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Nowadays, under the pressure of numerous research publications, researchers increasingly pay attention to writing survey papers to track and understand one research topic they are interested in, and then begin to conduct more in-depth research. Until this moment, there are two types of survey papers: traditional review analysis and bibliometric statistical analysis. Compared with traditional review analysis, due to the analysis of various bibliometric information that can be quickly summarized to assess and predict one research field’s development, the bibliometric statistical analysis is progressively proposed. However, no research relied on the bibliometric approach to explore fuzzy inference system (FIS) -based classifiers. More importantly, since the current open-ended bibliometric analysis approaches have different assessment focuses, choosing a suitable approach is problematic. Therefore, based on the extraction, integration, and expansion of previous bibliometric analysis theories, this research proposes a new systematic and time-saving bibliometric statistical analysis approach. It is worth noting that the proposed approach eliminates the need to read the internal content of all publications. Two core parts (Publication Information and TOP 20 SETs) are generated by the proposed analysis approach. Among them, analyzing Author Keywords and TOP 20 SETs are unprecedented guiding features to assist researchers in exploring research topic. Significantly, this research relies on the proposed approach to explore FIS-based classifiers. Various assessments cover the bibliometric information of the entire related publications. In addition, these results may need to be considered to increase the citation rate of future publications.

The software-defined networking (SDN) paradigm has recently emerged as a trend to build various protocols, develop more reliable networks, enhance the data flow controlling, and provide security in a much simpler and flexible way. SDN helps to ease management and handle asymmetric connectivity across various nodes. It solves the problems of network and cloud security and hence provides the best solution for the safety of data on the network. Therefore, we feel the urge to research more and provide the basics of SDN forensics, mention its advantages in network especially in the cloud, and present its elaborate prospects in context with Network Forensic (NF) and Cloud Forensic (CF). In this research article, we explained in detail the NF and CF with emphasis on Network security (NS) and Cloud Security (CS). The paper also provided the various security approaches and categories. Then, an overview of the software-defined networking (SDN) is mentioned. We also discussed the use of SDN in Network Forensic and Cloud Forensic. Furthermore, to aid the SDN forensic, we presented the advantages, challenges, and issues along with future research directions of SDN in network forensic and cloud forensic, and at last, we thus express and explore the need for security in forensic based on the SDN paradigm in the form of a set of suggested recommendations.

Data replications effectively replicate the same data to various multiple locations to accomplish the objective of zero loss of information in case of failures without any downtown. Dynamic data replication strategies (providing run time location of replicas) in clouds should optimize the key performance indicator parameters, like response time, reliability, availability, scalability, cost, availability, performance, etc. To fulfill these objectives, various state-of-the-art dynamic data replication strategies has been proposed, based on several criteria and reported in the literature along with advantages and disadvantages. This paper provides a quantitative analysis and performance evaluation of target-oriented replication strategies based on target objectives. In this paper, we will try to find out which target objective is most addressed, which are average addressed, and which are least addressed in target-oriented replication strategies. The paper also includes a detailed discussion about the challenges, issues, and future research directions. This comprehensive analysis and performance evaluation based-work will open a new door for researchers in the field of cloud computing and will be helpful for further development of cloud-based dynamic data replication strategies to develop a technique that will address all attributes (Target Objectives) effectively in one replication strategy.

Wireless Sensor Networks (WSNs) are defined as networks of nodes that work in a cooperative way in order to sense and control the surrounding environment. Several WSNs algorithms have been proposed by utilizing the Fuzzy Logic technique to select the cluster heads (CHs). Each technique employs a different combination of input parameters such as nodes density, communication cost, and residual energy. CHs determination is critical towards this goal, whereas the combination of input parameters is expected to play an important role. Nevertheless, the received signal strength (RSSI) is one of the main criteria which get little attention from researchers on the topic of CHs selection. In this study, an RSSI based scheme was proposed which utilizes Fuzzy Logic approach in order to be combined with residual energy and centrality of the fuzzy descriptor. In order to evaluate the proposed scheme, the performance Multi-Tier Protocol (MAP) and Stable Election Protocol (SEP) were compared. The simulation results show that the proposed approach has significantly prolonged the survival time of the network against SEP and MAP, while effectively decelerating the dead process of sensor nodes.

The increasing development of the Internet, more and more applications are put into websites can be directly accessed through the network. This development has attracted an attacker with phishing websites to compromise computer systems. Several solutions have been proposed to detect a phishing attack. However, there still room for improvement to tackle this phishing threat. This paper aims to investigate and evaluate the effectiveness of machine learning approach in the classification of phishing attack. This paper applied a heuristic approach with machine learning classifier to identify phishing attacks noted in the web site applications. The study compares with five classifiers to find the best machine learning classifiers in detecting phishing attacks. In identifying the phishing attacks, it demonstrates that random forest is able to achieve high detection accuracy with true positive rate value of 94.79% using website features. The results indicate that random forest is effective classifiers for detecting phishing attacks.

YouTube has become a popular social media among the users. Due to YouTube popularity, it became a platform for spammer to distribute spam through the comments on YouTube. This has become a concern because spam can lead to phishing attack which the target can be any user that click any malicious link. Spam has its own features that can be analyzed and detected by classification. Hence, enhancement features are proposed to detect YouTube spam. In order to conduct the experiments, a YouTube Spam detection framework that consists of five (5) phases such as data collection, pre-processing, features selection and extraction, classification and detection were developed. This paper, proposed the YouTube detection framework, examined and validate each of the phases by using two types of data mining tool. The features are constructed from analysis by using data collected from YouTube Spam dataset by using Naïve Bayes and Logistic Regression and tested in two different data mining tools which is Weka and Rapid Miner. From the analysis, thirteen (13) features that had been tested on Weka and RapidMiner shows high accuracy, hence is being used throughout the experiment in this research. Result of Naïve Bayes and Logistic Regression run in Weka is slightly higher than RapidMiner. In addition, result of Naïve Bayes is higher than Logistic Regression with 87.21% and 85.29% respectively in Weka. While in RapidMiner there is slightly different of accuracy between Naïve Bayes and Logistic Regression 80.41% and 80.88%. But, precision of Naïve Bayes is higher than Logistic Regression.