Visible to the public Biblio

Filters: Author is Payne, J.  [Clear All Filters]
2020
Hayes, J. Huffman, Payne, J., Essex, E., Cole, K., Alverson, J., Dekhtyar, A., Fang, D., Bernosky, G..  2020.  Towards Improved Network Security Requirements and Policy: Domain-Specific Completeness Analysis via Topic Modeling. 2020 IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engineering (AIRE). :83—86.

Network security policies contain requirements - including system and software features as well as expected and desired actions of human actors. In this paper, we present a framework for evaluation of textual network security policies as requirements documents to identify areas for improvement. Specifically, our framework concentrates on completeness. We use topic modeling coupled with expert evaluation to learn the complete list of important topics that should be addressed in a network security policy. Using these topics as a checklist, we evaluate (students) a collection of network security policies for completeness, i.e., the level of presence of these topics in the text. We developed three methods for topic recognition to identify missing or poorly addressed topics. We examine network security policies and report the results of our analysis: preliminary success of our approach.

2019
Malvankar, A., Payne, J., Budhraja, K. K., Kundu, A., Chari, S., Mohania, M..  2019.  Malware Containment in Cloud. 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :221—227.

Malware is pervasive and poses serious threats to normal operation of business processes in cloud. Cloud computing environments typically have hundreds of hosts that are connected to each other, often with high risk trust assumptions and/or protection mechanisms that are not difficult to break. Malware often exploits such weaknesses, as its immediate goal is often to spread itself to as many hosts as possible. Detecting this propagation is often difficult to address because the malware may reside in multiple components across the software or hardware stack. In this scenario, it is usually best to contain the malware to the smallest possible number of hosts, and it's also critical for system administration to resolve the issue in a timely manner. Furthermore, resolution often requires that several participants across different organizational teams scramble together to address the intrusion. In this vision paper, we define this problem in detail. We then present our vision of decentralized malware containment and the challenges and issues associated with this vision. The approach of containment involves detection and response using graph analytics coupled with a blockchain framework. We propose the use of a dominance frontier for profile nodes which must be involved in the containment process. Smart contracts are used to obtain consensus amongst the involved parties. The paper presents a basic implementation of this proposal. We have further discussed some open problems related to our vision.

Payne, J., Kundu, A..  2019.  Towards Deep Federated Defenses Against Malware in Cloud Ecosystems. 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :92—100.

In cloud computing environments with many virtual machines, containers, and other systems, an epidemic of malware can be crippling and highly threatening to business processes. In this vision paper, we introduce a hierarchical approach to performing malware detection and analysis using several recent advances in machine learning on graphs, hypergraphs, and natural language. We analyze individual systems and their logs, inspecting and understanding their behavior with attentional sequence models. Given a feature representation of each system's logs using this procedure, we construct an attributed network of the cloud with systems and other components as vertices and propose an analysis of malware with inductive graph and hypergraph learning models. With this foundation, we consider the multicloud case, in which multiple clouds with differing privacy requirements cooperate against the spread of malware, proposing the use of federated learning to perform inference and training while preserving privacy. Finally, we discuss several open problems that remain in defending cloud computing environments against malware related to designing robust ecosystems, identifying cloud-specific optimization problems for response strategy, action spaces for malware containment and eradication, and developing priors and transfer learning tasks for machine learning models in this area.