@misc{TorkuraSukmanaChengetal.2017, author = {Torkura, Kennedy A. and Sukmana, Muhammad Ihsan Haikal and Cheng, Feng and Meinel, Christoph}, title = {Leveraging cloud native design patterns for security-as-a-service applications}, series = {IEEE International Conference on Smart Cloud (SmartCloud)}, journal = {IEEE International Conference on Smart Cloud (SmartCloud)}, publisher = {Institute of Electrical and Electronics Engineers}, address = {New York}, isbn = {978-1-5386-3684-8}, doi = {10.1109/SmartCloud.2017.21}, pages = {90 -- 97}, year = {2017}, abstract = {This paper discusses a new approach for designing and deploying Security-as-a-Service (SecaaS) applications using cloud native design patterns. Current SecaaS approaches do not efficiently handle the increasing threats to computer systems and applications. For example, requests for security assessments drastically increase after a high-risk security vulnerability is disclosed. In such scenarios, SecaaS applications are unable to dynamically scale to serve requests. A root cause of this challenge is employment of architectures not specifically fitted to cloud environments. Cloud native design patterns resolve this challenge by enabling certain properties e.g. massive scalability and resiliency via the combination of microservice patterns and cloud-focused design patterns. However adopting these patterns is a complex process, during which several security issues are introduced. In this work, we investigate these security issues, we redesign and deploy a monolithic SecaaS application using cloud native design patterns while considering appropriate, layered security counter-measures i.e. at the application and cloud networking layer. Our prototype implementation out-performs traditional, monolithic applications with an average Scanner Time of 6 minutes, without compromising security. Our approach can be employed for designing secure, scalable and performant SecaaS applications that effectively handle unexpected increase in security assessment requests.}, language = {en} } @misc{TorkuraSukmanaMeinigetal.2018, author = {Torkura, Kennedy A. and Sukmana, Muhammad Ihsan Haikal and Meinig, Michael and Kayem, Anne V. D. M. and Cheng, Feng and Meinel, Christoph and Graupner, Hendrik}, title = {Securing cloud storage brokerage systems through threat models}, series = {Proceedings IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA)}, journal = {Proceedings IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA)}, publisher = {IEEE}, address = {New York}, isbn = {978-1-5386-2195-0}, issn = {1550-445X}, doi = {10.1109/AINA.2018.00114}, pages = {759 -- 768}, year = {2018}, abstract = {Cloud storage brokerage is an abstraction aimed at providing value-added services. However, Cloud Service Brokers are challenged by several security issues including enlarged attack surfaces due to integration of disparate components and API interoperability issues. Therefore, appropriate security risk assessment methods are required to identify and evaluate these security issues, and examine the efficiency of countermeasures. A possible approach for satisfying these requirements is employment of threat modeling concepts, which have been successfully applied in traditional paradigms. In this work, we employ threat models including attack trees, attack graphs and Data Flow Diagrams against a Cloud Service Broker (CloudRAID) and analyze these security threats and risks. Furthermore, we propose an innovative technique for combining Common Vulnerability Scoring System (CVSS) and Common Configuration Scoring System (CCSS) base scores in probabilistic attack graphs to cater for configuration-based vulnerabilities which are typically leveraged for attacking cloud storage systems. This approach is necessary since existing schemes do not provide sufficient security metrics, which are imperatives for comprehensive risk assessments. We demonstrate the efficiency of our proposal by devising CCSS base scores for two common attacks against cloud storage: Cloud Storage Enumeration Attack and Cloud Storage Exploitation Attack. These metrics are then used in Attack Graph Metric-based risk assessment. Our experimental evaluation shows that our approach caters for the aforementioned gaps and provides efficient security hardening options. Therefore, our proposals can be employed to improve cloud security.}, language = {en} } @misc{TorkuraSukmanaStraussetal.2018, author = {Torkura, Kennedy A. and Sukmana, Muhammad Ihsan Haikal and Strauss, Tim and Graupner, Hendrik and Cheng, Feng and Meinel, Christoph}, title = {CSBAuditor}, series = {17th International Symposium on Network Computing and Applications (NCA)}, journal = {17th International Symposium on Network Computing and Applications (NCA)}, publisher = {IEEE}, address = {New York}, isbn = {978-1-5386-7659-2}, doi = {10.1109/NCA.2018.8548329}, pages = {10}, year = {2018}, abstract = {Cloud Storage Brokers (CSB) provide seamless and concurrent access to multiple Cloud Storage Services (CSS) while abstracting cloud complexities from end-users. However, this multi-cloud strategy faces several security challenges including enlarged attack surfaces, malicious insider threats, security complexities due to integration of disparate components and API interoperability issues. Novel security approaches are imperative to tackle these security issues. Therefore, this paper proposes CSBAuditor, a novel cloud security system that continuously audits CSB resources, to detect malicious activities and unauthorized changes e.g. bucket policy misconfigurations, and remediates these anomalies. The cloud state is maintained via a continuous snapshotting mechanism thereby ensuring fault tolerance. We adopt the principles of chaos engineering by integrating Broker Monkey, a component that continuously injects failure into our reference CSB system, Cloud RAID. Hence, CSBAuditor is continuously tested for efficiency i.e. its ability to detect the changes injected by Broker Monkey. CSBAuditor employs security metrics for risk analysis by computing severity scores for detected vulnerabilities using the Common Configuration Scoring System, thereby overcoming the limitation of insufficient security metrics in existing cloud auditing schemes. CSBAuditor has been tested using various strategies including chaos engineering failure injection strategies. Our experimental evaluation validates the efficiency of our approach against the aforementioned security issues with a detection and recovery rate of over 96 \%.}, language = {en} }