Intrusion Detection Algorithm Based on Density,Cluster Centers,and Nearest Neighbors

Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic firewalls.Many intrusion detection methods are processed through machine learning.Previous literature has shown that the performance of an intrusion detection method based on hybrid learning or integration approach is superior to that of single learning technology.However,almost no studies focus on how additional representative and concise features can be extracted to process effective intrusion detection among massive and complicated data.In this paper,a new hybrid learning method is proposed on the basis of features such as density,cluster centers,and nearest neighbors(DCNN).In this algorithm,data is represented by the local density of each sample point and the sum of distances from each sample point to cluster centers and to its nearest neighbor.k-NN classifier is adopted to classify the new feature vectors.Our experiment shows that DCNN,which combines K-means,clustering-based density,and k-NN classifier,is effective in intrusion detection.

Anomalous Cell Detection with Kernel Density-Based Local Outlier Factor

Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.

Management factors affecting densities of common grassland birds of high elevation grasslands of eastern South Africa:Ingula as a case study

Background: World?wide grassland birds are in decline due to habitat loss and degradation resulting from inten?sive agricultural practices. Understanding how key grassland habitat attributes determine grassland bird densities is required to make appropriate conservation decisions. We examine drivers of bird densities in a South African grass?land area that has been managed for biodiversity conservation with reduced grazing pressure.Methods: We estimated the density of the eight most common grassland bird species encountered in our area to evaluate the effects of recent grassland management changes on the avifauna. We collected data on birds and habitat from the austral summers of 2006/2007, 2007/2008 and 2010/2011. We used hierarchical distance sampling methods to estimate density of birds relative to two main habitat variables, i.e., grass cover and height. In addition, we used regression splines within these distance sampling models as a more flexible description of suitable ranges of grass height and cover for each species.Results: For most species, density is related to grass height and cover as expected. The African Quailfinch(Ortygospiza atricollis) and Common Quail(Coturnix coturnix) preferred relatively short and open grass. The Yellow?breasted Pipit(Anthus chloris), African Pipit(Anthus cinnamomeus) and Red?capped Lark(Calandrella cinerea) preferred short and relatively dense grass, while the Wing?snapping Cisticola(Cisticola ayresii) preferred grass of intermediate height and cover. The Cape Longclaw(Macronyx capensis) and Zitting Cisticola(Cisticola juncidis) preferred tall and dense grass. Our results agree with previous studies that grass height combined with grass cover are the most important habitat features that managers should manipulate in order to increase the density of target species. The regression splines show that the effect of these two habitat variables on density is well described by linear relationships for most species.Conclusions: This study supports previous studies suggesting that grazing and fire are important tools for manage?ment to use in order to create a mosaic of grass height and cover that would support high densities of desired spe?cies. We suggest that conservation managers of these grasslands combine fire and grazing as management tools to create suitable habitats for grassland birds in general.

A study of the strong pulses detected from PSR B0656+14 using the Urumqi 25-m radio telescope at 1540 MHz

We report on the properties of strong pulses from PSR B0656＋14 by analyzing the data obtained using the Urumqi 25-m radio telescope at 1540 MHz from August 2007 to September 2010.In 44 h of observational data,a total of 67 pulses with signal-to-noise ratios above a 5σthreshold were detected.The peak flux densities of these pulses are 58 to 194 times that of the average profile,and their pulse energies are 3 to 68 times that of the average pulse.These pulses are clustered around phases about 5-ahead of the peak of the average profile.Compared with the width of the average profile,they are relatively narrow,with the full widths at half-maximum ranging from 0.28 ° to 1.78 °.The distribution of pulse-energies follows a lognormal distribution.These sporadic strong pulses detected from PSR B0656＋14 have different characteristics from both typical giant pulses and its regular pulses.

Improved Flight Conflict Detection Algorithm Based on Gauss-Hermite Particle Filter

In order to improve the accuracy of free flight conflict detection and reduce the false alarm rate, an improved flight conflict detection algorithm is proposed based on Gauss-Hermite particle filter（GHPF）. The algorithm improves the traditional flight conflict detection method in two aspects：（i） New observation data are integrated into system state transition probability, and Gauss-Hermite Filter（GHF） is used for generating the importance density function.（ii） GHPF is used for flight trajectory prediction and flight conflict probability calculation. The experimental results show that the accuracy of conflict detection and tracing with GHPF is better than that with standard particle filter. The detected conflict probability is more precise with GHPF, and GHPF is suitable for early free flight conflict detection.