Anomaly Detection in Invasively Recorded Neuronal Signals Using Deep Neural Network: Effect of Sampling Frequency

2021 ◽  
pp. 79-91
Author(s):  
Marcos Fabietti ◽  
Mufti Mahmud ◽  
Ahmad Lotfi
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Sampling Frequency ◽  
Network Effect

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.

scholarly journals Anomaly Detection of CAN Bus Messages Using A Deep Neural Network for Autonomous Vehicles

2019 ◽  
Vol 9 (15) ◽  
pp. 3174 ◽  
Author(s):  
Zhou ◽  
Li ◽  
Shen
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Autonomous Vehicles ◽  
Deep Neural Network ◽  
Can Bus ◽  
Detection Accuracy ◽  
Area Network ◽  
Data Packets ◽  
Essential Components ◽  
Triplet Loss

The in-vehicle controller area network (CAN) bus is one of the essential components for autonomous vehicles, and its safety will be one of the greatest challenges in the field of intelligent vehicles in the future. In this paper, we propose a novel system that uses a deep neural network (DNN) to detect anomalous CAN bus messages. We treat anomaly detection as a cross-domain modelling problem, in which three CAN bus data packets as a group are directly imported into the DNN architecture for parallel training with shared weights. After that, three data packets are represented as three independent feature vectors, which corresponds to three different types of data sequences, namely anchor, positive and negative. The proposed DNN architecture is an embedded triplet loss network that optimizes the distance between the anchor example and the positive example, makes it smaller than the distance between the anchor example and the negative example, and realizes the similarity calculation of samples, which were originally used in face detection. Compared to traditional anomaly detection methods, the proposed method to learn the parameters with shared-weight could improve detection efficiency and detection accuracy. The whole detection system is composed of the front-end and the back-end, which correspond to deep network and triplet loss network, respectively, and are trainable in an end-to-end fashion. Experimental results demonstrate that the proposed technology can make real-time responses to anomalies and attacks to the CAN bus, and significantly improve the detection ratio. To the best of our knowledge, the proposed method is the first used for anomaly detection in the in-vehicle CAN bus.

Deep neural network for text anomaly detection in SIoT

2021 ◽  
Vol 178 ◽  
pp. 286-296
Author(s):  
Jie Mu ◽  
Xianchao Zhang ◽  
Yuangang Li ◽  
Jun Guo
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Deep Neural Network

Benchmarking Deep Neural Network Architectures for Machining Tool Anomaly Detection

2020 ◽  
Vol 4 (2) ◽  
pp. 20190039
Author(s):  
Tejas Puranik ◽  
Aroua Gharbi ◽  
Burak Bagdatli ◽  
Olivia Pinon Fischer ◽  
Dimitri N. Mavris
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Network Architectures ◽  
Neural Network Architectures

scholarly journals Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models

2021 ◽  
Vol 21 (3) ◽  
pp. 175-188
Author(s):  
Sumaiya Thaseen Ikram ◽  
Aswani Kumar Cherukuri ◽  
Babu Poorva ◽  
Pamidi Sai Ushasree ◽  
Yishuo Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Network Models ◽  
Neural Network Models ◽  
Detection Model ◽  
Detection Systems ◽  
Learning Techniques

Abstract Intrusion Detection Systems (IDSs) utilise deep learning techniques to identify intrusions with maximum accuracy and reduce false alarm rates. The feature extraction is also automated in these techniques. In this paper, an ensemble of different Deep Neural Network (DNN) models like MultiLayer Perceptron (MLP), BackPropagation Network (BPN) and Long Short Term Memory (LSTM) are stacked to build a robust anomaly detection model. The performance of the ensemble model is analysed on different datasets, namely UNSW-NB15 and a campus generated dataset named VIT_SPARC20. Other types of traffic, namely unencrypted normal traffic, normal encrypted traffic, encrypted and unencrypted malicious traffic, are captured in the VIT_SPARC20 dataset. Encrypted normal and malicious traffic of VIT_SPARC20 is categorised by the deep learning models without decrypting its contents, thus preserving the confidentiality and integrity of the data transmitted. XGBoost integrates the results of each deep learning model to achieve higher accuracy. From experimental analysis, it is inferred that UNSW_ NB results in a maximal accuracy of 99.5%. The performance of VIT_SPARC20 in terms of accuracy, precision and recall are 99.4%. 98% and 97%, respectively.

scholarly journals Deep Neural Network with Dropout for Anomaly Detection in Software Defined Networking

2019 ◽  
Vol 8 (11) ◽  
pp. 4084-4090
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Early Stage ◽  
Model Performance ◽  
Software Defined Networking ◽  
Computer Networking ◽  
Detection Model ◽  
Network Intrusion ◽  
Class Labels

A novel anomaly detection-based NIDS is main demand in the computer networking security for discriminating malicious software attack at the early stage. It monitors and analyzes network traffics, checking abnormal behaviors or attack signatures. The detection rate or accuracy is the prerequisite in the network intrusion detection models, also, developing adaptive and flexible model is a critical challenge regarding to unseen attack. This search paper included the deep neural network (DNN) as anomaly detection model can be used within software defined networking (SDN). Dropout technique is used to prevent DNN model from overfitting. Six features have information about the flow were chosen from NSL-KDD dataset to fit and evaluate this model, these data features could be matched to packet-in message header values, also, these features enable the model to be a good generative, and well perform on intrusion recognition issue with a subset of the data. Cross entropy loss function with SoftMax output layer were used for getting the differences between the two different distribution and mapping to multiple class classification covered five class labels, one is normal and the others are attacks (Dos, R2L, U2L and Probe). Accuracy is a comparative metric utilized for assessing the model performance. The results are promising, where accuracy achieved 92.65%.

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