scholarly journals Simulation of Attack Signal Path Identification in Radio Network Information

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Ma Yuanyuan
Keyword(s):  
Time Series ◽  
Traditional Method ◽  
Time Window ◽  
Radio Network ◽  
Network Information ◽  
Attack Graph ◽  
Attack Path ◽  
Signal Path ◽  
Boosting Method ◽  
Sample Set

It is possible to improve the safety performance of the radio network by accurately identifying the attack signal path in the radio network information. When the attack signal path identification is carried out, it is necessary to calculate the vulnerability of the attack signal path, obtain the time series sample set of the net-work attack path, train the sample set for different vulnerabilities, and complete the path recognition.The traditional method is to establish the attack graph model. It cannot calculate the vulnerability of the attack signal path and cannot carry out the training, which leads to the limitation of the path recognition and the low efficiency. An improved identification method of attack signal path in radio network information with improved attack graph is proposed. Firstly, the attack map of the traditional method is transformed according to the theory of graph theory, and the concept of vulnerability factor is introduced into the improved attack graph. The vulnerability of the attack route is improved, and then the sliding time window is used to construct the network attack path recognition Time series sample set, Ada-Boosting method is used to train the sample sets with different vulnerabilities,and the regression matrix is obtained by using the training results. Finally, the attack signal path identification in the radio network information is completed.The simulation results show that the improved method has high accuracy and can guarantee the smooth operation ofthe radio network.

Computer Network Attack Modeling and Network Attack Graph Study

2014 ◽  
Vol 1079-1080 ◽  
pp. 816-819 ◽  
Author(s):  
Yuan Qin
Keyword(s):  
Network Security ◽  
Computer Network ◽  
Multiple Point ◽  
Network Attack ◽  
Network Information ◽  
Attack Graph ◽  
Attack Modeling ◽  
Network Intrusion ◽  
Single Host ◽  
Attack Path

With the development of computer network and rapid popularity of Internet, network information security has become the focus of safeguarding national security and social stability. In the network security event, the hacker often can’t successfully intrude into the network by means of a single host / services hacker. With the help of various kinds of "vulnerability" generated bydifferent relationship existing in multiple point multiple host, the hacker can achieve the purpose of network intrusion. Therefore one important aspect of network security is after obtaining the vulnerability of the network information, considering a combination of multiple exploits and analyzing the attack path of network penetration attacks that the attacker may take.

scholarly journals Goodness-of-Fit Based Secure Cooperative Spectrum Sensing for Cognitive Radio Network

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Hiep Vu-Van ◽  
Insoo Koo
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Goodness Of Fit ◽  
Cognitive Radio Network ◽  
Cooperative Spectrum Sensing ◽  
Harmful Effect ◽  
Radio Network ◽  
Measured Distance ◽  
Simulation Results ◽  
Sample Set

Cognitive radio (CR) is a promising technology for improving usage of frequency band. Cognitive radio users (CUs) are allowed to use the bands without interference in operation of licensed users. Reliable sensing information about status of licensed band is a prerequirement for CR network. Cooperative spectrum sensing (CSS) is able to offer an improved sensing reliability compared to individual sensing. However, the sensing performance of CSS can be destroyed due to the appearance of some malicious users. In this paper, we propose a goodness-of-fit (GOF) based cooperative spectrum sensing scheme to detect the dissimilarity between sensing information of normal CUs and that of malicious users, and reject their harmful effect to CSS. The empirical CDF will be used in GOF test to determine the measured distance between distributions of observation sample set according to each hypothesis of licensed user signal. Further, the DS theory is used to combine results of multi-GOF tests. The simulation results demonstrate that the proposed scheme can protect the sensing process against the attack from malicious users.

scholarly journals Recurrence time distribution and temporal clustering properties of a cellular automaton modelling landslide events

2013 ◽  
Vol 20 (6) ◽  
pp. 1071-1078 ◽  
Author(s):  
E. Piegari ◽  
R. Di Maio ◽  
A. Avella
Keyword(s):  
Time Series ◽  
Probability Distribution ◽  
Cellular Automaton ◽  
Time Window ◽  
Fano Factor ◽  
Recent Analysis ◽  
Recurrence Time ◽  
Model Parameters ◽  
Reasonable Prediction ◽  
First Time

Abstract. Reasonable prediction of landslide occurrences in a given area requires the choice of an appropriate probability distribution of recurrence time intervals. Although landslides are widespread and frequent in many parts of the world, complete databases of landslide occurrences over large periods are missing and often such natural disasters are treated as processes uncorrelated in time and, therefore, Poisson distributed. In this paper, we examine the recurrence time statistics of landslide events simulated by a cellular automaton model that reproduces well the actual frequency-size statistics of landslide catalogues. The complex time series are analysed by varying both the threshold above which the time between events is recorded and the values of the key model parameters. The synthetic recurrence time probability distribution is shown to be strongly dependent on the rate at which instability is approached, providing a smooth crossover from a power-law regime to a Weibull regime. Moreover, a Fano factor analysis shows a clear indication of different degrees of correlation in landslide time series. Such a finding supports, at least in part, a recent analysis performed for the first time of an historical landslide time series over a time window of fifty years.

An Attack Graph Based Approach for Threat Identification of an Enterprise Network

2009 ◽  
pp. 23-47 ◽  
Author(s):  
Somak Bhattacharya ◽  
Samresh Malhotra ◽  
S. K. Ghosh
Keyword(s):  
Causal Relationship ◽  
Risk Mitigation ◽  
Automatic Assessment ◽  
Enterprise Network ◽  
Attack Graphs ◽  
Attack Graph ◽  
Attack Path ◽  
Critical Resource ◽  
Attack Surface ◽  
The Individual

As networks continue to grow in size and complexity, automatic assessment of the security vulnerability becomes increasingly important. The typical means by which an attacker breaks into a network is through a series of exploits, where each exploit in the series satisfies the pre-condition for subsequent exploits and makes a causal relationship among them. Such a series of exploits constitutes an attack path where the set of all possible attack paths form an attack graph. Attack graphs reveal the threat by enumerating all possible sequences of exploits that can be followed to compromise a given critical resource. The contribution of this chapter is to identify the most probable attack path based on the attack surface measures of the individual hosts for a given network and also identify the minimum possible network securing options for a given attack graph in an automated fashion. The identified network securing options are exhaustive and the proposed approach aims at detecting cycles in forward reachable attack graphs. As a whole, the chapter deals with identification of probable attack path and risk mitigation which may facilitate in improving the overall security of an enterprise network.

scholarly journals A Long Time-Series Radiometric Normalization Method for Landsat Images

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4505 ◽  
Author(s):  
Wei Wu ◽  
Xia Sun ◽  
Xianwei Wang ◽  
Jing Fan ◽  
Jiancheng Luo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Traditional Method ◽  
Acquisition Time ◽  
Reference Image ◽  
Optimization Strategy ◽  
Remote Sensing Images ◽  
Gray Scale ◽  
Long Time Series ◽  
Long Time

Radiometric normalization attempts to normalize the radiomimetic distortion caused by non-land surface-related factors, for example, different atmospheric conditions at image acquisition time and sensor factors, and to improve the radiometric consistency between remote sensing images. Using a remote sensing image and a reference image as a pair is a traditional method of performing radiometric normalization. However, when applied to the radiometric normalization of long time-series of images, this method has two deficiencies: first, different pseudo-invariant features (PIFs)—radiometric characteristics of which do not change with time—are extracted in different pairs of images; and second, when processing an image based on a reference, we can minimize the residual between them, but the residual between temporally adjacent images may induce steep increases and decreases, which may conceal the information contained in the time-series indicators, such as vegetative index. To overcome these two problems, we propose an optimization strategy for radiometric normalization of long time-series of remote sensing images. First, the time-series gray-scale values for a pixel in the near-infrared band are sorted in ascending order and segmented into different parts. Second, the outliers and inliers of the time-series observation are determined using a modified Inflexion Based Cloud Detection (IBCD) method. Third, the variation amplitudes of the PIFs are smaller than for vegetation but larger than for water, and accordingly the PIFs are identified. Last, a novel optimization strategy aimed at minimizing the correction residual between the image to be processed and the images processed previously is adopted to determine the radiometric normalization sequence. Time-series images from the Thematic Mapper onboard Landsat 5 for Hangzhou City are selected for the experiments, and the results suggest that our method can effectively eliminate the radiometric distortion and preserve the variation of vegetation in the time-series of images. Smoother time-series profiles of gray-scale values and uniform root mean square error distributions can be obtained compared with those of the traditional method, which indicates that our method can obtain better radiometric consistency and normalization performance.

Predicting microbial interactions from time series data with network information

2017 ◽  
Vol 17 (2) ◽  
pp. 97
Author(s):  
Yan Wang ◽  
Mingzhi Mao ◽  
Fang Li ◽  
Wenping Deng ◽  
Shaowu Shen ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Microbial Interactions ◽  
Series Data ◽  
Network Information

scholarly journals A New Method for Automated Clearcut Disturbance Detection in Mediterranean Coppice Forests Using Landsat Time Series

2020 ◽  
Vol 12 (22) ◽  
pp. 3720 ◽  
Author(s):  
Francesca Giannetti ◽  
Raffaello Pegna ◽  
Saverio Francini ◽  
Ronald E. McRoberts ◽  
Davide Travaglini ◽  
...  
Keyword(s):  
Time Series ◽  
Sustainable Forest Management ◽  
Time Window ◽  
Detection Algorithm ◽  
Forest Disturbances ◽  
Forest Change ◽  
Short Time Window ◽  
Short Time ◽  
Automated Procedures ◽  
Disturbance Detection

A Landsat time series has been recognized as a viable source of information for monitoring and assessing forest disturbances and for continuous reporting on forest dynamics. This study focused on developing automated procedures for detecting disturbances in Mediterranean coppice forests which are characterized by rapid regrowth after a cut. Specifically, new methods specific to Mediterranean coppice forests are needed for mapping clearcut disturbances over time and for estimating related indicators in the context of Sustainable Forest Management and Biodiversity International monitoring frameworks. The aim of this work was to develop a new change detection algorithm for mapping clearcut disturbances in Mediterranean coppice forests with Landsat time series (LTS) using a short time window. Accuracy for the new algorithm, characterized as the Two Thresholds Method (TTM), was evaluated using an independent clearcut reference dataset over a temporal period of the 13 years between 2001 and 2013. TTM was also evaluated against two benchmark approaches: (i) LandTrendr, and (ii) the forest loss category of the Global Forest Change Map. Overall Accuracy for LandTrendr and TTM were greater than 0.94. Meanwhile, smaller accuracies were always obtained for the GFC. In particular, Producer’s Accuracy ranged between 0.45 and 0.84 for TTM and between 0.49 and 0.83 for LT, while for the GFC, PA ranged between 0 and 0.38. User’s Accuracy ranged between 0.86 and 0.96 for TTM and between 0.73 and 0.91 for LT, while for the GFC UA ranged between 0.19 and 1.00. Moreover, to illustrate the utility of TTM for mapping clearcut disturbances in Mediterranean coppice forests, we applied TTM to a Landsat scene that covered almost the entirety of the Tuscany region in Italy.

Sign in / Sign up

Export Citation Format

Share Document