Cyber risk and cybersecurity: a systematic review of data availability

被引:95
作者
Cremer, Frank [1 ]
Sheehan, Barry [1 ]
Fortmann, Michael [2 ]
Kia, Arash N. [1 ]
Mullins, Martin [1 ]
Murphy, Finbarr [1 ]
Materne, Stefan [2 ]
机构
[1] Univ Limerick, Limerick, Ireland
[2] TH Koln Univ Appl Sci, Cologne, Germany
关键词
Cyber insurance; Cyber risk; Open data; Systematic review; Cybersecurity; INTRUSION DETECTION SYSTEM; DEEP NEURAL-NETWORK; ATTACK DETECTION; DDOS ATTACKS; DATA BREACH; MALWARE DETECTION; FEATURE-SELECTION; HEALTH-CARE; FRAMEWORK; MACHINE;
D O I
10.1057/s41288-022-00266-6
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
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.
引用
收藏
页码:698 / 736
页数:39
相关论文
共 230 条
[41]   A Dynamic DL-Driven Architecture to Combat Sophisticated Android Malware [J].
Bibi, Iram ;
Akhunzada, Adnan ;
Malik, Jahanzaib ;
Iqbal, Javed ;
Mussaddiq, Arslan ;
Kim, Sungwon .
IEEE ACCESS, 2020, 8 :129600-129612
[42]   Insurability of Cyber Risk: An Empirical Analysis [J].
Biener, Christian ;
Eling, Martin ;
Wirfs, Jan Hendrik .
GENEVA PAPERS ON RISK AND INSURANCE-ISSUES AND PRACTICE, 2015, 40 (01) :131-158
[43]   Identifying and Benchmarking Key Features for Cyber Intrusion Detection: An Ensemble Approach [J].
Binbusayyis, Adel ;
Vaiyapuri, Thavavel .
IEEE ACCESS, 2019, 7 :106495-106513
[44]   Botnet traffic identification using neural networks [J].
Biswas, Rajib ;
Roy, Sambuddha .
MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (16) :24147-24171
[45]   Detecting network cyber-attacks using an integrated statistical approach [J].
Bouyeddou, Benamar ;
Harrou, Fouzi ;
Kadri, Benamar ;
Sun, Ying .
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2021, 24 (02) :1435-1453
[46]   LogoSENSE: A companion HOG based logo detection scheme for phishing web page and E-mail brand recognition [J].
Bozkir, Ahmet Selman ;
Aydos, Murat .
COMPUTERS & SECURITY, 2020, 95
[47]  
Brower D., 2021, FINANC TIMES
[48]   An Unsupervised Method for Detecting Shilling Attacks in Recommender Systems by Mining Item Relationship and Identifying Target Items [J].
Cai, Hongyun ;
Zhang, Fuzhi .
COMPUTER JOURNAL, 2019, 62 (04) :579-597
[49]  
Cebula J.J., 2014, A taxonomy of operational cyber security risks version 2
[50]   Learning to Learn Sequential Network Attacks Using Hidden Markov Models [J].
Chadza, Timothy ;
Kyriakopoulos, Konstantinos G. ;
Lambotharan, Sangarapillai .
IEEE ACCESS, 2020, 8 :134480-134497