M. N. Doja

GENERALIZATION OF DIJKSTRA'S ALGORITHM FOR EXTRACTION OF SHORTEST PATHS IN DIRECTED MULTIGRAPHS

The classical Dijkstra’s algorithm to find the shortest path in graphs is not applicable to multigraphs. In this study the authors generalize the classical Dijkstra’s algorithm to make it applicable to directed multigraphs. The modified algorithm is called by Generalized Dijkstra’s algorithm or GD Algorithm (GDA in short). The GDA outputs the shortest paths and the corresponding min cost. It is claimed that GDA may play a major role in many application areas of computer science, communication, transportation systems, in particular in those networks which cannot be modeled into graphs but into multigraphs.

ABC Optimization Based Construction of Strong Substitution-Boxes

Most of the block ciphers contain substitution-boxes as their cornerstone primitive to add requisite nonlinearity. S-boxes are responsible to ensure strong confusion and resistance to linear and differential assaults. The security of such ciphers relies on the strength of S-boxes deployed during substitution stage. It is challenging to construct cryptographically strong S-boxes that satisfy several properties such as high nonlinearity, good avalanche effect, bit-independent criteria, low differential uniformity and linear probability, etc. In this paper, we proposed to construct an S-box based on artificial bee colony optimization and chaotic map. The algorithm is designed to optimize an initial S-box to satisfy many properties. The simulation outcomes and comparison with recent proposals show that the proposed ABC optimization based algorithm performs reasonably well and construct S-box that exhibits higher security strength.

Stable Period Extension for Heterogeneous Model in Wireless Sensor Network

In past few decades, energy efficiency issue in wireless sensor network (WSN) has attracted researchers due to its constrained power source. Focus on the parameters which affects the energy level of the sensor nodes of the WSN is the key to attain energy optimization. Introduction of heterogeneity increases the capability and lifetime. In this paper, we propose a heterogeneous-model-based energy efficient scheme for clustering. Any clustering algorithm which groups the sensors can contribute in increasing efficiency of the network. This paper proposes an energy conscious clustering method which takes into account the energy of the nodes residing within the proximity of its transmission range. Indecent designed self-organizing clustering algorithm can drop down the lifetime of the nodes. The simulation work of the proposed algorithm is done for heterogeneous energy model with varying parameters. Simulation results ratify the stability period extension of proposed protocol. The proposed algorithm is capable to prolong the stable period of network and balances the overall energy dissipation of the network over its comparatives.

A New Hyperchaotic System-Based Design for Efficient Bijective Substitution-Boxes

In this paper, we present a novel method to construct cryptographically strong bijective substitution-boxes based on the complicated dynamics of a new hyperchaotic system. The new hyperchaotic system was found to have good characteristics when compared with other systems utilized for S-box construction. The performance assessment of the proposed S-box method was carried out based on criteria, such as high nonlinearity, a good avalanche effect, bit-independent criteria, and low differential uniformity. The proposed method was also analyzed for the batch-generation of 8 × 8 S-boxes. The analyses found that through a proposed purely chaos-based method, an 8 × 8 S-box with a maximum average high nonlinearity of 108.5, or S-boxes with differential uniformity as low as 8, can be retrieved. Moreover, small-sized S-boxes with high nonlinearity and low differential uniformity are also obtainable. A performance comparison of the anticipated method with recent S-box proposals proved its dominance and effectiveness for a strong bijective S-box construction.

Analysis of weight initialization methods for gradient descent with momentum

The back propagation algorithm using gradient descent with momentum is a commonly used training algorithm for the artificial neural networks. In this work, a set of experiments were conducted to obtain a detailed comparison of various known weight initialization methods. By doing so, the best suited weight initialization routines for the gradient descent approach with momentum was identified. Six problems of the functions approximation domain were selected for these experiments. Statistical metrics like one sided tailed t-test, the standard deviation of simulation error as well as its mean value were evaluated and used for the purpose of decision making. Results obtained from these experiments strongly advocate that the weight initialization method proposed by Nguyen and Widrow was the best suited technique while training the network by Gradient Descent with momentum approach.

An Improvement in DSR Routing Protocol of MANETs Using ANFIS

In mobile ad hoc networks(MANET’s), dynamically changing topology, constraints over energy, breakage of links and providing security to network results in the generation of multiple routes from source to destination. One of the well-known reactive routing protocol of MANET is named as Dynamic Source Routing (DSR) which stored the value of previously determined path to be used in route cache of intermediate nodes. Therefore, while selection from multiple routes this cache information is used to determine new route. Since the amount of cache is limited, therefore it is impossible to store all the information over cache. One of the solutions to this problem is to have cache information not only on the basis of hop count as done by DSR. Hence in the proposed algorithm, the output parameter “Anfis cost (Anf)” is computed on the basis of input parameters “hop count”, “energy”, and “delay” to select the optimal route in cache. The proposed routing algorithm uses adaptive fuzzy inference system (ANFIS) to improve the performance of DSR protocol hence named as A-DSR. The simulation results proved that the proposed A-DSR protocol have substantial high value of packet delivery ratio compared to state-of-art soft computing-based protocol and the conventional DSR.

Group based centrality for immunization of complex networks

Abstract Network immunization is an extensively recognized issue in several domains like virtual network security, public health and social media, to deal with the problem of node inoculation so as to minimize the transmission through the links existed in these networks. We aim to identify top ranked nodes to immunize networks, leading to control the outbreak of epidemics or misinformation. We consider group based centrality and define a heuristic objective criteria to establish the target of key nodes finding in network which if immunized result in essential network vulnerability. We propose a group based game theoretic payoff division approach, by employing Shapley value to assign the surplus acquired by participating nodes in different groups through the positional power and functional influence over other nodes. We tag these key nodes as Shapley Value based Information Delimiters (SVID). Experiments on empirical datasets and model networks establish the efficacy of our proposed approach and acknowledge performance of node inoculation to delimit contagion outbreak.

Performance Enhancement of AODV Routing Protocol Using ANFIS Technique

The moving nodes of mobile ad hoc networks (MANETs) can wander arbitrarily and thus therefore form dynamic topologies. This dynamic nature of MANET characteristics affects the communication process. In MANET’s communication process, the packet may also be delayed due to various reasons like congestion, link failure, and power failure. This constant update of variables leads to development of routing algorithms that consider delay and hop count for routing decision. Therefore, an improvement of AODV routing algorithm using adaptive fuzzy logic system is proposed in this paper. In the presented algorithm, each node will calculate its cost value based on input parameters, i.e., hop count and delay. Further, with the routing decision, optimal path route is selected based on minimum cost value. This leads to better utilization of the network in terms of packet delivery ratio and end-to-end delay. NS2.35 is used for simulation process and results show the better performance of proposed AAODV algorithm than standard AODV.

A Chaotic Steganography Method Using Ant Colony Optimization

Since ancient times, there is a need to save data from third parties that spy on the data, leads to the practice of steganography to ensure data integrity and privacy. The advent in multimedia technologies and greater need for security has raised the popularity of image steganography. The traditional algorithm used for image steganography is the Least Significant Bit Method (LSB). A better strategy to overcome the LSB method is to hide data only in edge pixel of image. In this paper, first we apply the ant colony optimization (ACO) method to find edges of an image given by Xiaochen Liu et al. then will hide sensitive message in edges of image randomly to provide more security level. The edge detection technique and chaotic scheme-based steganography is introduced based on the ACO. This strategy efficiently finds the edges and does not attract much attraction compared to other traditional methods. Our result indicates the higher value of PSNR that shows the performance of the proposed algorithm.

Stability of Local Information-Based Centrality Measurements Under Degree Preserving Randomizations

Node centrality is one of the integral measures in network analysis with wide range of applications from socioeconomic to personalized recommendation. We argue that an effective centrality measure should undertake stability even under information loss or noise introduced in the network. With six local information-based centrality metric, we investigate the effect of varying assortativity while keeping degree distribution unchanged, using networks with scale free and exponential degree distribution. This model provides a novel scope to analyze the stability of centrality metric which can further find many applications in social science, biology, information science, community detection and so on.