نشریه فرماندهی و کنترل
سال پنجم شماره 1 (پیاپی 15، بهار 1400)

A conventional air defense network consists of a rigid centralised hierarchical structure that guarantees the convergence of individual operational actions in a critical air defense battle. Although there are sophisticated proposals for developing decentralised air defense networks, they often suffer massive data/information traffic and high risk of operational failure. In this paper, we propose a cognitive model for a meaningful convergent composition of individual actions for the collective realisation of an operational intention. We then propose and explain the structure and software architecture of a cognitive interaction platform to support the interactive realisation of an air defense operation. Such a platform is called a Razmayeshgah-e Araye-i for air defense operation. This platform can support the emergence of various air defense command and control structures and networks in different operational situations. This is achieved by helping the agents in the battlefield with hints for a meaningful distribution and aggregation of their operational actions.

The diverse applications and widespread effects of cyberspace in most military and civilian applications have led to the rapid growth of data, information, knowledge, technology, methods, tools, and cyber systems. One of the important and strategic requirements in the cyber command and control cycle is the capability to extract, process, fusion and analyze data and information from various sources to achieve the desired situational awareness of the cyberspace and cyber operations environment. Considering the various aspects of this issue, the use of soft data, ie data and information from human resources, along with hard data, ie data and information of machine resources, can help in achieving more accurate and reliable recognition and decision. One of the main research topics in this area is designing a conceptual model and appropriate processing framework for analysis and inference based on data and information with the ability of linking various information from different sources with each other and modeling different types of uncertainty in hard and soft data and fusing them.This paper presents an ontology-based approach to the processing and fusion of hard and soft data in cyber command and control, in which various aspects of the issue has been addressed including ontology-based architecture and processing framework of information inference and fusion, the method of representation of uncertainty and reliability in hard and soft data and fusing these data, conversion of beliefs into probabilities to be able to make decisions on the hypotheses under consideration, designing an appropriate ontology model for cyber command and control purposes, and design and implementation of ontology-based logic of inference and fusion. The results of applying the proposed model in a typical scenario of cyber command and control show that it is operational in fusion of hard and soft cyber data. In addition to the ability of inference and fusion, one of the notable features of the proposed approach is its scalability and adaptability to new extensions in the tools and requirements of cyber command and control.

Detection and classification of Low Probability of Interception (LPI) radar signals is one of the most important challenges in electronic warfare (EW), since there are limited methods for identifying these type of signals. In this paper, a radar waveform automatic identification system for detecting and classifying LPI radar is studied, and accordingly we propose a method based on deep learning networks to detect and classify LPI radar waveforms. To this end, the GoogLeNet architecture as one of the well-known convolutional neural networks (CNN) is utilized. We employ the Short Time Fourier Transform (STFT) for time-frequency analysis in order to construct the entry image for proposed method 1,2(improved the GoogLeNet and AlexNet networks) to recognize offline training and online recognition. After the training procedure with the supervised data sets the proposed method 1,2 can detect and classify nine modulation types of LPI radar, including LFM, poly-phase (P1, P2, P3, P4) and poly-time (T1, T2, T3, T4) waveforms. The numerical results for proposed method 1 and method 2, show considerable accuracies up to 98.7% and 80% at the SNR level of -15db respectively , which outperforms the existing methods.

One of the fundamental problems in wireless sensor networks is to provide area coverage for a specific task. This paper addresses the problem of default unknown network coverage by a group of sensor nodes with nonidentical coverage and communication capabilities. The sensors are mobile and detect the area in a distributed manner and provide optimal coverage. First, we model the coverage optimization problem as a repetitive multiplayer game in which a utility function is formulated to consider the quality of the coverage. Then, we propose a distributed payoff-based learning algorithm in which each sensor tries to maximize its utility function by moving to uncovered locations. The simulation results show the performance of the proposed algorithm

Today, not only is the world and the external environment evolving, but also the economies and markets of developing countries. Under these circumstances, many past opportunities are lost and new ones are created. Lack of strategic thinking in senior executives of companies and government organizations in developing countries deprives these organizations of taking advantage of new opportunities. Therefore, equipping senior managers with a strategic approach seems necessary and even vital. On the other hand, in developing countries, for reasons such as privatization and readiness to join the WTO, there is a growing sense of the need to make effective use of strategic thinking. Therefore, fostering strategic thinking in managers as one of the key factors in the effectiveness of the strategic management process, needs more attention. The effectiveness and efficiency of thinking strategies depends on the study and pathology of research in this field. In the present study, in order to strengthen and cultivate strategic thinking, first identify and rank cognitive errors related to the dimensions of strategic thinking in order to plan to cultivate strategic thinking in managers after identifying related errors. With the help of the results of this research, first, the errors that exist in each dimension of strategic thinking were identified and ranked, the results of which are presented in the present study. The research method was confirmed by the questionnaire of the existence of error, its importance and frequency through the opinions of experts and eight errors by the dimensions of strategic thinking by experts. The results of this study showed that cognitive errors related to the dimensions of strategic thinking and to improve strategic thinking in the minds of managers, first necessary measures should be taken to reduce the cognitive errors of each manager, then strengthen strategic thinking in this area. The article identified and ranked high priority errors.

Despite all the efforts of security experts to detect SQL injection attacks, according to OWASP  report’s, SQL injection attack is still used as the most important cyber attack by attackers. In order to detect attacks, two methods are used: signature-based and behavior-based. Signature-based methods are used for known attacks, and behavior-based methods are suitable for detecting unknown attacks. Behavior-based intrusion detection systems are more useful because attacks are implemented in different ways. Behavior can be analyzed by methods such as classification, clustering, etc. One of the most important classification algorithms is the random forest algorithm which has high accuracy and on the other hand the implementation and interpretation of the results can be done easily using this algorithm. According to the studies, the accuracy of the random forest algorithm is highly dependent on its input parameters. These parameters include 9 items, including the number of trees, their depth, voting method, information gain, and so on. Optimal determination of these parameters is an optimization problem with large state space. In this research, a method based on genetic algorithm to determine the optimal values of these parameters is presented. Due to the optimal determination of the parameters, the obtained results show an improvement in the detection accuracy compared to the default state of the algorithm and other researches. The evaluation results indicate that the intrusion detection accuracy in the proposed method was %98, which is about %11 higher than the random forest algorithm with default parameters and %08 higher than previous studies.