فصلنامه پدافند الکترونیکی و سایبری
سال هشتم شماره 3 (پیاپی 31، پاییز 1399)

Nowadays, social networks have become a strong tool among researchers in addition to their social functions. This tool has many applications in identifying crimes, criminals and terrorists, solving epidemic problems, successful marketing and other topics in various fields. The researchers are using the influence maximization (IM) to achieve these goals. The task of maximization is to identify the influential nodes that are known as the seed nodes. It is a  strategy to achieve the maximum information diffusion or minimum epidemy with minimal cost. Since maximization is an NP-hard problem, researchers are looking for ways to reduce the complexity and acceptable identification accuracy by identifying influential nodes. Therefore, to overcome the complexity and increase the identification  accuracy, in this research a new method with activity-centrality combination is proposed. In this approach, to extract nodes by the centrality method a total constraint is constructed on the network graph in order to proceed to the local nodes extracted from the node activity analysis. The results of analyzing the activity of each node are combined with its    centrality method score which ultimately leads to the identification of influential nodes. The proposed method is compared with other methods such as PageRank and Closeness Centrality methods, and the evaluation results show that whilst having a lower complexity, the proposed method is better than both in terms of accuracy. In the future, the concepts of repetitive scoring can be used to further enhance the accuracy of the activity analysis phase.

Regarding the general acceptance of the web, analyzing its weaknesses and vulnerabilities in order to find and face security attacks has become more urgent. In case there is a communication contrary to the system security policies, a covert channel has been created. The attacker can easily disclose information from the victim’s system with just one public access permission. Covert timing channels, unlike covert storage channels, do not have memory storage and draw less attention. Different methods have been proposed for their identification, which generally benefit from the shape of traffic and the channel’s regularity. The applicative nature of HTTP protocol allows the creation of a covert timing channel based on different features (or different levels) of this protocol, which has not been  addressed in previous researches. In this article, the entropy-based detection method was designed and implemented. The attacker can adjust the amount of channel entropy by controlling measures such as changing the channel’s level or creating noise on the channel to hide from the analyst’s detection. As a result, the entropy threshold is not always constant for detection. By comparing the entropy from different levels of the channel and the analyst, we concluded that the analyst must investigate the traffic at all possible levels. We also illustrated that by making noise on the covert channel, although its capacity would decrease, but as the entropy has increased, the attacker would have more difficulty in its detection.

This paper presents a new method for determining the middle band of discrete cosine transform based on the combination of genetic algorithm and tabu search. As embedded watermark is visible in low frequencies, and not resistant against attacks in high frequencies, the most suitable band for embedding is the middle band. In the Fourier domain, it can be determined by some filters but such a method does not exist in the discrete cosine domain. In discrete cosine transform the arrangement of frequency components does not have the orderliness of the Fourier transform itself, so, it is tried to find the middle band of image by Fourier transform and then find the exact location of the  middle band of discrete cosine transform by genetic algorithm and tabu search. In this method, we minimize the difference between the image that is obtained by applying the middle band of discrete cosine transform mask and the image obtained by the Fourier transform mask. Also, the proposed method can be used for low and high band of discrete cosine transform masks. The implementation and tests for low, middle and high bands resulted in 0.0062, 0.0149 and 0.0061 averages errors, respectively. These results for the test images are satisfactory.

Cognitive sensors, as the main part of cognitive radio systems, are the instruments which determine the spectral cavity, and thus provide optimal use of the bandwidth and prevent interference between permissible users. For reasons such as environmental noise effects, low levels of the signal, fading and multi-path phenomena, and receiver  sensitivity, the functionality of these sensors encounters many problems. In this paper, by first applying the  multi-antenna method in the receiver to obtain environmental signals and then applying the energy detector method, the detection threshold is adaptively determined with the CFAR method and the initial measurements of the environmental spectrum are achieved. The range of the spectrum where the signal is not detected is entered into the final step for decision making. In this stage, the final measurement of the spectrum is performed blindly and   non-cooperatively by finding specific values of the signal covariance matrix by the MME method, to increase the reliability in decision making and also to increase the likelihood of correct detection of the spectral cavity, in addition to preventing interference between authorized users. Simulation results show the probability of detection in the -25dB environmental SNR to be 75 %, which has improved by 15 dB compared to the benchmarks. After hardware  implementation, the simulation results are compared with the results obtained by experimental tests in the real environment.

In this paper, a novel method for signal detection and blind estimation of the length of spreading sequences, in direct sequence code division multiple access (DS/CDMA) systems with different short and long code modes, is presented. The spreading sequences are the basis of the spread spectrum systems which is mainly modeled as a quasi-random process, and their identification and estimation is the most important part of the structure of a spread spectrum receiver and the present article addresses this important issue. The basis of the proposed method of code length estimation is the information theory learning (ITL), and two of its major categories namely entropy criterion and mutual information criterion, which are used in modeling stochastic processes. The proposed method is a kind of generalized entropy and because it includes the integral of the power of probability density function, it is easier to estimate than the Shannon entropy and in large scale implementations it shows better performance. Besides, the proposed method does not need the basic information and that is why it is used in the design of blind receivers. Simulations and numerical results show the validity of our claim in the ability of the presented method in the estimation of the length of spreading sequences in both short and long coding modes.

Increasing the popularity and profitableness of cloud computing is dependent on providing the capabilities and features that the users desire. Elasticity is one of the strongest features that distinguish the cloud computing domain from other distributed system approaches. Cloud computing takes into account an unlimited capacity of the resources for the consumer, and the consumer can take the resources in demand based on competitive rates and increase or  decrease the number of resources. There have been many improvements to elasticity management by previous   researches. However, further researches are necessary to manage elasticity more efficiently. In this paper, an  approach for improving elasticity is presented using the fuzzy control system based on threshold changes for high-performance computing applications in cloud computing. In the proposed approach, elasticity management is based on continuous monitoring and decision making. The results indicate that the proposed approach has a better performance in terms of response time, cost and service level agreement (SLA) violation, compared to previous      studies. In comparison with each of the two specified approaches, the response time of the proposed method has decreased by 6.5% and 9%, cost by 6% and 12%, and service level agreement (SLA) violation by 68% and 77.5%, respectively.

Large-scale data may consist of big, distributed, scattered, heterogeneous, irrelevant, misleading, real, and unrealistic data or any combination of them. Therefore, analyzing, creating value and data productivity is always an important and open challenge. Therefore, the purpose of this study is to present a new coalition architecture for generating valuable information for decision making among the masses of data. The proposed architecture, abbreviated ASMLDE, aims to develop and improve data mining and semantic exploration, and to produce useful and high-quality rules consisting of four layers, seven components and six key elements. In the proposed architecture, conceptualization with 4v's process, insight into the volume and scale of data in the form of 3v's model and finally qualitative insight based on data thickness, are used for conceptualization and standardization of qualitative processes and more complex interpretations. This architecture, supported by ontology and agent mining, reduces large search spaces and increases the speed and quality of data mining operations due to the use of multi-agent systems. Automating exploration operations, reducing data complexity and business processes are also important achievements of the proposed architecture. To evaluate the proposed architecture, a large-scale dataset of natural disasters and earthquake ontology classes from the DBpedia knowledge base have been used. The evaluation results obtained by exploring the semantic rules of the mentioned dataset highlight the effectiveness and capabilities of the ASMLDE  architecture in enhancing the quality of the semantic rules explored to fit the user need and reducing the large data mining space over other similar frameworks and architectures.

In this paper, we present two verifiable multi-secret sharing schemes, including a linear multi-secret sharing scheme with public access structure and a threshold (t, n) scheme based on the learning with errors (LWE) problem. The first scheme is a linear multi-secret sharing scheme in which a number of secrets is distributed by a dealer among a set of participants according to the access structure corresponding to each secret. This scheme has the advantages of the earlier ones and it also has many practical applications    compared to previous schemes including a multi-use verifiable multi-secret sharing scheme in which the secret reconstruction is according to the dealer’s preassigned order. In addition, the security of the scheme has been proven in the standard model. This scheme is based on the hard problems in number theory and therefore is not secure against quantum attacks. The second scheme presented in this paper is a lattice-based secret sharing scheme. In this scheme, which is a threshold (t, n) multi-secret sharing scheme, the presence of at least t participants is required for the reconstruction of the secret. The security of this scheme is based on the difficulty of the LWE problem and so it is resistant against quantum attacks.

This paper investigates joint user association, sub-channel assignment, and power allocation in the uplink of multi-user orthogonal frequency division multiple access (OFDMA) of a heterogeneous network (HetNet) where users are subject to maximum transmission power constraints. It is shown that the  underlying problem is a highly non-convex mixed integer non-linear problem. To tackle the problem, the integer variables are relaxed and a penalty function is added to the objective function to make sure that the relaxed variables take binary values. Finally, the objective function is rewritten as the difference of two convex (D.C.) functions and the resulting problem is addressed through using the successive convex  approximation method. It is worth mentioning that, to the best of the author’s knowledge, the problem of joint user association, sub-channel allocation, and power control in the uplink of a heterogeneous network has not been addressed in the literature till now. Simulation results demonstrate the superiority of the proposed method over existing works in terms of achieving higher throughput despite exhibiting a higher complexity.

Transmission of information through indoor optical telecommunication systems is one of the safe methods against eavesdropping due to the non-emission of radio waves. This method is one of the   jamming-resistant techniques for military applications. In this paper, a hybrid algorithm using OFDM-CDMA based on the multiple access technique is presented. This algorithm improves the efficiency of   visible light communications (VLC) and increases the number of indoor users. The simulation results show that the BER parameter for 4 users improves from 0.035 to 0.0006 when the SNR is 0dB. Also, the bit  transmission rate for 4 users increases from 1 MB/s to 5 MB/s for SNR= 30 dB. Besides, the number of users can increase from 4 users to 20 users for the same bit rate and BER by using the proposed hybrid algorithm. These improvements were made in exchange for increasing the complexity of the system due to the wide spectrum.

In this paper, an image encryption algorithm is proposed based on the Sn permutation group and chaotic functions. The proposed algorithm consists of three steps. In the first step, by applying the hash functions to the plain image information and using the 256-bit external key, a 256-bit secret key is extracted and used to calculate the initial values and parameters of the chaotic functions. In the second step known as the  confusion step, the pixel positions of the plain image are rearranged using a row and column level permutation based on the chaotic functions, such that the correlation between adjacent pixels of the plain image is significantly reduced.  In the third step or the diffusion step, the gray value of each pixel is changed based on a bit level permutation using the S8 permutation group and the chaotic functions.       Finally, by applying the bit level transform using the S8Sbox and XOR operation, the security of the  proposed image encryption algorithm is increased. The experimental results and security analysis show that the NPCR is , the UACI is , entropy is  and the correlation coefficients of the encrypted images are close to 0. Also, the proposed image encryption algorithm has high resistance against common attacks such as the exhaustive search, cropping and noise attacks.

Database outsourcing in cloud computing is one of the main solutions to maintain and access a database easily without the need for large infrastructure investment. Although data owners usually trust service  providers and infrastructure providers in terms of maintainability and accessibility, but they are concerned about the privacy and confidentiality of information, and therefore prefer to keep data encrypted on cloud servers. Encrypted data is not searchable, and a solution needs to be provided by the server to search for that data. One solution is to use keyword indexing as metadata alongside the encrypted database. There are several key challenges to using these solutions: high volume of indexes, user error exposure and search speed. In this study, fuzzy keyword sets are used instead of fixed words when searching for users, and in order to reduce storage space using the keyword clustering method, appropriate fuzzy keyword sets are  selected and metadata with less production volume is used and encrypted. Using hierarchical clustering methods with specific metrics, the same keywords are placed in a cluster, and to find the desired keyword, it is not necessary to search all metadata and thus the search time is reduced. Practical results and evaluations show that the proposed method is practical, safe and efficient.

In this paper we specify the class of integral circulant graphs ICG(n;D), which can be characterized by their order n and the set D of positive divisors of n in such a way that they have the vertex set Zn and the edge set This group of graphs is called BMM graphs because of the form of its set of edges. A bipartite G graph is a graph whose vertex set can be divided into two subsets X, and Y such that no two vertices in X and no two vertices in Y are adjacent. The duplicate graph is called complete if each vertex in X is connected to all vertices in Y. This graph is  represented by K_ (m, n), if | X | = m and | Y | = n. Multipartite graphs are also defined as bipartite graphs.