Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020, indicating an increase of more than 50% since 2018. With the average cyber insurance claim rising from USD 145,000 in 2019 to USD 359,000 in 2020, there is a growing necessity for better cyber information sources, standardised databases, mandatory reporting and public awareness. This research analyses the extant academic and industry literature on cybersecurity and cyber risk management with a particular focus on data availability. From a preliminary search resulting in 5219 cyber peer-reviewed studies, the application of the systematic methodology resulted in 79 unique datasets. We posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue. In particular, we identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks. The resulting data evaluation and categorisation will support cybersecurity researchers and the insurance industry in their efforts to comprehend, metricise and manage cyber risks.The online version contains supplementary material available at 10.1057/s41288-022-00266-6. 

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Cyber risk and cybersecurity: a systematic review of data availability

Blockchain-based recommender systems: Applications, challenges and future opportunities

Over the past decade, many approaches have been presented to detect shilling attacks in collaborative recommender systems. However, these approaches focus mainly on detecting individual attackers and rarely consider the collusive shilling behaviors among attackers, i.e., a group of attackers working together to bias the output of collaborative recommender systems by injecting fake profiles. Such shilling behaviors are generally termed group shilling attacks, which are more harmful to collaborative recommender systems than traditional shilling attacks. In this paper, we propose a graph embedding-based method to detect group shilling attacks in collaborative recommender systems. First, we construct a user relationship graph by analyzing the user rating behaviors and use a graph embedding method to obtain the low-dimensional vector representation of each node in the user relationship graph. Second, we employ the k-means++ clustering algorithm to obtain candidate groups based on the generated user feature vectors. Finally, we calculate the suspicious degree of each candidate group according to the attack group detection indicators and use the Ward’s hierarchical clustering method to cluster the candidate groups according to their suspicious degrees and obtain the attack groups. The experimental results on the Amazon and Netflix datasets show that the proposed method outperforms the baseline methods in detection performance.

Graph embedding-based approach for detecting group shilling attacks in collaborative recommender systems

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Fuzhi Zhang

Papers

An Unsupervised Method for Detecting Shilling Attacks in Recommender Systems by Mining Item Relationship and Identifying Target Items