Summary As social networks are getting more and more popular day by day, large numbers of users becoming constantly active social network users. In this way, there is a huge amount of data produced by users in social networks. While social networking sites and dynamic applications of these sites are actively used by people, social network analysis is also receiving an increasing interest. Moreover, semantic understanding of text, image, and video shared in a social network has been a significant topic in the network analysis research. To the best of the author's knowledge, there has not been any comprehensive survey of social networks, including semantic analysis. In this survey, we have reviewed over 200 contributions in the field, most of which appeared in recent years. This paper not only aims to provide a comprehensive survey of the research and application of social network analysis based on semantic analysis but also summarizes the state‐of‐the‐art techniques for analyzing social media data. First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. Then, the emerging approaches in social network analysis research, especially in semantic social network analysis, are discussed. Finally, the trending topics and applications for future directions of the research are emphasized; the information on what kind of studies may be realized in this area is given.

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Low-rate distributed denial-of-service (LDDoS) attacks dramatically reduce transmission control protocol throughput by exploiting the vulnerability in the transmission control protocol congestion control mechanism. The current study proposes a new metric called mean Internet Protocol (IP) packet delay variation (mipdv) to detect LDDoS flows and a filtering method called ipdv-based LDDoS filtering (ILF) using mipdv. Receiving first seven packets from a flow is sufficient to calculate the mipdv metric. Subsequently, mipdv can be recalculated for each received packet. This makes the detection of LDDoS flows possible in a short time (in a few tens of milliseconds in most cases). Ns2 simulations were conducted to evaluate the performance of ILF. Experimental results show that ILF detects LDDoS flows in a very short time with very high accuracy.

Detection and filtering of low-rate distributed denial of service (LDDoS) attacks is hard since their behavior is similar to legitimate users' behavior. In the literature, there are many filtering approaches and metrics for LDDoS attacks. However, most of the LDDoS detection methods in the literature only monitor congestion state. Actually, precongestion period that the attack has already started has valuable information about the attack. In this study, we proposed a method that uses precongestion period for metric calculation. Also, most of LDDoS filtering approaches have high false-positive and false-negative rates and also require long period of time for detection. Additionally, we developed an efficient method for detection and filtering of LDDoS attacks. According to the experimental results, the proposed LDDoS detection method has zero falsepositive and false-negative rates under the scenarios; attack detection time is significantly reduced with using the proposed metric calculation approach. Also, the proposed method has a simple logic, and it requires simple calculations. This increases the applicability of the developed method.KEYWORDS distributed denial of service attacks, internet of things, lightweight security, network security

Influence Maximization (IM) aims at finding the most influential users in a social network, that is, users who maximize the spread of an opinion within a certain propagation model. Previous work investigated the correlation between influence spread and nodal centrality measures to bypass more expensive IM simulations. The results were promising but incomplete, since these studies investigated the performance (i.e. the ability to identify influential users) of centrality measures only in restricted settings, for example, in undirected/unweighted networks and/or within a propagation model less common for IM. In this article, we first show that good results within the Susceptible-Infected-Removed propagation model for unweighted and undirected networks do not necessarily transfer to directed or weighted networks under the popular Independent Cascade (IC) propagation model. Then, we identify a set of centrality measures with good performance for weighted and directed networks within the IC model. Our main contribution is a new way to combine the centrality measures in a closed formula to yield even better results. Additionally, we also extend gravitational centrality (GC) with the proposed combined centrality measures. Our experiments on 50 real-world data sets show that our proposed centrality measures outperform well-known centrality measures and the state-of-the art GC measure significantly.