International Journal Information and Communication Technology Research
Volume:16 Issue: 4, Autumn 2024

In this paper, the combined use of cryptography and information hiding in the image is discussed. First, the RSA method is used to perform encryption information. Secondly, steganography for the encrypted information has been used by genetic evolutionary algorithm (GA). Also, the evolutionary GA is used to find the best bit string length in order to maximize the peak signal-to-noise ratio (PSNR). A graphical user interface (GUI) has been provided to apply the proposed method to sentences of different lengths in four different images to obtain the mean squared error (MSE) and PSNR benchmarks relative to changing the image size and compression rate on the receiving side. In the proposed algorithm, the encryption sentences have been applied 10 times on the data by the proposed algorithm to improve the key strength. The simulation results showed higher efficiency of about 28.57 percent better than normal AES, 15 percent better than DSA, 18.01 percent better than ECC and about 7.76 percent better than RSA.

One of the most important issues in the design of CNN accelerators pertains to the accelerator's ability to effectively leverage the available opportunities in the type and processing of input data, and the task of achieving this objective mostly lies with the dataflow. Equal channel size in the input feature map and filter of CNNs is one of these opportunities, which makes it desirable to design dataflow as Channel Dimension Stationary (CDS). On the other hand, the complexity of designing computations based on the Cartesian product (due to its all-to-all nature) is lower, especially in CDS dataflows. But, since the Cartesian product method causes the generation of useless products and, as a result, reduces performance and energy efficiency, there is less desire for this type of design. This paper presents a frame called FUCA for Cartesian product-based dataflows, which avoids operations leading to useless products. The analysis revealed that FUCA reduces runtime and energy consumption in the Cartesian product-based dataflow by 1.5x, potentially surpassing the sliding window-based dataflow

Due to the large scale of the IoT, cloud computing capabilities such as data storage, management, and analysis are close to the edge of fog network. As the internet becomes more widely used in our business operations through the IoT, the desire for secure and efficient communication also increases. Fog and cloud security is an issue associated with any pattern of data storage, management or processing of data. If a network attack occurs, it has irreversible and destructive effects on the development of the IoT, fog, and cloud computing. Therefore, many security systems or models have been proposed or implemented for fog security reasons. Intrusion detection systems are one of the best options designed using artificial intelligence. In this paper, we present an intrusion detection system for fog security against cyber-attacks. The proposed model uses several machine learning methods designed for the security of fog computing and IoT devices. We used the comprehensive NSLKDD standard dataset for our proposed model. The performance of our model is measured using a variety of common metrics and compared with other methods.

During the last few years, rumor and its rapid diffusion via social media have affected public opinions, even in some important such as presidential elections. One of the main approaches for rumor detection methods is based on content and natural language processing. Despite considerable improvement made in this regard in the English language, unfortunately, we have not witnessed enough progress in the Persian language, mainly due to a lack of datasets in this area. The main novelty of this paper is combining different learning methods to consider the classification problem from different aspects and combine the classifiers’ results to achieve a reasonable final result. In the proposed method, each classifier is assigned a weight depending on its f-measure value; thus, the final fused result is closer to the performance of the best classifier. When news samples have various characteristics, and the best classifier is not predetermined, this fusion method is more beneficial. Therefore, as the conclusion of this research, compared to a single rumor detection method, the fusion of classifiers could be used to achieve better results when the news samples have various characteristics.

Internet domain ranking is one of the important tools for demonstrating a domain popularity level. To evaluate and rank Internet/web domains according to their referrals, popularity, and traffic, domain rankings are extracted in various methods. This study investigates and analyzes these methods and their relevant platforms. For this purpose, the main ranking methods and their inherent characteristics, including similarity, stability, responsiveness, and the degree of benignity (e.g., the low possibility of changing the lists), are examined, and their potential effects on the conclusions are determined. Furthermore, this study specifies the domains ranking indicators used by the main ranking methods. Finally, as the main conclusion of this study, using the Cloudflare radar and combined Tranco ranking are recommended to rank the Internet domains. Moreover, in domains ranking, for each domain of the extracted list, it is necessary to check their IP and Name Server (NS) and delete those that do not have an IP address or their NS are expired.  We have used the multi-criteria decision making (MCDM) methodology to obtain an overall ranking score between different competing ranking scenarios/criteria. Based on the results of this paper, we can conclude that the Cloudflare radar and Tranco with overall ranking efficiency score of 81.8% and 79.9% are the most efficient ranking methodologies based on mixture of different ranking metrics, respectively.

The objective of this study is to construct a framework and provide a comprehensive model that effectively identifies and categorizes the factors influencing digital transformation at the national level, and conceptually determines the relationships among them. To this end, a systematic literature review (meta-synthesis) was conducted, examining 66 studies on digital transformation published between January 2018 and December 2022 across 13 databases. This review resulted in the identification of a framework comprising eight major factors influencing digital transformation. Subsequently, utilizing the resulting conceptual framework and considering Iran's plans and high-level documents such as the Seventh Development Plan, a conceptual model for Iran's digital transformation was designed. This model offers valuable insights for government, organizations, policymakers, and researchers seeking to implement digital transformation strategies.