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2021-05-05
Rizvi, Syed R, Lubawy, Andrew, Rattz, John, Cherry, Andrew, Killough, Brian, Gowda, Sanjay.  2020.  A Novel Architecture of Jupyterhub on Amazon Elastic Kubernetes Service for Open Data Cube Sandbox. IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. :3387—3390.

The Open Data Cube (ODC) initiative, with support from the Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has developed a state-of-the-art suite of software tools and products to facilitate the analysis of Earth Observation data. This paper presents a short summary of our novel architecture approach in a project related to the Open Data Cube (ODC) community that provides users with their own ODC sandbox environment. Users can have a sandbox environment all to themselves for the purpose of running Jupyter notebooks that leverage the ODC. This novel architecture layout will remove the necessity of hosting multiple users on a single Jupyter notebook server and provides better management tooling for handling resource usage. In this new layout each user will have their own credentials which will give them access to a personal Jupyter notebook server with access to a fully deployed ODC environment enabling exploration of solutions to problems that can be supported by Earth observation data.

2020-05-11
Kanimozhi, V., Jacob, T. Prem.  2019.  Artificial Intelligence based Network Intrusion Detection with Hyper-Parameter Optimization Tuning on the Realistic Cyber Dataset CSE-CIC-IDS2018 using Cloud Computing. 2019 International Conference on Communication and Signal Processing (ICCSP). :0033–0036.

One of the latest emerging technologies is artificial intelligence, which makes the machine mimic human behavior. The most important component used to detect cyber attacks or malicious activities is the Intrusion Detection System (IDS). Artificial intelligence plays a vital role in detecting intrusions and widely considered as the better way in adapting and building IDS. In trendy days, artificial intelligence algorithms are rising as a brand new computing technique which will be applied to actual time issues. In modern days, neural network algorithms are emerging as a new artificial intelligence technique that can be applied to real-time problems. The proposed system is to detect a classification of botnet attack which poses a serious threat to financial sectors and banking services. The proposed system is created by applying artificial intelligence on a realistic cyber defense dataset (CSE-CIC-IDS2018), the very latest Intrusion Detection Dataset created in 2018 by Canadian Institute for Cybersecurity (CIC) on AWS (Amazon Web Services). The proposed system of Artificial Neural Networks provides an outstanding performance of Accuracy score is 99.97% and an average area under ROC (Receiver Operator Characteristic) curve is 0.999 and an average False Positive rate is a mere value of 0.001. The proposed system using artificial intelligence of botnet attack detection is powerful, more accurate and precise. The novel proposed system can be implemented in n machines to conventional network traffic analysis, cyber-physical system traffic data and also to the real-time network traffic analysis.

2020-02-24
Snyder, Bradley Lee, Jones, James H..  2019.  Determining the Effectiveness of Data Remanence Prevention in the AWS Cloud. 2019 7th International Symposium on Digital Forensics and Security (ISDFS). :1–6.
Previous efforts to detect cross-instance cloud remanence have consisted of searching current instance unallocated space for fragments easily attributable to a prior user or instance, and results were necessarily dependent on the specific instances tested and the search terms employed by the investigator. In contrast, this work developed, tested, and applied a general method to detect potential cross-instance cloud remanence that does not depend on specific instances or search terms. This method collects unallocated space from multiple cloud virtual machine instances based on the same cloud provider template. Empty sectors and sectors which also appear in the allocated space of that instance are removed from the candidate remanence list, and the remaining sectors are compared to sectors from instances based on other templates from that same provider; a matching sector indicate potential cross-instance remanence. Matching sectors are further evaluated by considering contiguous sectors and mapping back to the source file from the other instance template, providing additional evidence that the recovered fragments may in fact be content from another instance. This work first found that unallocated space from multiple cloud instances based on the same template is not empty, random, nor identical - in itself an indicator of possible cross-instance remanence. This work also found sectors in unallocated space of multiple instances that matched contiguous portions of files from instances created from other templates, providing a focused area for determining whether cross-instance data remanence exists. This work contributes a general method to indicate potential cross-instance cloud data remanence which is not dependent on a specific provider or infrastructure, instance details, or the presence of specific user-attributable remnant fragments. A tool to implement the method was developed, validated, and then run on Amazon's AWS cloud service.
2019-01-31
Zheng, Erkang, Gates-Idem, Phil, Lavin, Matt.  2018.  Building a Virtually Air-Gapped Secure Environment in AWS: With Principles of Devops Security Program and Secure Software Delivery. Proceedings of the 5th Annual Symposium and Bootcamp on Hot Topics in the Science of Security. :11:1–11:8.

This paper presents the development and configuration of a virtually air-gapped cloud environment in AWS, to secure the production software workloads and patient data (ePHI) and to achieve HIPAA compliance.