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. 

/pdf/cyber-risk-and-cybersecurity-a-systematic-review-of-data-1k47k809.pdf

Cyber risk and cybersecurity: a systematic review of data availability

Marine Goal Optimizer Tuned Deep BiLSTM-Based Self-Configuring Intrusion Detection in Cloud

Enhanced security for IoT networks: a hybrid optimized learning model for intrusion classification

This paper proposes a network anomaly detection model of direct batch growing hierarchical self-organizing mapping based on entropy, which facilitates clear topology representation for the asymmetrically-distributed data. Since the entropy-defined parameters dynamically vary with the incident dataset, that is, follow a data-adaptive manner, the proposed model is naturally valid in all cases with various data types. For fine-grained data distinguishing, a resemble entropy parameter is proposed for the first time to our best knowledge. The experimental results validate that the proposed model achieves a more efficient network anomaly detection than the conventional models, especially for real-world applications with unexpected anomaly data updating.

/pdf/entropy-defined-direct-batch-growing-hierarchical-self-5cpo34s9qe.pdf

Entropy-Defined Direct Batch Growing Hierarchical Self-Organizing Mapping for Efficient Network Anomaly Detection

/pdf/cyber-risk-and-cybersecurity-a-systematic-review-of-data-twh9lpjv.pdf

A new evaluation mechanism was proposed to enhance the representation of data topology in the directed batch growth hierarchical self-organizing mapping. In the proposed mechanism, the growth threshold and the correlation worked in a case-sensitive manner through the statistic calculation of the input data. Since the proposed model enabled a more thorough representation of data topology from both the horizontal and the vertical directions, it naturally held great potential in detecting various traffic attacks. Numerical experiments of network intrusion detection were carried out on the datasets of KDD99, Moore and CICIDS2017, where the good performance validated the superiority of the proposed method.

/pdf/direct-batch-growth-hierarchical-self-organizing-mapping-1exrkj59mv.pdf

Direct Batch Growth Hierarchical Self-Organizing Mapping Based on Statistics for Efficient Network Intrusion Detection

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Direct Batch Growth Hierarchical Self-Organizing Mapping Based on Statistics for Efficient Network Intrusion Detection