Forecasting cyberattacks with incomplete,imbalanced,and insignificant data

作     者：Ahmet Okutan Gordon Werner Shanchieh Jay Yang Katie McConky 

作者机构：Computer EngineeringRochester Institute of TechnologyRochesterNYUSA Industrial&Systems EngineeringRochester Institute of TechnologyRochesterNYUSA 

出 版 物：《Cybersecurity》 (网络空间安全科学与技术（英文）)

年 卷 期：2018年第1卷第1期

页      面：263-278页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：Intelligence Advanced Research Projects Activity(IARPA)with contract number FA875016C0114 

主　　题：Cyber security Forecasting Unconventional signals 

摘      要：Having the ability to forecast cyberattacks before they happen will unquestionably change the landscape of cyber warfare and cyber *** work predicts specific types of attacks on a potential victim network before the actual malicious actions take *** challenge to forecasting cyberattacks is to extract relevant and reliable signals to treat sporadic and seemingly random acts of *** paper builds on multi-faceted machine learning solutions and develops an integrated system to transform large volumes of public data to aggregate signals with imputation that are relevant and predictive of cyber incidents.A comprehensive analysis of the individual parts and the integrated whole demonstrates the effectiveness and trade-offs of the proposed *** 16-months of reported cyber incidents by an anonymized victim organization,the integrated approach achieves up to 87%,90%,and 96% AUC for forecasting endpoint-malware,malicious-destination,and malicious-email attacks,*** assessed month-by-month,the proposed approach shows robustness to perform consistently well,achieving F-Measure between 0.6 and *** framework also enables an examination of which unconventional signals are meaningful for cyberattack forecasting.