scholarly journals An Unsupervised Learning-Based Network Threat Situation Assessment Model for Internet of Things

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hongyu Yang ◽  
Renyun Zeng ◽  
Fengyan Wang ◽  
Guangquan Xu ◽  
Jiyong Zhang
Keyword(s):  
Internet Of Things ◽  
Unsupervised Learning ◽  
Network Traffic ◽  
Test Group ◽  
Reconstruction Error ◽  
Assessment Model ◽  
Generative Adversarial Networks ◽  
Situation Assessment ◽  
Variational Autoencoder ◽  
Threat Situation

With the wide application of network technology, the Internet of Things (IoT) systems are facing the increasingly serious situation of network threats; the network threat situation assessment becomes an important approach to solve these problems. Aiming at the traditional methods based on data category tag that has high modeling cost and low efficiency in the network threat situation assessment, this paper proposes a network threat situation assessment model based on unsupervised learning for IoT. Firstly, we combine the encoder of variational autoencoder (VAE) and the discriminator of generative adversarial networks (GAN) to form the V-G network. Then, we obtain the reconstruction error of each layer network by training the network collection layer of the V-G network with normal network traffic. Besides, we conduct the reconstruction error learning by the 3-layer variational autoencoder of the output layer and calculate the abnormal threshold of the training. Moreover, we carry out the group threat testing with the test dataset containing abnormal network traffic and calculate the threat probability of each test group. Finally, we obtain the threat situation value (TSV) according to the threat probability and the threat impact. The simulation results show that, compared with the other methods, this proposed method can evaluate the overall situation of network security threat more intuitively and has a stronger characterization ability for network threats.

Application of Bayesian for the Situation Assessment of Sea-Battlefield

2013 ◽  
Vol 346 ◽  
pp. 135-139 ◽  
Author(s):  
Yong Tao Yu ◽  
Ying Ding ◽  
Zheng Xi Ding
Keyword(s):  
Decision Support ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Assessment Model ◽  
Situation Assessment ◽  
Network Characteristics ◽  
Specific Assessment

The sea-battlefield situation is dynamic and how efficient sea-battlefield situation assessment is a major problem facing operational decision support. According to research based on Bayesian networks Sea-battlefield situation assessment, first constructed sea-battlefield situation assessment Bayesian network; followed by specific assessment objectives, to simplify creating sub Bayesian assessment model; once again based on Bayesian network characteristics to determine each node probability formula; finally, according to the formula for solving the edge of the probability and the conditional probability of each node, sea-battlefield situation assessment.

scholarly journals Functioning of the Internet of Things Networks with Moving Nodes

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 31-36
Author(s):  
A. Marochkina ◽  
А. Paramonov
Keyword(s):  
Internet Of Things ◽  
Network Traffic ◽  
The Internet ◽  
Traffic Stream ◽  
The Internet Of Things ◽  
Area Of Application ◽  
Self Organizing

The area of application for the Internet of Things networks is vast. One of the main uses for such a net-work is the organization of network traffic. A traffic stream can be considered as a self-organizing net-work with moving nodes. This article describes the various features of such networks. Models with vari-ous mobility, velocity and density parameters of nodes are considered for studying the routes in this networks.

scholarly journals Technoeconomic Assessment Based on Active Context-Knowledge Orchestration for Power Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yuanshuo Zheng ◽  
Shujuan Sun ◽  
Chenyang Li ◽  
Jingtang Luo ◽  
Jiuling Dong ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Power Distribution ◽  
Economic Benefits ◽  
Assessment Model ◽  
Learning Technology ◽  
Assessment Framework ◽  
Technical Performance ◽  
System Requirements ◽  
Context Knowledge ◽  
Technology And Economy

Power Internet of Things (abbreviated as PIoT) is the information infrastructure to provide ubiquitous perception ability for smart grid (abbreviated as SG). To better deploy and utilize PIoT, its perception ability must be comprehensively assessed in terms of technical performance and economic benefits. However, at present, there is no assessment framework for PIoT due to the high diversity and heterogeneousness of SG scenarios. Additionally, there is information overlap between metrics in the assessment framework. The assessment model which could remove redundant information between metrics and simplify the assessment framework is an urgent demand to improve the effectiveness and timeliness of assessment. Consequently, first, aiming at the power system requirements of complex and diverse, a general assessment framework is put forward to assess the ability of PIoT in terms of technology and economy. Next, the requirement characteristics of power distribution scenario (abbreviated as PDS) are precisely analyzed with active context-knowledge orchestration technology. The general assessment framework is instantiated to build an instantiation assessment scheme in PDS. Moreover, an assessment model is established based on the instantiation assessment scheme to assess the efficiency of PIoT in Beijing. Finally, the assessment model is further refined with the machine learning technology to improve the efficiency of assessment. This refinement model achieves the extraction of 4-dimensional metrics from 23-dimensional metrics for assessment and finally improves assessment efficiency by 82.6%.

Cluster-Based Countermeasures for DDoS Attacks

2016 ◽  
pp. 206-227
Author(s):  
Mohammad Jabed Morshed Chowdhury ◽  
Dileep Kumar G
Keyword(s):  
Unsupervised Learning ◽  
Network Traffic ◽  
Denial Of Service ◽  
Security Threats ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Real Progress ◽  
Bandwidth Capacity

Distributed Denial of Service (DDoS) attack is considered one of the major security threats in the current Internet. Although many solutions have been suggested for the DDoS defense, real progress in fighting those attacks is still missing. In this chapter, the authors analyze and experiment with cluster-based filtering for DDoS defense. In cluster-based filtering, unsupervised learning is used to create profile of the network traffic. Then the profiled traffic is passed through the filters of different capacity to the servers. After applying this mechanism, the legitimate traffic will get better bandwidth capacity than the malicious traffic. Thus the effect of bad or malicious traffic will be lesser in the network. Before describing the proposed solutions, a detail survey of the different DDoS countermeasures have been presented in the chapter.

Classification Based on Unsupervised Learning

2011 ◽  
pp. 348-395
Author(s):  
Yu Wang
Keyword(s):  
Network Security ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Network Traffic ◽  
High Speed ◽  
Ad Hoc ◽  
Training Data ◽  
Traffic Data ◽  
Response Variable ◽  
Learning Techniques

The requirement for having a labeled response variable in training data from the supervised learning technique may not be satisfied in some situations: particularly, in dynamic, short-term, and ad-hoc wireless network access environments. Being able to conduct classification without a labeled response variable is an essential challenge to modern network security and intrusion detection. In this chapter we will discuss some unsupervised learning techniques including probability, similarity, and multidimensional models that can be applied in network security. These methods also provide a different angle to analyze network traffic data. For comprehensive knowledge on unsupervised learning techniques please refer to the machine learning references listed in the previous chapter; for their applications in network security see Carmines, Edward & McIver (1981), Lane & Brodley (1997), Herrero, Corchado, Gastaldo, Leoncini, Picasso & Zunino (2007), and Dhanalakshmi & Babu (2008). Unlike in supervised learning, where for each vector 1 2 ( , , , ) n X x x x = ? we have a corresponding observed response, Y, in unsupervised learning we only have X, and Y is not available either because we could not observe it or its frequency is too low to be fit ted with a supervised learning approach. Unsupervised learning has great meanings in practice because in many circumstances, available network traffic data may not include any anomalous events or known anomalous events (e.g., traffics collected from a newly constructed network system). While high-speed mobile wireless and ad-hoc network systems have become popular, the importance and need to develop new unsupervised learning methods that allow the modeling of network traffic data to use anomaly-free training data have significantly increased.

Sign in / Sign up

Export Citation Format

Share Document