scholarly journals Design of Intrusion Detection System for Internet of Things Based on Improved BP Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 106043-106052 ◽  
Author(s):  
Aimin Yang ◽  
Yunxi Zhuansun ◽  
Chenshuai Liu ◽  
Jie Li ◽  
Chunying Zhang
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
Intrusion Detection System ◽  
Detection System

The Development of Intrusion Detection System Based on Improved BP Neural Network

2013 ◽  
Vol 718-720 ◽  
pp. 1973-1979 ◽  
Author(s):  
Jin Song Yuan ◽  
Yi Wang
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
Intrusion Detection System ◽  
High Efficiency ◽  
Learning Algorithm ◽  
Detection System ◽  
Back Propagation ◽  
Learning Ability ◽  
Feed Forward Neural Network

BP neural network is a multilayer feed-forward neural network, it achieved from input to output arbitrary nonlinear mapping, and weights are adjusted by using the back propagation learning algorithm. Intrusion detection systems using the learning ability of neural network to extract the network data profile, and it also can use the neural network has the ability of self-learning and parallel processing ability, through the construction of intelligent neural network classifier to identify abnormal, so as to achieve the purpose of detecting intrusion behavior. The paper proposes the development of intrusion detection system based on improved BP neural network. Experimental results show that the proposed algorithm has high efficiency.

scholarly journals Intrusion Detection System for Internet of Things Based on Temporal Convolution Neural Network and Efficient Feature Engineering

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Abdelouahid Derhab ◽  
Arwa Aldweesh ◽  
Ahmed Z. Emam ◽  
Farrukh Aslam Khan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Convolution Neural Network ◽  
Feature Engineering ◽  
Training Time

In the era of the Internet of Things (IoT), connected objects produce an enormous amount of data traffic that feed big data analytics, which could be used in discovering unseen patterns and identifying anomalous traffic. In this paper, we identify five key design principles that should be considered when developing a deep learning-based intrusion detection system (IDS) for the IoT. Based on these principles, we design and implement Temporal Convolution Neural Network (TCNN), a deep learning framework for intrusion detection systems in IoT, which combines Convolution Neural Network (CNN) with causal convolution. TCNN is combined with Synthetic Minority Oversampling Technique-Nominal Continuous (SMOTE-NC) to handle unbalanced dataset. It is also combined with efficient feature engineering techniques, which consist of feature space reduction and feature transformation. TCNN is evaluated on Bot-IoT dataset and compared with two common machine learning algorithms, i.e., Logistic Regression (LR) and Random Forest (RF), and two deep learning techniques, i.e., LSTM and CNN. Experimental results show that TCNN achieves a good trade-off between effectiveness and efficiency. It outperforms the state-of-the-art deep learning IDSs that are tested on Bot-IoT dataset and records an accuracy of 99.9986% for multiclass traffic detection, and shows a very close performance to CNN with respect to the training time.

scholarly journals Application of Temperature Prediction Based on Neural Network in Intrusion Detection of IoT

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xuefei Liu ◽  
Chao Zhang ◽  
Pingzeng Liu ◽  
Maoling Yan ◽  
Baojia Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Defense Mechanism ◽  
Detection System ◽  
The Internet ◽  
Adaptive Capabilities ◽  
The Neural Network ◽  
The Internet Of Things

The security of network information in the Internet of Things faces enormous challenges. The traditional security defense mechanism is passive and certain loopholes. Intrusion detection can carry out network security monitoring and take corresponding measures actively. The neural network-based intrusion detection technology has specific adaptive capabilities, which can adapt to complex network environments and provide high intrusion detection rate. For the sake of solving the problem that the farmland Internet of Things is very vulnerable to invasion, we use a neural network to construct the farmland Internet of Things intrusion detection system to detect anomalous intrusion. In this study, the temperature of the IoT acquisition system is taken as the research object. It has divided which into different time granularities for feature analysis. We provide the detection standard for the data training detection module by comparing the traditional ARIMA and neural network methods. Its results show that the information on the temperature series is abundant. In addition, the neural network can predict the temperature sequence of varying time granularities better and ensure a small prediction error. It provides the testing standard for the construction of an intrusion detection system of the Internet of Things.

scholarly journals ANNIDS: Artificial Neural Network based Intrusion Detection System for Internet of Things

2019 ◽  
Vol 8 (11) ◽  
pp. 2583-2588 ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Detection Systems ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Internet of Things (IoT) makes everything in the real world to get connected. The resource constrained characteristics and the different types of technology and protocols tend to the IoT be more vulnerable than the conventional networks. Intrusion Detection System (IDS) is a tool which monitors analyzes and detects the abnormalities in the network activities. Machine Learning techniques are implemented with the Intrusion detection systems to enhance the performance of IDS. Various studies on IoT reveals that Artificial Neural Network (ANN) provides better accuracy and detection rate than other approaches. In this paper, an Artificial Neural Network based IDS (ANNIDS) technique based on Multilayer Perceptron (MLP) is proposed to detect the attacks initiated by the Destination Oriented Direct Acyclic Graph Information Solicitation (DIS) attack and Version attack in IoT environment. Contiki O.S/Cooja Simulator 3.0 is used for the IoT simulation.

Development of Intrusion Detection System for Security Threats in Internet of Things Using Artificial Neural Network

2019 ◽  
Vol 16 (8) ◽  
pp. 3242-3245
Author(s):  
R. Ramadevi ◽  
N. R. Krishnamoorthy ◽  
D. Marshiana ◽  
Sujatha Kumaran ◽  
N. Aarthi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Security Threats ◽  
Data Set ◽  
Artificial Neural

Internet of things (IoT) is a revolutionary technology which changes our life and work. Many industry sectors such as manufacturing, transportation, utilities, health care, consumer electronics and automobiles are invested and adopted towards IoT technology. The major inconvenience with IoT is its safety, as it is prone to attack by hackers. Detection Systems are used to detect these intrusions to protect the information and communication systems. Hence it is essential to design an intrusion detection system for security threats of IoT networks. This paper focuses, on the development of Artificial Neural Network (ANN) based Intrusion Detection System for threat analysis in IoT network. KDD-99 data set with Denial of Service (DoS) type attack is used to train and test three different ANN models. In this research, a Feed Forward Back Propagation (FFBP) network is used to detect the DoS attack. The process of optimization of a FFBP network involves comparison of classification accuracy during both training and testing in terms of true positive and false positive rates. For the data set considered the optimised network has achieved 100% efficiency during both training and testing.

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