Security and privacy are the inherent problems in RFID communications. There are several protocols have been proposed to overcome those problems. Hash chain is commonly employed by the protocols to improve security and privacy for RFID authentication. Although the protocols able to provide specific solution for RFID security and privacy problems, they fail to provide integrated solution. This article is a survey to closely observe those protocols in terms of its focus and limitations.

The focus of this research is the application of the k-Nearest Neighbor algorithm in terms of classifying botnet attacks in the IoT environment. The kNN algorithm has several advantages in classification tasks, such as simplicity, effectiveness, and robustness. However, it does not perform well in handling large datasets such as the Bot-IoT dataset, which represents a huge amount of data about botnet attacks on IoT networks. Therefore, improving the kNN performance in classifying IoT botnet attacks is the main concern in this study by applying several feature selection techniques. The whole research process was conducted in the Rapidminer environment using three prebuilt feature selection techniques, namely, Information Gain, Forward Selection, and Backward Elimination. After comparing accuracy, precision, recall, F1 score and processing time, the combination of the kNN algorithm and the Forward Selection technique (kNN-FS) achieves the best results among others, with the highest level of accuracy and the fastest execution time among others. Finally, kNN-FS is used in developing SUKRY, which stands for Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi.

Ensuring security of e-government applications and infrastructures is crucial to maintain trust among stakeholders to store, process and exchange information over the e-government systems. Due to dynamic and continuous threats on e-government information security, policy makers need to perform evaluation on existing information security strategy as to deliver trusted e-government services. This paper presents an information security evaluation framework based on new fuzzy multi criteria decision making (MCDM) to help policy makers conduct comprehensive assessment of e-government security strategy.

Changes in technology have resulted in new ways for bankers to deliver their services to costumers. Electronic banking systems in various forms are the evidence of such advancement. However, information security threats also evolving along this trend. This paper proposes the application of analytic hierarchy process (AHP) methodology to guide decision makers in banking industries to deal with information security policy. The model is structured according aspects of information security policy in conjunction with information security elements. We found that cultural aspect is valued on the top priority among other security aspects, while confidentiality is considered as the most important factor in terms of information security elements.

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This paper addresses the use of a specific decision support methodology in operational research termed the analytic hierarchy process (AHP). We examine the application of AHP method in guiding information security policy decision making with respect to Indonesia. We suggest four aspects of information security policy, namely management, technology, economy and culture. In addition, information security components derived from literature review are applied which are confidentiality, integrity and availability. Based on information security policy aspects and information security components we introduce our AHP based model of government information security policy. Our examination of this model shows how AHP can help policy makers to produce appropriate decisions. It is found that AHP shows a robust and encompassing treatment useful for decision makers in both qualitative and quantitative ways. Therefore, it is reasonable to apply AHP for larger scope of research.