JOURNAL OF ELECTRONIC AND CYBER DEFENCE
Year:2017 | Volume:5 | Issue:1 (17) |Papers Count:9

On June 2013, Beaulieu and et. al from the U. S National Security Agency proposed a family of block ciphers, SIMON. This family of block ciphers is classified as lightweight block ciphers that comes in a variety of widths and key sizes. SIMON offers excellent performance on hardware and software platforms from which hardware performance is optimal. The main purpose of this paper is to provide improved differential attacks proposed on this family of block ciphers. Getting help from the new ideas and viewpoints about methods and key-guessing policies, we improve differential attack on 22-round SIMON32, 23-round SIMON48 and 29-round SIMON64. This attack adds one round to the latest differential cryptanalysis presented before this paper submission.

In this paper, a generalized scheme is proposed for extracting biometric keys from keystroke dynamics of the user. In the proposed scheme, first some features of are extracted from keystroke patterns of the user. Then, using a secret sharing scheme, a number of true shares are assigned to each feature to reproduce the secret key based on its discriminatory power. This true shares are hidden among random shares. When a user wants to be authenticated, the same features are extracted from his or her keystroke pattern and if its value is in the proper region, a true share is extracted. If the total number of true shares are extracted, and exceeds a threshold, reproduction of the secret key will be possible. Otherwise no information about the key will be leaked. Two parameters are used in the scheme, through which FAR and FRR can be balanced. The proposed scheme is able to extract 20 to 50 bits of secret key (besides the intrinsic entropy of the password itself). The equal error rate (ERR) of the scheme is nearly 4%. Besides, on top of a traditional password-based system, using the proposed scheme increases the security by a factor of 2 or 3 (in number of bits).

Steganography is largely used in information security systems. In steganography, the goal is to hide the information such that only the sender and receiver are aware of the communication and information. Steganography is carried out on different media such as image, sound and text. It can be performed in both transform and spatial domains. In transform domain methods, first the cover image is transformed into a different domain using, for example, a frequency transform. Then, the secret message is embedded in the conversion coefficients. In this paper, a new steganography method based on the transform domain is proposed to conceal text in an HSV color image. After converting the image to HSV, it is divided into blocks and the mixed column transfer is applied on each block. The secret message is embedded in the transferred columns. Simulation results and comparisons show the high capacity and increased security of the proposed method.

Adaptive filters are one of the most important building blocks of digital signal processing (DSP) systems which are used in a wide variety of applications such as echo and noise cancellation, channel equalizers, radar and sonar systems. Compared to software implementation, hardware implementation of DSP systems has some inherent advantages including higher speed and throughput, integratability and parallel processing. In recent years, Field Programmable Gate Arrays (FPGAs) have received a lot of attention due to their architecture flexibility, low cost and providing the possibility of parallel processing of DSP algorithms. Efficient realization of adaptive filters on FPGAs has always been a motivational and challenging research topic. In this article, we have presented an efficient hardware implementation of a 9-tap LMS adaptive filter that is much faster and consumes fewer resources compared to similar published works. The results have been verified by comparing those obtained from hardware implementation of an LMS adaptive noise canceller filter with the results obtained from simulation of a similar filter. Since the permanent collection and processing of meaningful signals are of the fundamentals of management cycle and crisis prevention, the presented design can be well used in hardware equipment related to this area.

One of the most important areas of symmetric cryptography is block cipher algorithms which have many applications in security mechanisms. Linear and differential cryptanalysis are the most important statistical attacks against block ciphers. Since most of the attacks against block cipher algorithms are based on these two types of cryptanalysis, encryption algorithm design methods are guided to resist these attacks. This paper presents a new block cipher design method based on data dependent key which prevents linear and differential attacks. Based on the proposed method, an instance structure for block cipher algorithms is presented and evaluated. It has been shown that the proposed structure resists linear and differential attacks even in reduced number of rounds.

Moving target tracking is a process in which an object is tracked and its location is determined in each frame. The goal of this process is facilitating the subsequent process in order to analyze the behavior or detect moving objects. In this paper, a new approach has been proposed for aerial moving targets detection and tracking based on feature matching algorithms. By this way, we propose spectral density for target detection and ASIFT feature matching algorithm for tracking. The challenge is selecting features that are robust against the changes of brightness, noise, rotation, scaling and viewing angle. To solve this problem, key points and their correspondence on the patterns extracted from consecutive frames, are calculated by the ASIFT algorithm. Also, to reduce false matches in consecutive frames, the RANSAC algorithm is used. In addition to strengthening the proposed algorithm against the scale change of target, the object history scale in the 10 previous frames is used. The proposed algorithm was performed on an AIRCRAFT TRACKING standard database. Experimental results demonstrate the robustness and accuracy of our proposed technique.

One of the most impressive tools for evaluation of Cyber security is the usage of the secure verification system with high reliability.Biometric based verification systems have been able to overcome this problem to a large extent.In many cases,some consideration such as hardware cost,speed and accuracy make chal-lenges to choose the architecture of verification system.In this paper,an ordinary sensor is used to increase the security and accuracy of the verification system and also decrease the hardware cost.After acquiring the hand image,it is divided into 6 segments and finally by using Arc-Gabor filter bank as a descriptor,unique features are extracted from images.Also,by extracting knowledge from the FAR and FRR curves in order to estimate verifiers’weight and using weighted score summation for information fusion in the level of matching score,the verification system is designed.In this system,by utilizing one ordinary sensor and potential of multimodal architecture,its average of Equal Error Rate for COEP database is around 2.25% and also the average of elapsed time for verification is less than 0.19 seconds.

With the rapid growth of developing malwares in Android platform as the widest used mobile operating system, knowing security risk of an application (app) can be helpful for warning users regarding the use of potential malicious applications. The security risk of an Android app can be estimated using its requested permissions. In this paper, the concept of critical permissions is precisely re-defined according to the abuse of permissions by previously known Android malwares. Based on this definition and analysis of requested permissions of the large numbers of malwares and benign apps, a new criterion is proposed to measure the security risk of the apps. In this criterion, informative permissions have higher impact on the resulting measured security risk values of the apps. Experimental evaluations show the superiority of the proposed criterion with respect to previously proposed ones in terms of detection rete and generalization capability.

Cyber operations, which the operation of distributed denial of service (DDoS) are the most important type of impact can cause a variety of effects. Measuring the success of a cyber-attack is hard; because of the complexity of the effects of the attacks have conditions like high uncertainty, widespread and nonlinear relationship between power and efficiency of operations with the effects of their condition. Generally to overcome the above challenges of integrating data from multiple sensors are used. In the operations, the sensors are divided into two types: Open and Closed sensor, but sensors detected in cyberspace are more important. In this study a suitable mechanism is designed to measure the success of DDoS attacks. For feedback and measure the ultimate effect of the DDoS, from open sources as sensors and receive the effects of this type of operation is used. The data open sources close to linguistically and also expressed as a range imprecise and fuzzy method so that one of the best ways of solving problems is uncertainty been used. The weighted fuzzy logic and fuzzy Delphi sensors have been used simultaneously for data integration.