scholarly journals A Markov-Based Intrusion Tolerance Finite Automaton

2021 ◽  
Vol 28 (2) ◽  
pp. 89-100
Keyword(s):  
Network Security ◽  
Markov Model ◽  
Finite Automaton ◽  
Network Services ◽  
Intrusion Tolerance ◽  
Security Strategy ◽  
Automatic Learning ◽  
Learning Mechanism ◽  
Proposed Model ◽  
Tolerance Model

It is inevitable for networks to be invaded during operation. The intrusion tolerance technology comes into being to enable invaded networks to provide the necessary network services. This paper introduces an automatic learning mechanism of the intrusion tolerance system to update network security strategy, and derives an intrusion tolerance finite automaton model from an existing intrusion tolerance model. The proposed model was quantified by the Markov theory to compute the stable probability of each state. The calculated stable probabilities provide the theoretical guidance and basis for administrators to better safeguard network security. Verification results show that it is feasible, effective, and convenient to integrate the Markov model to the intrusion tolerance finite automaton.

scholarly journals Entropy Based Features Distribution for Anti-DDoS Model in SDN

2021 ◽  
Vol 13 (3) ◽  
pp. 1522
Author(s):  
Raja Majid Ali Ujjan ◽  
Zeeshan Pervez ◽  
Keshav Dahal ◽  
Wajahat Ali Khan ◽  
Asad Masood Khattak ◽  
...  
Keyword(s):  
Network Security ◽  
False Positive ◽  
Denial Of Service ◽  
Network Services ◽  
Detection Accuracy ◽  
Data Sets ◽  
Traffic Patterns ◽  
Average Accuracy ◽  
Ddos Detection ◽  
Quantitative Results

In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users accessing a targeted network service or a resource. Although these attacks have been widely studied, there are few works which collect and analyse truly representative characteristics of DDoS traffic. The current research mostly focuses on DDoS detection and mitigation with predefined DDoS data-sets which are often hard to generalise for various network services and legitimate users’ traffic patterns. In order to deal with considerably large DDoS traffic flow in a Software Defined Networking (SDN), in this work we proposed a fast and an effective entropy-based DDoS detection. We deployed generalised entropy calculation by combining Shannon and Renyi entropy to identify distributed features of DDoS traffic—it also helped SDN controller to effectively deal with heavy malicious traffic. To lower down the network traffic overhead, we collected data-plane traffic with signature-based Snort detection. We then analysed the collected traffic for entropy-based features to improve the detection accuracy of deep learning models: Stacked Auto Encoder (SAE) and Convolutional Neural Network (CNN). This work also investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results. Quantitative results demonstrated SAE achieved relatively higher detection accuracy of 94% with only 6% of false-positive alerts, whereas the CNN classifier achieved an average accuracy of 93%.

scholarly journals A Proposed Model for Detecting Learning Styles Based on Agent Learning

2016 ◽  
Vol 11 (10) ◽  
pp. 65 ◽  
Author(s):  
M S Hasibuan ◽  
L E Nugroho ◽  
P I Santosa ◽  
S S Kusumawardani
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Evaluation Model ◽  
Learning System ◽  
Learning Activities ◽  
Instructional Materials ◽  
Data Driven ◽  
Automatic Learning ◽  
Proposed Model ◽  
Agent Learning

A learning style is an issue related to learners. In one way or the other, learning style could assist learners in their learning activities if students ignore their learning styles, it may influence their effort in understanding teaching materials. To overcome these problems, a model for reliable automatic learning style detection is needed. Currently, there are two approaches in detecting learning styles: data driven and literature based. Learners, especially those with changing learning styles, have difficulties in adopting these two approach since they are not adaptive, dynamic and responsive (ADR). To solve the above problems, a model using agent learning approach is proposes. Agent learning involves performing activities in four phases, i.e. initialization, learning, matching and, recommendations to decide the learning styles the students use. The proposed system will provide instructional materials that match the learning style that has been detected. The automatics detection process is performed by combining the data-driven and literature-based approaches. We propose an evaluation model agent learning system to ensure the model is working properly.

scholarly journals Detecting Cyber Threat Event from Twitter Using IDCNN and BiLSTM

2020 ◽  
Vol 10 (17) ◽  
pp. 5922 ◽  
Author(s):  
Yong Fang ◽  
Jian Gao ◽  
Zhonglin Liu ◽  
Cheng Huang
Keyword(s):  
Network Security ◽  
Language Processing ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Political Strategy ◽  
The Public ◽  
Cyber Threats ◽  
Proposed Model ◽  
Cyber Threat ◽  
Public Datasets

In the context of increasing cyber threats and attacks, monitoring and analyzing network security incidents in a timely and effective way is the key to ensuring network infrastructure security. As one of the world’s most popular social media sites, users post all kinds of messages on Twitter, from daily life to global news and political strategy. It can aggregate a large number of network security-related events promptly and provide a source of information flow about cyber threats. In this paper, for detecting cyber threat events on Twitter, we present a multi-task learning approach based on the natural language processing technology and machine learning algorithm of the Iterated Dilated Convolutional Neural Network (IDCNN) and Bidirectional Long Short-Term Memory (BiLSTM) to establish a highly accurate network model. Furthermore, we collect a network threat-related Twitter database from the public datasets to verify our model’s performance. The results show that the proposed model works well to detect cyber threat events from tweets and significantly outperform several baselines.

scholarly journals Assessment and Day-Ahead Forecasting of Hourly Solar Radiation in Medellín, Colombia

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4402
Author(s):  
Julián Urrego-Ortiz ◽  
J. Alejandro Martínez ◽  
Paola A. Arias ◽  
Álvaro Jaramillo-Duque
Keyword(s):  
Solar Radiation ◽  
Markov Model ◽  
Power Plants ◽  
Wrf Model ◽  
Tropical Andes ◽  
Clear Sky ◽  
Proposed Model ◽  
Solar Power Plants ◽  
Solar Resource ◽  
Sky Conditions

The description and forecasting of hourly solar resource is fundamental for the operation of solar energy systems in the electric grid. In this work, we provide insights regarding the hourly variation of the global horizontal irradiance in Medellín, Colombia, a large urban area within the tropical Andes. We propose a model based on Markov chains for forecasting the hourly solar irradiance for one day ahead. The Markov model was compared against estimates produced by different configurations of the weather research forecasting model (WRF). Our assessment showed that for the period considered, the average availability of the solar resource was of 5 PSH (peak sun hours), corresponding to an average daily radiation of ~5 kWh/m2. This shows that Medellín, Colombia, has a substantial availability of the solar resource that can be a complementary source of energy during the dry season periods. In the case of the Markov model, the estimates exhibited typical root mean squared errors between ~80 W/m2 and ~170 W/m2 (~50%–~110%) under overcast conditions, and ~57 W/m2 to ~171 W/m2 (~16%–~38%) for clear sky conditions. In general, the proposed model had a performance comparable with the WRF model, while presenting a computationally inexpensive alternative to forecast hourly solar radiation one day in advance. The Markov model is presented as an alternative to estimate time series that can be used in energy markets by agents and power-system operators to deal with the uncertainty of solar power plants.

Study of 3D Tolerance Modeling Method for Complex Feature Geometric Variations

2016 ◽  
Vol 851 ◽  
pp. 567-573
Author(s):  
He Ping Peng ◽  
Wen Long Lu
Keyword(s):  
Geometric Feature ◽  
Proposed Model ◽  
Tolerance Modeling ◽  
Geometric Deviations ◽  
Geometry Feature ◽  
Tolerance Model ◽  
Definition Of ◽  
Gps System ◽  
New Generation ◽  
Product Specifications

The aim of this paper is to present a 3D tolerance mathematical model for complex feature geometric variations oriented the new generation Geometrical Product Specifications (GPS). According to the definition of geometric feature and taxonomy of geometric variations in the new generation GPS system, the proposed model is based on the small displacements torsor (SDT) concept, and the geometric deviations treated as relative location or rotation variations between the nominal geometry feature and the associated feature are expressed by the SDT parameters. Moreover, the tolerance models for several kinds of complex feature geometric variations are constructed and discussed. Finally, a numerical example is given to illustrate the application of the proposed tolerance model, and the results indicate that the proposed model is capable in dimensional and geometrical tolerances modeling. The model completely based on the mathematics foundation is in compliance with the current GPS standards system.

An Adaptive Intrusion Tolerance Model Based on Autonomic Computing

2010 ◽  
Vol 44-47 ◽  
pp. 3259-3263
Author(s):  
Qing Tao Wu ◽  
Bin Hua ◽  
Rui Juan Zheng ◽  
Ming Chuan Zhang
Keyword(s):  
Autonomic Computing ◽  
Feedback Mechanism ◽  
Adaptive Performance ◽  
Intrusion Tolerance ◽  
Dynamic Reliability ◽  
Model Based ◽  
System Survivability ◽  
Network Connection ◽  
Tolerance Model ◽  
Intrusion Tolerant

Intrusion tolerance has been a key technology of system survivability. To cope with the absence of self-adaptability on existent intrusion tolerance system, an intrusion tolerant model based on autonomic computing is proposed, in which the reliability of the network connection is analyzed in real time to generate the initial reliability by an autonomic feedback mechanism, and the classification between suspicious information and confident information is guaranteed for implementing the tolerance on suspicious connections through the dynamic reliability optimization. The simulation results show that the intrusion tolerant model with autonomic feedback mechanism is desirable for enhancing the system’s self-adaptive performance, making it highly promising for further study.

The Study on Network Security Strategy Based on Security Evaluation

2013 ◽  
Vol 443 ◽  
pp. 446-450
Author(s):  
Yan Bi
Keyword(s):  
Network Security ◽  
Assessment Model ◽  
Security Evaluation ◽  
Security Strategy ◽  
System Safety ◽  
Correct Understanding ◽  
Effective Assessment ◽  
The Impact ◽  
Domain Information ◽  
Security Capability

The feedbacks of the results of assessing the network security strategy based on security capability, as a reference to the security strategy for effective adjustment, providing a more robust system and improve the safety. The correct security strategy results from the correct understanding on the system security demands and effective assessment model, because the various security domain information subjects and objects have different security needs. On the basis of the establishment of the network security strategy based on security domain, through analysis of the security strategy defensive methods, application objects, information security attributes and their relationship, this paper proposed an assessment model of network security strategy based on security capability, and analyzed the impact of the network security strategy attributes upon network security capability, that is the robust security strategy is conducive to system safety.

Quality Control of Information Engineering Surveillance Based on Baum-Welch Algorithm

2011 ◽  
Vol 63-64 ◽  
pp. 178-181
Author(s):  
Hong Zhi Liu ◽  
Li Gao
Keyword(s):  
Quality Control ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Random Process ◽  
Real Time ◽  
Software Quality ◽  
Hidden Markov ◽  
Proposed Model ◽  
Information Engineering ◽  
Five Elements

A new method of Quality Control for Information Engineering Surveillance based on Hidden Markov Model (HMM) has been proposed and the related model been built by us. The process of information engineering quality surveillance can be seen as a two-layered random process. The five elements of HMM correspond with the process of quality surveillance through abstracting the characteristics of the surveillance process. Software quality can be estimated under the model. In this paper, we divided the five elements. Therefore, the model was improved from single dimension to multi-dimension, trained by Baum-Welch algorithm. Experimental results show that the proposed model proves to be feasible and real-time when it is used for quality control.

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