JOURNAL OF ELECTRONIC AND CYBER DEFENCE
Year:2016 | Volume:4 | Issue:2 (14) |Papers Count:7

Distributed denial of service (DDoS) attacks are one of the most important threats for E-commerce. Their main goal is to prevent the users from accessing to web sites and internet resources through excessive use of the resources. In these attacks, availability which is one of the elements of security is targeted. One of the ways to achieve this goal is to apply web robots by which the attackers design and carry out the DDoS attacks at application layer. Various methods have been used to distinguish between malicious and non-malicious web robots. One of the most popular methods in the recent years is data mining and machine learning. This method is based on extracting and selecting those features which are fit for web sessions via web server access log files and applying data mining algorithms. Considering the fact that the DDoS attacks are dynamic and customizable, in this research, an attempt is made to present a customizable dynamic defensive mechanism for detecting malicious web robots through the analysis of behaviors of their browsing. At the present study, features extraction was carried out based on the characteristics of DDoS attacks together with optimization of the previous methods to determine web sessions. Furthermore refining the extracted features and selecting a set of efficient features reduced the time required for building a model. As a consequence, the efficiency enhanced by two percent compared to the best similar study.

The secure and hidden communication, is one of the essential requirements in cyberspace. Steganography is the best approach, in which the communication security in common network like Internet, is provided. Steganography based on Mod4 is one of the prevalent methods in DCT transformation domain which embeds the message in the image in different way from the past methods. In this method, correlation between coefficients is kept well and is robust against blind steganalysis. The ultimate of this paper is to improve steganography based on Mod4, that is, increasing capacity and transparency, as two principal criteria in steganography. In the proposed method, by changing the size of GQC blocks and by determining the suitable conditions for choosing the valid blocks, transparency is increased more than 5 percent and capacity is increased more than 86 percent. For evaluation the stego images transparency in the proposed method, and comparing with basic Mod4 method, two criteria, PSNR and SSIM are used.

Recently, the use of the loran system in navigation has gained much attention respect to its jamming security and possibility of improving the performance using hardware and new algorithms. Various sources of the error, continuous wave interference as the main one, cause error in the phase tracing and consequently cause error in measuring the time of arrival which finally result in the accuracy of navigation. In this paper, using the structure of loran pulses in the time domain, a new algorithm is proposed in order to removing the continuous wave interference. This algorithm has low complexity, has better performance than the conventional scenario, and its implementation causes improving of performance and accuracy of navigation. The simulation results show that the performance of the proposed algorithm is approximately independent of SIR of the received signal, and the limiter factor is the SNR of the received signal only. The proposed algorithm for measuring the time of arrival (TOA) result in removing the effect of coherent interference (improvement compared to the other conventional scenario averaging and phase decoding with coefficient 1023). This noticeable improvement is provided in the cost of decreasing the received SIR caused by the non-coherent continuous wave interference with maximum value 3 dB which can be simply removed by use of a band pass filter.

Due to the very similar physical characteristics of active sonar echoes, which are related to true and false targets, the task of classification and distinguishing them from each other has become difficult and complex issues for the researchers and industrialists in this field. Radial Basis Function Neural Networks (RBF NN) is one of the most used artificial NNs in the classification of the real-world objects. Training is an important part of the development of this type of network that it has been highly regarded in recent years. For RBF NN training, the use of recursive and gradient descent methods has traditionally been common. However, poor classification accuracy, trapped in local minima, and low convergence speed are among the disadvantages of these methods. In recent years, the use of heuristic and meta-heuristic methods has been very common to overcome these disadvantages. To overcome the GSA’ s weakness in the exploitation phase, this paper introduces and uses Leader Mass Gravitational Search algorithm (LMGSA) in the training of RBF NNs. The results show that the designed classifier provides better results than benchmark classifiers in all areas. To a comprehensive comparison, the designed classifier is compared to GSA, Gradient Descent (GD), Genetic Algorithm (GA), Kalman Filter (KF), and Extended Kalman Filter (EKF) algorithms through three benchmark datasets. The evaluation criteria are convergence rate, the probability of being caught in local minima, and classification accuracy. Finally, as a practical application, sonar dataset is classified by the designed classifier.

CAESAR is a competition for designing authenticated encryption schemes (AE). The schemes that are considered in this competition are supported associated data (AEAD). 57 candidates have been submitted to this competition, out of them 30 candidates later announced as the second round candidates. In this paper, we analysis the security of MORUS, a second round candidate of CAESAR, against mixed integer linear programing based linear cryptanalysis. In this study, the length of associated data is considered as zero (AD|=0|) and linear characteristics for two version of MORUS, MORUS-640 and MORUS-1280, reduced to 3 rounds with bias and respectively are presented. The result of this paper is the first third party linear analysis on round reduced of MORUS, to the best of our knowledge.

Small targets Enhancement in infrared images are one of the most important issues in the target search and tracking systems. Target enhancement algorithms Provide more accurate differentiation between the back-ground and target areas in Infrared Images. In this paper, a new method of statistical weighted entropy has been suggested to suppress background clutters and increasing the contrast between target and clutter background. Local entropy is the best ways to express image intensity specifications. As a result, statistical-ly weighted entropy is designed in accordance with the fact that, existence of small target areas cause the major changes in statistically weighted entropy value in local window of infrared image and target areas and background can be easily separated from each other. In first stage, the local Statistical weighted entro-py corresponding to each pixel is calculated. At the end of this stage, background clutters are greatly sup-pressed and target areas are enhanced. In the next step, an automatic enhancement coefficient is used to highlight the targets and create higher contrast between the target area and background clutters. In order to qualitatively and quantitatively evaluation, the proposed algorithm was tested on a set of infrared images with 30 different background complex clutters. The results show that the proposed method can well sup-press background clutter, whereas enhance the target area and increase the possibility of detecting and tracking targets.

Security of the Advanced Encryption Standard (AES) block cipher is the main reason for using its compo-nents in the new designs. The diffusion layer is one of the main block ciphers components that effect the per-formance and security of them. Recursive diffusion layers that was defined formally in FSE 2012 by Sajadiyeh et al. are a class of the diffusion layers which using some words of output as inputs. In this pa-per, a new approach for designing a block cipher is defined. Using it, one can obtain a 256-block cipher based on the four rounds of AES and a recursive diffusion layer. The suggested algorithm is compared with Rijndael-256 and it is shown that it is more resistant against linear and differential cryptanalysis. Increas-ing this resistance, decreases the speed by one percent when compared by Rijndael-256, i. e. there is a tradeoff between the resistance and speed. Moreover, the suggested method has been generalized for de-signing the block ciphers with block sizes of 384 and 512 bit.