نشریه فناوری اطلاعات و ارتباطات انتظامی
پیاپی 5 (بهار 1400)

It is not cost-effective to use sophisticated, expensive and heavy navigation systems that consume a lot of power on training aircrafts and UAVs with low flight range. Therefore, there is a need for light navigation systems with low cost and good reliability for such light flying objects. In this paper, a system for positioning and monitoring the flight rout for light training aircraft and UAVs is presented. The system, which is based on the use of GPS receivers, has a very low power consumption (about 1 Watt) and can work with batteries or aircraft power supply. By using this system and analyzing and comparing the data received from GPS with the profile and virtual flight range that is performed by the pilot before the flight, it is possible to prevent the drone from getting lost and/or entering flight-restricted zones. Once the pilot has planned and entered the flight plan, the system is able to alert the pilot during the flight by locating the aircraft online and comparing the flight path with the planned range when the flying object approaches the restricted area. Also, the data related to the movement path is stored inside the memory card and can be read and mapped on the map using the software that has been designed. By designing interface circuits that connect the apparatus to other navigation systems such as the INS, the apparatus can be upgraded for use in other aircrafts.

Automatic border control (ABC) is one of the new methods of border management using intelligent technologies to accelerate and facilitate traffic, as well as to improve security and efficiency, and can viewed as an example of digital transformation for making smart police. In this paper, the ABC systems are reviewed and the benefits and objectives of the deployment and types of these systems are examined, and the relationship of this system with related technologies and types of equipment is described. Since the deployment of the first ABC systems in the last 20 years until the end of February 2018, more than 4,800 electronic gates have been launched in 73 countries. Due to the wide range of technologies in this field, this system is now established in most countries, especially in Europe, North America, and East Asia. In this paper, the ABC programs of about 30 countries are reviewed, which the results show that the ABC system as a technology to facilitate the process of passenger control has had acceptable performance. Most countries, in addition to achieving the goals of facilitating and accelerating the process and providing security, as well as controlling migrants at borders, have taken steps to provide solutions or provide them through companies active in this field. It should be noted that the ABC system is an upgrade or complement to the existing border control process, and security considerations and border control policies should be observed and adapted to existing infrastructure. It is hoped that this research will be a step in the realization of these systems in our country and making our police smarter than before.

Nowadays, acceptable banking service is impossible without e-banking. Recently, several crimes have been executed in the fields of e-banking, which the numbers of these kinds of crimes are increasing rapidly, which have caused customers damages. Subsequently, according to the mentioned issues, the necessity of presenting a comprehensive model in the field of information security in banks has become more important. Also, the old idea, which says the security will only achieve with the help of instruments and technology is not more acceptable. The purpose of this paper is to present a desirable quantitative model for information security in e-banking industry. The current research method is applicable from the purpose aspect and is mixed from the execution aspect. The statistical society in the qualitative study is composed of experts in the field of security and e-banking. In the qualitative part, the statistical society includes 18 experts and in the quantitative part, the statistical society includes 123 experimental experts. The analysis of the data is performed in the two separated parts in this paper. the first part is about qualitative statistics of research data. While, the second part is about inferential statistics of the research. The SPSS and PLS software are used to study in the research data. The study has shown that the seven parameters are mostly effective parameters on the e-banking security which are: OTP, ISOs, culture building, comprehensive education, infrastructures improvement, up to date software, and utilization of experts.

Millimeter waves have unique properties such as the ability to penetrate the fibers of clothing that lead to the detection of hidden objects under clothing. For this reason, the millimeter wave imaging system is used in the public security departments of important centers, especially airports. Under security conditions and in order to increase intelligence for monitoring criminals, detecting hidden objects under clothing with high accuracy and speed will increase police authority. By introducing methods for detecting objects based on deep convolutional neural networks, these methods can be used to meet the needs of high accuracy and speed. In this paper, the YOLOv3 algorithm is used as the base of object detection algorithm to detect targets due to its high accuracy and speed. In order to increasing the mean average precision of the YOLOv3 algorithm and also increasing the accuracy of small object detection, we add the spatial pyramid pooling module in the network structure of the YOLOv3 algorithm and also use the suitable anchor boxes with the purposes within the dataset. The smaller structure of YOLOv3 is also used to reduce computational complexity as well as detection time. Finally, the efficiency of the proposed method is compared with the original YOLOv3 algorithm. The results show that the proposed method, in addition to increasing the mean average precision by 4.04%, also consumes a detection time of 14 milliseconds.

Advances in the technology of UAVs and MAVs have led to an expanse use of these drones which has brought both securing the information and safekeeping of particular areas such as stadiums, facilities, military and law enforcement agencies, that are being considered and managed more than before. Accordingly, defense mechanisms should be sized congenially to the exact type of thread dealing with these threats. The proposed system in this study is the uses only a high-resolution camera that identifies specified targets based on a given database. In recent studies, several methods such as using particle filters, neural networks systems, have been presented. They are reviewed in this work and then, using a combination of the mentioned techniques with a novel laboratory benchmark detector and tracker of the drone is designed and implemented. The main focus of the project is the use of convolutional neural networks, which should maximize the frame rate of the received data from the camera. This project is implemented using three different methods including Darknet Framework, YOLO, and Fast RCNN network. In all cases, framerates are processed. The expected average accuracy in this project is more than 90%. The presented system has been tested by computer but it has the capability of performing on FPGA environment using HLS tools.

We have designed and implemented an Artificial Intelligence (AI) and graph-based planner, working on cybersecurity models and standards. The suggested planner applies on the extracted features in order to introduce a sequence of prioritized cybersecurity controls. These features are independent and comprehensive for making a good security management decision. Implementation of this sequence guarantees that the target organization’s overall cybersecurity maturity rises evenly in all domains. This planner has taken the advantage of three major algorithms: Push-Relabel, Minimum Scoring and Dijkstra. It is implemented with Python programming language. We have used the Cybersecurity Capability Maturity Model (C2M2) as a case study, and the Police organization as a use case. As a result, we have introduced a security roadmaps for the Islamic Republic of Iran Police organization. The suggested plans provide intelligent maturity in cybersecurity. If an organization does these plans in order and well, it saves resources and costs.