Adaptive clustering with feature ranking for DDoS attacks detection

Distributed Denial of Service (DDoS) attacks pose an increasing threat to the current internet. The detection of such attacks plays an important role in maintaining the security of networks. In this paper, we propose a novel adaptive clustering method combined with feature ranking for DDoS attacks detection. First, based on the analysis of network traffic, preliminary variables are selected. Second, the Modified Global K-means algorithm (MGKM) is used as the basic incremental clustering algorithm to identify the cluster structure of the target data. Third, the linear correlation coefficient is used for feature ranking. Lastly, the feature ranking result is used to inform and recalculate the clusters. This adaptive process can make worthwhile adjustments to the working feature vector according to different patterns of DDoS attacks, and can improve the quality of the clusters and the effectiveness of the clustering algorithm. The experimental results demonstrate that our method is effective and adaptive in detecting the separate phases of DDoS attacks. © 2010 IEEE

An application of consensus clustering for DDoS attacks detection

The detection of Distributed Denial of Service (DDos) attacks is very important for maintaining the security of networks and the Internet. This paper introduces a novel iterative consensus process based on Hybrid Bipartite Graph Formulation (HGBF) consensus function for DDos attacks detection. First, the features are extracted during feature extraction process based on the analysis of network traffic. Second, several clustering algorithms are applied in combination with the silhouette index to obtain a collection of independent initial clusterings. Third, the HGBF consensus function and silhouette index are used to find an appropriate consensus clustering of the initial clusterings. Fourth, this new consensus clustering is added to the pool of initial clusterings replacing another clustering with the worst Silhouette index. Fifth, the process continues iteratively until the Silhouette index of the resulting consensus clusterings stabilizes. This iterative consensus clustering process can improve the quality of the clusters. The experimental results demonstrate that our iterative consensus process is effective and can be used in practice for detecting the separate phased of DDos attacks

Optimal rees matrix constructions for analysis of data

Abstract We introduce a new construction involving Rees matrix semigroups and max-plus algebras that is very convenient for generating sets of centroids. We describe completely all optimal sets of centroids for all Rees matrix semigroups without any restrictions on the sandwich matrices. © 2013 Australian Mathematical Publishing Association Inc

Role of Microorganisms in Microbial Fuel Cells for Bioelectricity Production

The catalytic microorganisms oxidise the organic matter to produce electrical energy in microbial fuel cells (MFCs). The microorganisms that can shuttle the electrons exogenously to the electrode surface without utilising artificial mediators are referred as exoelectrogens. The microorganisms produce specific proteins or genes for their inevitable performance towards electricity generation in MFCs. Multiple studies have confirmed the expression of certain genes for outer membrane multiheme cytochromes (e.g. OmcZ), redox-active compounds (e.g. pyocyanin), conductive pili, and their potential roles in the exoelectrogenic activity of various microorganisms, particularly in the members of Geobacteraceae and Shewanellaceae family. This chapter explores the various mechanisms of microorganisms that are advantageous for the technology: biofilm formation, metabolism, electron transfer mechanisms from inside the microorganisms to the electrodes and vice versa