scholarly journals Intrusion Detection Method of Internet of Things Based on Multi GBDT Feature Dimensionality Reduction and Hierarchical Traffic Detection

2021 ◽  
Vol 3 (4) ◽  
pp. 161-171
Author(s):  
Taifeng Pan
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Dimensionality Reduction ◽  
Detection Method ◽  
Feature Dimensionality Reduction ◽  
Traffic Detection

scholarly journals An Automata Based Intrusion Detection Method for Internet of Things

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Yulong Fu ◽  
Zheng Yan ◽  
Jin Cao ◽  
Ousmane Koné ◽  
Xuefei Cao
Keyword(s):  
Open Access ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Method ◽  
Detection System ◽  
Network Communication ◽  
5G Networks ◽  
Machine To Machine ◽  
Security Of Iot

Internet of Things (IoT) transforms network communication to Machine-to-Machine (M2M) basis and provides open access and new services to citizens and companies. It extends the border of Internet and will be developed as one part of the future 5G networks. However, as the resources of IoT’s front devices are constrained, many security mechanisms are hard to be implemented to protect the IoT networks. Intrusion detection system (IDS) is an efficient technique that can be used to detect the attackers when cryptography is broken, and it can be used to enforce the security of IoT networks. In this article, we analyzed the intrusion detection requirements of IoT networks and then proposed a uniform intrusion detection method for the vast heterogeneous IoT networks based on an automata model. The proposed method can detect and report the possible IoT attacks with three types: jam-attack, false-attack, and reply-attack automatically. We also design an experiment to verify the proposed IDS method and examine the attack of RADIUS application.

scholarly journals Efficient BiSRU Combined With Feature Dimensionality Reduction for Abnormal Traffic Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 164414-164427
Author(s):  
Pengpeng Ding ◽  
Jinguo Li ◽  
Mi Wen ◽  
Liangliang Wang ◽  
Hongjiao Li
Keyword(s):  
Dimensionality Reduction ◽  
Feature Dimensionality Reduction ◽  
Traffic Detection

scholarly journals An Efficient Communication Intrusion Detection Scheme in AMI Combining Feature Dimensionality Reduction and Improved LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Guanyu Lu ◽  
Xiuxia Tian
Keyword(s):  
Intrusion Detection ◽  
Dimensionality Reduction ◽  
Short Term Memory ◽  
Classification Model ◽  
High Dimensional ◽  
Short Term ◽  
Detection Scheme ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Feature Dimensionality Reduction

Communication intrusion detection in Advanced Metering Infrastructure (AMI) is an eminent security technology to ensure the stable operation of the Smart Grid. However, methods based on traditional machine learning are not appropriate for learning high-dimensional features and dealing with the data imbalance of communication traffic in AMI. To solve the above problems, we propose an intrusion detection scheme by combining feature dimensionality reduction and improved Long Short-Term Memory (LSTM). The Stacked Autoencoder (SAE) has shown excellent performance in feature dimensionality reduction. We compress high-dimensional feature input into low-dimensional feature output through SAE, narrowing the complexity of the model. Methods based on LSTM have a superior ability to detect abnormal traffic but cannot extract bidirectional structural features. We designed a Bi-directional Long Short-Term Memory (BiLSTM) model that added an Attention Mechanism. It can determine the criticality of the dimensionality and improve the accuracy of the classification model. Finally, we conduct experiments on the UNSW-NB15 dataset and the NSL-KDD dataset. The proposed scheme has obvious advantages in performance metrics such as accuracy and False Alarm Rate (FAR). The experimental results demonstrate that it can effectively identify the intrusion attack of communication in AMI.

Research of IOT Intrusion Detection System Based on Hidden Markov Model

2012 ◽  
Vol 263-266 ◽  
pp. 2949-2952
Author(s):  
Xiu Mei Wei ◽  
Xue Song Jiang ◽  
Xin Gang Wang
Keyword(s):  
Time Delay ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Intrusion Detection System ◽  
Detection Method ◽  
Hidden Markov ◽  
Detection System ◽  
Markov State

Along with the development of Internet of Things (IOT), there are a lot of increasingly serious security problems. The traditional intrusion detection method cannot adapt to the requirement of IOT. In this paper we advance a new intrusion detection method which can adapt to IOT. It is based on Hidden Markov Model (HMM), which is named as Hidden Markov state time delay sequence embedding (HMMSTdse) method.

Research on Data Security Detection Algorithm in IoT Based on K-means

2021 ◽  
Vol 14 ◽  
Author(s):  
Jianxing Zhu ◽  
Lina Huo ◽  
Mohd Dilshad Ansari ◽  
Mohammad Asif Ikbal
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Data Security ◽  
Detection Method ◽  
Detection Algorithm ◽  
The Internet ◽  
Human Beings ◽  
Network Attacks ◽  
Hybrid Detection ◽  
The Internet Of Things

Background: The development of the Internet of Things has prominently expanded the perception of human beings, but ensuing security issues have attracted people's attention. From the perspective of the relatively weak sensor network in the Internet of Things. Method: Proposed method Aiming at the characteristics of diversification and heterogeneity of collected data in sensor networks, the data set is clustered and analyzed from the aspects of network delay and data flow to extract data characteristics. Then, according to the characteristics of different types of network attacks, a hybrid detection method for network attacks is established. An efficient data intrusion detection algorithm based on K-means clustering is proposed Results: This paper proposes a network node control method based on traffic constraints to improve the security level of the network. Simulation experiments show that compared with traditional password-based intrusion detection methods; the proposed method has a higher detection level and is suitable for data security protection in the Internet of Things. Conclusions: This paper proposes an efficient intrusion detection method for applications with Internet of Things

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