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Distributed denial-of-service (DDoS) attacks cause havoc by exploiting threats to Internet services. In this paper, we propose E-LDAT, a lightweight extended-entropy metric-based system for both DDoS flooding attack detection and IP (Internet Protocol) traceback. It aims to identify DDoS attacks effectively by measuring the metric difference between legitimate traffic and attack traffic. IP traceback is performed using the metric values for an attack sample detected by the detection scheme. The method uses a generalized entropy metric with packet intensity computation on the sampled network traffic with respect to time. The E-LDAT system has been evaluated using several real-world DDoS datasets and outperforms competing methods when detecting four classes of DDoS flooding attacks, including constant rate, pulsing rate, increasing rate and subgroup attacks. The IP traceback model is also evaluated using NetFlow data in near real-time and performs well in large-scale attack networks with zombies. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Distributed denial of service (DDoS) attack is a coordinated attack, generally performed on a massive scale on the availability of services of a target system or network resources. Owing to the continuous evolution of new attacks and ever-increasing number of vulnerable hosts on the Internet, many DDoS attack detection or prevention mechanisms have been proposed. In this paper, we present a comprehensive survey of DDoS attacks, detection methods and tools used in wired networks. The paper also highlights open issues, research challenges and possible solutions in this area. [ABSTRACT FROM AUTHOR]

Password based authenticated key exchange (PAKE) allows sharing short passwords between two users to agree on a cryptographically strong session key. However, the main challenge in designing this type of protocol is that it must be secure from offline dictionary and various types of man-in-themiddle attacks. In this paper, we present a fast, easily implementable, yet secure cellular automata (CA) based 2-party key exchange protocol called CA-KEP. The proposed protocol is designed based on the features of group and non-group programmable CAs. CA-KEP is superior in comparison to [1-5] considering speed and invulnerabilities to most of the known attacks. [ABSTRACT FROM AUTHOR]

Protein complexes are known to play a major role in controlling cellular activity in a living being. Identifying complexes from raw protein protein interactions (PPIs) is an important area of research. Earlier work has been limited mostly to yeast. Such protein complex identification methods, when applied to large human PPIs often give poor performance. We introduce a novel method called CSC to detect protein complexes. The method is evaluated in terms of positive predictive value, sensitivity and accuracy using the datasets of the model organism, yeast and humans. CSC outperforms several other competing algorithms for both organisms. Further, we present a framework to establish the usefulness of CSC in analyzing the influence of a given disease gene in a complex topologically as well as biologically considering eight major association factors.

This paper presents an effective parameter-less graph based clustering technique (GCEPD). GCEPD produces highly coherent clusters in terms of various cluster validity measures. The technique finds highly coherent patterns containing genes with high biological relevance. Experiments with real life datasets establish that the method produces clusters that are significantly better than other similar algorithms in terms of various quality measures.

We have developed a software system called SIMISYS that models and simulates aspects of the human immune system based on the computational framework of cellular automata. We model tens of thousands of cells as exemplars of the significant players in the functioning of the immune system, and simulate normal and simple disease situations by interpreting interactions among the cells. SIMISYS 0.3, the current version, models and simulates the innate and adaptive components of the immune system. The specific players we model are the macrophages, dendritic cells, neutrophils, natural killer cells, B cells, T helper cells, complement proteins and pathogenic bacteria.