Driven by the increasing scale and high profile cybersecurity incidents related public data, recent years we have witnessed a paradigm shift in understanding and defending against the evolving cyber threats, from primarily reactive detection towards proactive prediction. Meanwhile, governments, businesses, and individual internet users show the growing public appetite to improve cyber resilience that refers to their ability to prepare for, combat and recover from cyber threats and incidents. Undoubtedly, predicting cybersecurity incidents is deemed to have excellent potential for proactively advancing cyber resilience. Research communities and industries have begun proposing cybersecurity incident prediction schemes by utilizing different types of data sources, including organization’s reports and datasets, network data, synthetic data, data crawled from webpages, and data retrieved from social media. With a focus on the dataset, this survey paper investigates the emerging research by reviewing recent representative works appeared in the dominant period. We also extract and summarize the data-driven research methodology commonly adopted in this fast-growing area. In consonance with the phases of the methodology, each work that predicts cybersecurity incident is comprehensively studied. Challenges and future directions in this field are also discussed.

The applications of compressive sensing (CS) in the field of information security have captured a great deal of researchers' attention in the past decade. To supply guidance for researchers from a comprehensive perspective, this paper, for the first time, reviews CS in information security field from two aspects: theoretical security and application security. Moreover, the CS applied in image cipher is one of the most widespread applications, as its characteristics of dimensional reduction and random projection can be utilized and integrated into image cryptosystems, which can achieve simultaneous compression and encryption of an image or multiple images. With respect to this application, the basic framework designs and the corresponding analyses are investigated. Specifically, the investigation proceeds from three aspects, namely, image ciphers based on chaos and CS, image ciphers based on optics and CS, and image ciphers based on chaos, optics, and CS. A total of six frameworks are put forward. Meanwhile, their analyses in terms of security, advantages, disadvantages, and so on are presented. At last, we attempt to indicate some other possible application research topics in future.

The need for fast and strong image cryptosystems motivates researchers to develop new techniques to apply traditional cryptographic primitives in order to exploit the intrinsic features of digital images. One of the most popular and mature technique is the use of complex dynamic phenomena, including chaotic orbits and quantum walks, to generate the required key stream. In this paper, under the assumption of plaintext attacks we investigate the security of a classic diffusion mechanism (and of its variants) used as the core cryptographic primitive in some image cryptosystems based on the aforementioned complex dynamic phenomena. We have theoretically found that regardless of the key schedule process, the data complexity for recovering each element of the equivalent secret key from these diffusion mechanisms is only O(1). The proposed analysis is validated by means of numerical examples. Some additional cryptographic applications of this paper are also discussed.