

The State ◽

Behaviour Analysis ◽

Network Anomaly Detection ◽

State Of Art ◽

Anomaly Detection System

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Network Anomaly Detection System with Optimized DS Evidence Theory

The Scientific World JOURNAL ◽

10.1155/2014/753659 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Yuan Liu ◽

Xiaofeng Wang ◽

Kaiyu Liu

Keyword(s):

Anomaly Detection ◽

Detection Rate ◽

Computer Network ◽

Detection System ◽

Evidence Theory ◽

Optimization Methods ◽

High Detection Rate ◽

Detection Model ◽

Network Anomaly Detection ◽

Anomaly Detection System

Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor’s regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.

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DCNN-IDS : Deep Convolutional Neural Network based Intrusion Detection System

10.36227/techrxiv.12151734.v1 ◽

2020 ◽

Author(s):

Sriram Srinivasan ◽

Shashank A ◽

vinayakumar R ◽

Soman KP

Keyword(s):

Neural Network ◽

Intrusion Detection ◽

Convolutional Neural Network ◽

Intrusion Detection System ◽

Input Data ◽

Detection System ◽

Deep Convolutional Neural Network ◽

Traffic Data ◽

Data Set ◽

Research Problems

In the present era, cyberspace is growing tremendously and the intrusion detection system (IDS) plays a key role in it to ensure information security. The IDS, which works in network and host level, should be capable of identifying various malicious attacks. The job of network-based IDS is to differentiate between normal and malicious traffic data and raise an alert in case of an attack. Apart from the traditional signature and anomaly-based approaches, many researchers have employed various deep learning (DL) techniques for detecting intrusion as DL models are capable of extracting salient features automatically from the input data. The application of deep convolutional neural network (DCNN), which is utilized quite often for solving research problems in image processing and vision fields, is not explored much for IDS. In this paper, a DCNN architecture for IDS which is trained on KDDCUP 99 data set is proposed. This work also shows that the DCNN-IDS model performs superior when compared with other existing works.

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