فصلنامه پدافند الکترونیکی و سایبری
سال پنجم شماره 3 (پیاپی 19، پاییز 1396)

Enhancing accuracy and intelligence of military equipment and systems are of the most important priorities in defense complexes. Data fusion of different sensors for target tracking is a basic requirement in military and civilian domains. Since received data from sensors is most of the time with great ambiguity and therefore causes trouble with decision making for military commanders especially in confrontation with jammers, considering various aspects of target and operating environment can be of great help. In this paper, by using the extended Kalman filter; state vector estimation and minimum MSE matrix will be computed as the central processor entries through each of these sensors and their fusion will be carried out by measurement fusion methods. Afterwards, by application of noise jamming on each of the mentioned methods, the effect of data fusion on the noise jamming condition will be evaluated. Simulation results demonstrate that with designing data fusion system of radar and IR sensors for target tracking in noise disturbance condition, tracking the target can be performed in spite of jamming and it would assist military commanders in decision making.

Automatic detection and tracking of small target in infrared images is the great importance in the modern world. Top-Hat transformation is the important class of nonlinear transformation morphology that has wide usages in the detection and tracking the target in infrared images. The most important problem in improving the efficiency of conversion of Top-Hat transformation, the use of structural elements according to the SNR of each image. Since the images have different clutter and targets, the use of structural elements with fixed shape and dimations for images with different SNR cannot lead to accurate detection. Therefore, to improve the detection, adaptive structural elements should be used. For this purpose, in this paper, we use continuous genetic algorithm to achieve Top-Hat transformation adaptive structural elements, in order to identify more precisely target point. Qualitative and quantitative evaluation results on real images show that the proposed method compared with other methods based on morphology with fixed structural element has a better performance in target detection in infrared image and produce fewer false alarms.

In this paper, a method is proposed for authentication using hand vessels images. This research has three steps: database collection, simulation and verification process. In the database collection step, M hand infrared images have been collected from different people for the main train cases. The images have been processed in the pre-processing, feature extraction and modeling steps and then verified by the SVM classifier. The structure of hand vessels are unique for different people; thus in this bioassay method, hand vessels images have been used for processing and analysis. This assessment showed its high performance in the authentication of 92% and 0% error of assessment on the input samples.

One of the challenging problems in wireless sensor networks is detecting and calculating the area of holes which occur because of several reasons such as accidental death of sensors, explosion in the environment or running out of the energy of the sensors that they all disrupt the task of monitoring wireless sensor networks. When the sensors are distributed randomly in some places like dense forests and rugged terrain, it is possible to detect the holes in the environment. Therefore, in this paper we aim to provide a centralized geometric based algorithm to detect and calculate the area of the holes for a given environment. We investigate hole detection problem for a set of sensors with non-identical sensing radii in both cases: (i) when there is no obstacle in the environment and (ii) when there are some polygonal obstacles in the environment. We propose efficient algorithms for detecting holes in both cases. We calculate the amount of holes and report the boundary edges of the holes using computational geometry approaches. The time complexity of algorithm in case (i) is O(n log2n), and in case (ii) is O(n log2n nm2). The results of the simulation show that the provided algorithms exactly detect all holes in a given environment. Also, we compare the algorithm in case (i) with one of state-of-the-art algorithms in the literature. The comparison result shows efficiency of the proposed algorithm.area.

Today, one of the most important requirements for businesses is providing IT services with respect to high reliability and availability. This will enforce us to try to compute the availability of services and infrastructures with quantitative models precisely. Up to now, there have been many researches about calculating availability in IT services. However, most of them have concentrated on availability of computer and network modules. Some researches were interested in computing availability of a whole network additionally. But none of them paid any attention to computing availability of a business process from the user perspective. In this research, we provide a three-layer model for the overall availability modeling of a business process with respect to its underlying layers in an integrated manner. Then, we present some sort of formulas for computing service availability based on the provided model. This approach is responsible for assessing availability in large and complex networks with a unified measurement model between three layers consisting business, application and infrastructure layers. It will also lead both service providers and users to better and more exact perception of the availability of services, better management of QoS for service providers and better management of investments and budgets for users. The simulation results showed that losing a more important component will have meaningfully more negative impact on process availability than a less one.

Sending and receiving information confidentially has always been very important. The cryptography Plays a major role in exchanging information securely. For this purpose, several encryption algorithms are designed and implemented. Among the designed algorithms, the old RSA and Diffie-Hellman algorithms have been superseded by the elliptic curve encryption algorithm, due to its unique features. The proposed method has higher speed for image encryption and decryption in comparison with the current method. For this purpose, the grouping of pixels by remainder Chinese theorem has been employed. The proposed method is applicable on other data such as text, sound and video.

The role of intrusion detection systems has been considered significant in network anomaly detection. New and unknown attacks have proved that signature-based detection methods are inefficient, and raised the attention to anomaly-based detection methods. Despite their great ability in anomaly detection, these methods suffer from high rate of false-alarms. Therefore, the idea of using hybrid intrusion detection systems is developed in order to reduce the false-alarm rate. In this paper, we propose a four-layered model based on hybrid methods. The first layer consists of data flow analysis and service type classification modules. The service type classifier uses both an n-gram-based statistical technique, and an evolutionary algorithm. In the intrusion detection layer, a signature-based and several anomaly-based detection modules have been implemented with hybrid methods. These specific detection modules are called according to the type of service which has been identified through the first layer. The decision-making layer is then called based on the results of intrusion detection process. This layer identifies the attack nature and the type of response, and then calls the event management layer. In this layer, network administrator is notified appropriately; and, responsive actions are managed if needed. Applying the cross-validation method shows that intrusion detection has been improved and, in result, the false alarm rate has been reduced.

Cyber attackers are able to have a significant impact on the computer networks’ hosts by using DDoS attacks. whereas, defenders use different defensive methods to defend themselves. In such circumstances, it is difficult to determine the network status of the defender (victim). In order to assess the cyber battle scene, it is necessary to evaluate the attacker and defender. The focus of this paper is to provide a framework to assess the status of the victim. In this study, monitoring of the victim is done by using different types of cyber sensors including both technical and human sensors through modeling and simulation. Initially, we review the cyberspace sensors, such as news sites, social networks, reports of the people and technical sensors. The attributes of each sensor are extracted and finally the importance of each feature is evaluated by using the experts’ analytic hierarchy process. Then the combination of attributes for each of the sensors is formed and status of the victim corresponding to the features is determined. The conditions of data fusion using the methods based on fuzzy logic are provided. Implementation of three scenarios show that the proposed method has the desired performance. In the first scenario, in which there was no attack, data fusion sensors have correctly estimated with a probability of 99.3%. In the second scenario, in which the server provides the service slowly and under pressure estimates with probability of 78.6%. In the third scenario, in which the server is under effective attack, data fusion sensors with a probability of 84.2% have estimated correctly. The lack of information about each of the sensors will cause conditions for uncertainty. In this study, we have evaluated 15 different cases. The results show that the proposed method for situation awareness of the host under attack has appropriate evaluation capabilities.