针对隐遁勒索病毒攻击威胁性极大以及传统方法对其防御不力的问题，该文提出了一种基于数字疫苗的隐遁勒索病毒攻击动态防御模型。借鉴生物免疫机理，给出了数字疫苗、抗原、抗体及抗体浓度等免疫概念的形式化定义。首先，通过接种数字疫苗（创建诱饵文件和文件夹），使系统生成抵御隐遁勒索病毒攻击的未成熟抗体；其次，通过免疫抗体动态演化机制，生成能抵御隐遁勒索病毒抗原的成熟抗体与记忆抗体；最后，通过在内核层和应用层实施双重动态监控抗体浓度变化，并借助交叉视图法来实时感知隐遁勒索病毒攻击。理论分析与实验结果表明：该模型有效解决了隐遁勒索病毒攻击难以实时检测的问题，且较传统方法性能更高。
Abstract
Ransomware is a type of malware from cryptovirology that threatens to publish the victim's data or permanently block access to it unless a ransom is paid. Stealthy ransomware is a new type of ransomware that tries to evade detection by deleting all hard copies of its files and just residing in a process running in memory. This study uses danger theory for the biological immune system to design a digital vaccine-based dynamic defense model for stealthy ransomware attacks. Formal definitions are given for some immune concepts such as digital vaccine, antigen, antibody and antibody concentration. Vaccinations with digital vaccines (creating bait files and folders) give the system immature antibodies against stealthy ransomware attacks. The system quickly detects stealthy ransomware attacks using dynamic monitoring of the stealthy ransomware attack antigens in both the core and application layers and by monitoring the dynamic evolution of antibodies and changes of the antibody concentration. Analyses and tests show that the model provides effective real-time detection of stealthy ransomware attacks that are more effective than traditional methods.
关键词
数字疫苗 /
免疫危险理论 /
隐遁勒索病毒攻击 /
危险信号 /
抗体浓度
Key words
digital vaccine /
immune danger theory /
stealthy ransomware attacks /
danger signals /
antibody concentration
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Carbon Black. The ransomware economy[R]. https://www.carbonblack.com/wp-content/uploads/2017/10/Carbon-Black-Ransomware-Economy-Report-101117.pdf,2019.
[2] KRUNAL G, VIRAL P. Survey on ransomware:A new era of cyber attack[J]. International Journal of Computer Applications, 2017, 168(3):38-41.
[3] MANSFIELD-DEVINE S. Fileless attacks:Compromising targets without malware[J]. Network Security, 2017, 2017(4):7-11.
[4] AL-RIMY B A S, MAAROF M A, SHAID S Z M. Ransomware threat success factors, taxonomy, and countermeasures:A survey and research directions[J]. Computers & Security, 2018, 74:144-166.
[5] KHARRAZ A, ROBERTSON W, KIRDA E. Protecting against ransomware:A new line of research or restating classic ideas?[J]. IEEE Security & Privacy, 2018, 16(3):103-107.
[6] HAMPTON N, BAIG Z, ZEADALLY S. Ransomware behavioural analysis on windows platforms[J]. Journal of Information Security and Applications, 2018, 40:44-51.
[7] HOMAYOUN S, DEHGHANTANHA A, AHMADZADEH M, et al. Know abnormal, find evil:Frequent pattern mining for ransomware threat hunting and intelligence[J]. IEEE Transactions on Emerging Topics in Computing, 2018. DOI:10.1109/TETC.2017.2756908.
[8] SRINIVASAN C R. Hobby hackers to billion-dollar industry:The evolution of ransomware[J]. Computer Fraud & Security, 2017, 2017(11):7-9.
[9] MCGILL J K. Ransomware:Is your practice protected?[J]. Journal of Clinical Orthodontics:JCO, 2018, 52(4):237-239.
[10] BREWER R. Ransomware attacks:Detection, prevention and cure[J]. Network Security, 2016, 2016(9):5-9.
[11] LI T. Dynamic detection for computer virus based on immune system[J]. Science in China Series F:Information Science, 2008, 51(10):1475-1486.
[12] PENNISI E. Immunology:Teetering on the brink of danger[J]. Science, 1996, 271(5256):1665-1667.
[13] YOSIFOVICH P, RUSSINOVICH M E, SOLOMON D A, et al. Windows internals, Part 1:System architecture, processes, threads, memory management, and more[M]. 7th ed. Hoboken:Microsoft Press, 2017.
[14] PERELSON A S, WEISBUCH G. Immunology for physicists[J]. Review of Modern Physics, 1997, 69(4):1219-1263.
基金
石元泉,教授,E-mail:syuanquan@163.com
PDF(2526 KB)
