Recent advances in power system monitoring and control require communication infrastructure to send and receive measurement data and control commands. These cyber-physical interactions, despite increasing efficiency and reliability, have exposed power systems to cyber attacks. The Automatic generation control (AGC) is one of the most important control systems in the power system, which requires communication infrastructure and has been highly regarded by cyber attackers. Since a successful attack on the AGC, not only has a direct impact on the system frequency, but can also affect the stability and economic performance of the power system. Therefore, understanding the impact of cyber attacks on AGC and developing strategies to defend against them have necessity and research importance. In most of the research in the field of attack-defense of AGC, the limitations of AGC in modeling such as governor dead band and communication network transmission delay have been ignored. On the other hand, considering two cyber attacks on the AGC and proposing a way to defend against them simultaneously, have not been considered. In this paper, while using the improved AGC model including governor dead band and communication network transmission delay, the effect of two attacks-data injection attack (FDI) and delay attack which are the most important cyber attacks on AGC-has been investigated. Also, the simultaneous effect of these two attacks is discussed as a combined cyber attack. The Kalman filter-based three-step defense method has been proposed to detect, estimate and mitigate the impact of the attacks and its effectiveness has been tested on the two-area AGC system.

Fuzzers can reveal vulnerabilities in the software by generating test input data and feeding inputs to software under test. The approach of grammar-based fuzzers is to search in the domain of test data which can be generated by grammar in order to find an attack vector with the ability to exploit the vulnerability. The challenge of fuzzers is a very large or infinite search domain and finding the answer in this domain is a hard problem. Grammatical Evolution(GE) is one of the evolutionary algorithms that can utilize grammar to solve the search problem. In this research, a new approach for generation of fuzz test input data by using grammatical evolution is introduced to exploit the cross-site scripting vulnerabilities. For this purpose, a grammar for generating of XSS attack vectors is presented and a fitness calculation function is proposed to guide the GE in search for exploitation. This method has realized the Automatic exploitation of vulnerability with black-box approach. In the results of this research, 19% improvement achieved in the number of vulnerabilities discovered compared to the white-box method of NAVEX and black-box ZAP tool, and without any false positives.

Considering ongoing developments in Photogrammetry and Remote Sensing and attending to their applications such as digital true orthophoto generation from high resolution images, have already proven the urgency and necessity of approaches for generating DEM as input of these applications. In this research we describe a novel method for Automatic DEM generation. We describe “Semi-Global Matching” algorithm then implement and test it for generating an initial disparity image from high resolution stereo satellite images. Furthermore, post-processing steps for removing outliers, recovering from specific problems of structured environments and the interpolation of gaps are needed, to generate an accurate disparity map. With generating a clear disparity map we can generate 3d point clouds. Then these point clouds are used for generating a precise DEM for other applications.

Fuzzing is a dynamic software testing technique. In this technique with repeated generation and injection of malformed test data to the software under test (SUT), we are looking for the possible errors and vulnerabilities. Files are significant inputs to most real-world applications. Many of test data which are generated for fuzzing such programs are rejected by the parser because they are not in the acceptable format and this results in a low code coverage in the process of fuzz testing. Using the grammatical structure of input files to generate test data leads to increase code coverage. However, often, the grammar extraction is performed manually, which is a time consuming, costly and error-prone task. In this paper, a new method, based on deep neural language models (NLMs), is proposed for Automatically learning the file structure and then generating and fuzzing test data. Our experiments demonstrate that the data produced by this method leads to an increase in the code coverage compared to previous test data generation methods. For MuPDF software, which accepts the PDF complex file format as an input, we have more than 1. 30 to 12 improvement in percent code coverage than both the intelligence and random methods.

Independent object test is one of the important steps in the object-oriented software test. This kind of test is faced with two problems: firstly, the object which is under test may call methods of other objects and therefore, the independent test of the object becomes impossible. Secondly, the called methods may be time-consuming and as a result the test of the object takes a long time. In order to overcome these problems, a useful method is to use the faked object which simulates the called methods. Faked objects are usually implemented using a table. This table-based implementation results in different problems such as time-consuming table search operation, and more importantly, inability to exact simulation of called methods. Besides, test samples are rare and therefore Automatic generation of test samples which span all the code paths within a method has become a challenging problem. In this paper, a new artificial neural network-based faked object is proposed which solves the two above-mentioned problems. This paper contains two proposed sections: in the first section, the operation of linear functions which are used in programs is simulated. In the second section, the best set of input parameters which are needed to train the artificial neural network of faked object is determined optimally using genetic algorithm. The superiority of the proposed methods is confirmed using different experiments for mathematical, logical and discrete functions.

Automatic test case generation is an approach to decrease cost and time in software testing. Although there have been lots of proposed methods for Automatic test case generation of web applications, there still exists some challenges which needs more researches. The most important problem in this area is the lack of a complete descriptive model which indicates the whole behaviors of web application as guidance for the generation of test cases with high software coverage. In this paper, test cases are generated Automatically to test web applications using a machine learning method. The proposed method called RTCGW (Rule-based Test Case Generator for Web Applications) generates test cases based on a set of fuzzy rules that try to indicate the whole software behaviors to reach to a high level of software coverage. For this purpose a novel machine learning approach based on fuzzy neural networks is proposed to extract fuzzy rules from a set of data and then used to generate a set of fuzzy rules representing software behaviors. The fuzzy rule set is then used to generate software test cases and the generated test cases are optimized using an optimization algorithm based on combination of genetic and simulated annealing algorithms. Two benchmark problems are tested using the optimized test cases. The results show a high level of coverage and performance for the proposed method in comparison with other methods.