In recent years, Smart Farming (SF) and Precision Agriculture (PA) have attracted attention from both the agriculture industry as well as the research community. Altogether, SF and PA aim to help farmers use inputs (such as fertilizers and pesticides) more efficiently through using Internet of Things (IoT) devices, but in doing so, they create new security threats that can defeat this purpose in the absence of adequate awareness and proper countermeasures. A survey on different security-related challenges is required to raise awareness and pave they way for further research in this area. In this paper, we first itemize the security aspects of SF and PA. Next, we review the types of cyber attacks that can violate each of these aspects. Accordingly, we present a taxonomy on cyber-threats to SF and PA on the basis of their relations to different stages of Cyber-Kill Chain (CKC). Among cyber-threats, we choose Advanced Persistent Threats (APTs) for further study. Finally, we studied related risk mitigation strategies and countermeasure, and developed a future road map for further study in this area. This paper’s main contribution is a categorization of security threats within the SF/PA areas and provide a taxonomy of security threats for SF environments so that we may detect the behavior of APT attacks and any other security threat in SF and PA environments.

Recently, Content Delivery Networks (CDN) have become more and more popular. The technology itself is ahead of academic research in this area. Several dimensions of the technology have not been adequately investigated by academia. These dimensions include outline management, security, and standardization. Discovering and highlighting aspects of this technology that may have or have not been covered by academic research is the first step toward helping academia bridge the gap with industry or even go one step further to lead industry in the right direction. This suggests a comprehensive survey on research works in this regard. The literature in this area has already come up with some surveys and taxonomies, but some of them are outdated or do not cover every aspect of CDN while others fail to detect existing trends or to develop a holistic roadmap for research on the technology. Furthermore, none of the existing surveys aim at enlightening the dark aspects of the technology that have not been subject to academic research. In this survey, we first extract the lifecycle of a CDN as suggested by the existing research. Then, we investigate previous relevant works on each phase of the lifecycle to clarify where the research is currently located and headed. We show how CDN technology tends to converge with emerging paradigms such as cloud computing, edge computing, and machine learning, which are more mature in terms of academic research. This helps us determine the right direction for further research by revealing the deficiencies in existing works.

Recently, many researchers have been interested in the application of chaos in cryptography. Specifically, numerous research works have been focusing on chaotic image encryption. A comprehensive survey can highlight existing trends and shed light on less-studied topics in the area of chaotic image encryption. In addition to such a survey, this paper studies the main challenges in this field, establishes an ecosystem for chaotic image encryption, and develops a future roadmap for further research in this area.

After being introduced by Shannon as a measure of disorder and unavailable information, the notion of entropy has found its applications in a broad range of scientific disciplines. In this paper, we present a systematic review on the applications of entropy and related information-theoretical concepts in the design, implementation and evaluation of cryptographic schemes, algorithms, devices and systems. Moreover, we study existing trends, and establish a roadmap for future research in these areas.

Ensemble methods try to improve performance via integrating different kinds of input data, features, or learning algorithms. In addition to other areas, they are finding their applications in cancer prognosis and diagnosis. However, in this area, the research community is lagging behind the technology. A systematic review along with a taxonomy on ensemble methods used in cancer prognosis and diagnosis can pave the way for the research community to keep pace with the technology and even lead trend. In this article, we first present an overview on existing relevant surveys and highlight their shortcomings, which raise the need for a new survey focusing on Ensemble Classifiers (ECs) used for the diagnosis and prognosis of different cancer types. Then, we exhaustively review the existing methods, including the traditional ones as well as those based on deep learning. The review leads to a taxonomy as well as the identification of the best-studied cancer types, the best ensemble methods used for the related purposes, the prevailing input data types, the most common decision-making strategies, and the common evaluating methodologies. Moreover, we establish future directions for researchers interested in following existing research trends or working on less-studied aspects of the area.