Increasing Accuracy and Reliability of IP Traceback for DDoS Attack Using Completion Condition

Abstract Probabilistic Packet Marking (PPM ) is one of the most promising schemes for performing IP Traceback. PPM reconstructs the attack graph in order to trace back to the attackers. Finding the Completion Condition Number (i.e. precise number of packets required to complete the traceback) is very important. Without a proper completion-condition, we might reconstruct a wrong attack-graph and attackers can evade detection. One presently being used works only for a single attacker based DoS attack and has an accuracy of just around 70%. We propose a new Completion Condition Number which has an accuracy of 95% and it works even for the multiple attacker based DDoS attacks. We confirm the results using detailed theoretical analysis and extensive simulation work. To the best of our knowledge, we are the first to apply the concept of Completion Condition Number to increase the reliability of IP Traceback for the DDoS attacks

An Analytical Model for Information Gathering and Propagation in Social Networks using Random Graphs

In this paper, we propose an analytical model for information gathering and propagation in social networks using random sampling. We represent the social network using the Erdos–Renyi model of the random graph. When a given node is selected in the social network, information about itself and all of its neighbors are obtained and these nodes are considered to be discovered. We provide an analytical solution for the expected number of nodes that are discovered as a function of the number of nodes randomly sampled in the graph. We use the concepts of combinatorics, probability, and inclusion–exclusion principle for computing the number of discovered nodes. This is a computationally-intensive problem with combinatorial complexity. This model is useful when crawling and mining of the social network graph is prohibited. Our work finds application in several important real-world decision support scenarios such as survey sample selection, construction of public directory, and crowdsourced databases using social networks, targeted advertising, and recommendation systems. It can also be used for finding a randomized dominating set of a graph that finds applications in computer networks, document summarization, and biological networks. We have evaluated the performance both analytically as well as by means of simulation, and the results are comparable. The results have an accuracy of around 96% for random graphs and above 87% for the power-law graphs

Improving the Efficiency of Hematite Nanorods for Photoelectrochemical Water Splitting by Doping with Manganese

Here, we report a significant improvement of the photoelectrochemical (PEC) properties of hematite (α-Fe2O3) to oxidize water by doping with manganese. Hematite nanorods were grown on a fluorine-treated tin oxide (FTO) substrate by a hydrothermal method in the presence on Mn. Systematic physical analyses were performed to investigate the presence of Mn in the samples. Fe2O3 nanorods with 5 mol % Mn treatment showed a photocurrent density of 1.6 mA cm(-2) (75% higher than that of pristine Fe2O3) at 1.23 V versus RHE and a plateau photocurrent density of 3.2 mA cm(-2) at 1.8 V versus RHE in a 1 M NaOH electrolyte solution (pH 13.6). We attribute the increase in the photocurrent density, and thus in the oxygen evolving capacity, to the increased donor density resulting from Mn doping of the Fe2O3 nanorods, as confirmed by Mott-Schottky measurement, as well as the suppression of electron-hole recombination and enhancement in hole transport, as detected by chronoamperometry measurements