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Vulnerabilities in web applications are due to various factors. Failure to properly validated user input is one of the factors that led to run unauthorized code in these programs. Sqli and Xss are two common vulnerabilities in web applications, That is due to lack of proper input validation. Therefore, in this paper we study how to protect organizational websites of north khorasan in iran against Sqli and Xss vulnerabilities. We have analyzed eleven websites. Ten of which related to government organizations and one of them is from private organization. These Web sites have been tested with licenses taken from the relevant organizations.

This paper presents an exposition of a new method of swarm intelligence-based algorithm for optimization. Modeling swallow swarm movement and their other behavior, this optimization method represents a new optimization method. There are three kinds of particles in this method: explorer particles, aimless particles, and leader particles. Each particle has a personal feature but all of them have a central colony of flying. Each particle exhibits an intelligent behavior and, perpetually, explores its surroundings with an adaptive radius. The situations of neighbor particles, local leader, and public leader are considered, and a move is made then. Swallow swarm optimization algorithm has proved high efficiency, such as fast move in flat areas (areas that there is no hope to find food and, derivation is equal to zero), not getting stuck in local extremum points, high convergence speed, and intelligent participation in the different groups of particles. SSO algorithm has been tested by 19 benchmark functions. It achieved good results in multimodal, rotated and shifted functions. Results of this method have been compared to standard PSO, FSO algorithm, and ten different kinds of PSO. Refereed/Peer-reviewed

The Swarm Intelligence is a new and modern method employed in optimization problems. The Swarm Intelligence method is based on the en masse movement of living animals like birds, fishes, ants and other social animals. Migration, seeking for food and fighting with enemies are social behaviors of animals. Optimization principle is seen in these animals. The Artificial Fish Swarm Optimization (AFSA) method is one of the Swarm Intelligence approaches that works based on the population and stochastic search. Fishes show very intelligently social behaviors. This algorithm is one of the best approaches of the Swarm Intelligence method with considerable advantages like high convergence speed, flexibility, error tolerance and high accuracy. this paper review the AFSA algorithm, its evolution stages from the start point up to now, improvements and applications in various fields like optimization, control, image processing, data mining, improving neural networks, networks, scheduling, and signal processing and so on. Also, various methods combining the AFSA with other optimization methods like PSO, Fuzzy Logic, Cellular Learning Automata or intelligent search methods like Tabu search, Simulated Annealing, Chaos Search and etc.

The aim of this comparative study is to evaluate the effects of different methods, used for ''aggregation'' and ''defuzzification'', on the output. To reach this goal, six fuzzy inference systems (FIS-1, FIS-2, FIS-3, FIS-4, FIS-5 and FIS-6) have been designed (with the same rule bases) with different methods used for aggregation, defuzzification, and overlapping between the fuzzy sets. The idea of this system is based on the UCI dataset. To design the systems, some of the input fields of UCI dataset have been replaced with other important fields that made system more applicable and suitable. All of these designed systems have the same input fields such as: Water/cement ratio, slump, maximum size of aggregate, coarse aggregate, fine aggregate and age (day). The output field of all systems measures the compressive strength of concrete. These three differences in 401 laboratory samples have caused the average error of predicted compressive strength, that is, 6.43% FIS-1, 6.64% FIS-2, 6.48% FIS-3, 5.56% FIS-4, 4.73% FIS-5 and 5.07% FIS-6. The experimental results reveal that the methods of "sum" and "Centroid" (used in the FIS-5) show the best results (among other methods) for the "aggregation" and the "defuzzification", respectively. Key words: Aggregation step, defuzzification step, concrete compressive strength (CCS), fuzzy inference system (FIS), W/C ratio, slump, maximum size of aggregate, coarse aggregate, fine aggregate, age.

The aim of this study is to design adaptive neural-fuzzy inference system (ANFIS) model and fuzzy expert system for determination of concrete mix designs and finally compare their results. Idea of these systems based on two surveys: first, ACI structures and principles, second a concrete mix designs dataset that collected via Prof. I-Cheng Yeh. Datasets that loaded in to ANFIS has 552 mix designs and based on ACI mix designs. Moreover, in this study, we have designed fuzzy expert system. Input fields of fuzzy expert system are Slump, Maximum Size of Aggregate (D max), Concrete Compressive Strength (CCS), and Fineness Modulus. Output fields are quantities of water, cement, fine aggregate (F.A.) and coarse aggregate (C.A.). In the ANFIS model, we have four layers (four ANFIS models): the first layer takes values of D max and Slump and then determines the quantity of Water, the second layer takes values of Water (computed in the past layer) and CCS then measures the value of Cement, the third layer takes values of D max and Slump to compute C.A. and the fourth layer takes values of Water, Cement, and C.A. (determined in past layers) and then measures the value of F.A. When these systems were designed and tested, comparison between two systems (FIS and ANFIS) results showed that results of ANFIS model are better than fuzzy expert system's results. In the ANFIS model, for Water output field, training and average testing errors are 0.86 and 0.8. For cement field, training error and average testing error are in the orders of 0.21 and 0.22. Training and average testing error of C.A. are in the orders of 0.0001 and 0.0004 and finally, training and average testing errors of F.A. are in the orders of 0.0049 and 0.0063. Results of fuzzy expert system in comparison to ACI results follow average errors: average error of Water, Cement, C.A., and F.A. are in the orders of 9.5%, 27.6%, 96.5%, and 49%.

A deniable authentication allows the receiver to identify the source of the received messages but cannot prove it to any third party. However, the deniability of the content, which is called restricted deniability in this paper, is concerned in electronic voting and some other similar application. At present, most non-interactive deniable authentication protocols cannot resist weaken key-compromise impersonation (W-KCI) attack. To settle this problem, a non-interactive identity-based restricted deniable authentication protocol is proposed. It not only can resist W-KCI attack but also has the properties of communication flexibility. It meets the security requirements such as correctness, restricted deniability as well. Therefore, this protocol can be applied in electronic voting.