Literature Survey

• Research Paper 1
• Research Paper 2
• Research Paper 3
• Research Paper 4
• Research Paper 5
• Research Paper 6
• Research Paper 7
• Research Paper 8
• Research Paper 9
• Research Paper 10

Literature Survey

Research Paper 1

Title: A Framework for eBPF-Based Network Functions in an Era of Microservices
Author: Miano, S., Risso, F., Bernal, M. V., Bertrone, M., and Lu, Y. (2021). A framework for eBPF-based network functions in an era of microservices. IEEE Transactions on Network and Service Management, 18(1), 133-151.
Summary: The paper proposes a framework that leverages eBPF (extended Berkeley Packet Filter) technology to develop and deploy network functions as eBPF programs in microservices environments. The framework consists of components for eBPF program development, deployment, and communication, enabling efficient and scalable implementation of network functions like load balancers and firewalls. Evaluation results demonstrate the framework's ability to achieve high throughput and low latency, comparable or better than traditional kernel-bypass solutions, while offering improved flexibility and agility in provisioning network functions.

Research Paper 2

Title: A Framework for Document-level Cybersecurity Event Extraction from Open Source Data
Author: Luo, N., Du, X., He, Y., Jiang, J., Wang, X., Jiang, Z., and Zhang, K. (2021, May). A framework for document-level cybersecurity event extraction from open source data. In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 422-427). IEEE.
Summary: The paper presents a framework for extracting cybersecurity events from opensource data at the document level. It proposes a deep learning model that performs joint entity recognition and event extraction, capturing both intra- and inter-sentence dependencies. The framework leverages external knowledge bases to enrich the extracted events with contextual information. Experimental results on real-world datasets demonstrate the framework's effectiveness in accurately identifying and characterizing cybersecurity incidents from unstructured text data.

Research Paper 3

Title: Distributed cloud monitoring using Docker as next generation container virtualization technology
Author: Dhakate, S., and Godbole, A. (2015, December). Distributed cloud monitoring using Docker as next generation container virtualization technology. In 2015 Annual IEEE India Conference (INDICON) (pp. 1-5). IEEE.
Summary: This paper proposes a distributed cloud monitoring system that leverages Docker, a next-generation container virtualization technology. The system employs Docker containers to encapsulate monitoring agents, enabling efficient deployment and management of monitoring components across distributed cloud environments. The authors demonstrate the system's ability to monitor cloud resources effectively while minimizing overhead and providing scalability benefits compared to traditional virtualization approaches.

Research Paper 4

Title: AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions
Author: Sarker, I. H., Furhad, M. H., and Nowrozy, R. (2021). Ai-driven cybersecurity: an overview, security intelligence modeling and research directions. SN Computer Science, 2(3), 173.
Summary: This paper provides an overview of leveraging artificial intelligence (AI) for cybersecurity. It explores various AI and machine learning techniques like deep learning, reinforcement learning, and ensemble methods that can be applied to domains such as network security, malware detection, and intrusion prevention. The authors highlight the benefits of AI-driven security solutions, including adaptability, scalability, and proactive threat detection capabilities. The paper also outlines research challenges and future directions for developing robust AI-based cybersecurity systems, such as handling adversarial attacks, dealing with data scarcity, and ensuring model transparency and interpretability.

Research Paper 5

Title: Unpacking strategic behavior in cyberspace: a schema-driven approach
Author: Gomez, M. A., and Whyte, C. (2022). Unpacking strategic behavior in cyberspace: a schema-driven approach. Journal of Cybersecurity, 8(1), tyac005.
Summary: This paper proposes a schema-driven approach to analyze and understand strategic behavior in cyberspace. It introduces a framework that combines cognitive schemas and game theory to model the decision-making processes and interactions between adversaries in cyber conflicts. The authors argue that this approach can provide insights into the motivations, goals, and potential actions of cyber threat actors, enabling more effective cybersecurity strategies and deterrence mechanisms. The framework is illustrated through case studies, demonstrating its applicability in unpacking the complexities of strategic cyber behavior.

Research Paper 6

Title: Developing a UI and Automation Framework for a Cybersecurity Research and Experimentation Environment
Author: Butler, C., Thompson, G., Hsieh, G., Hoppa, M. A., and Nauer, K. S. (2018). Developing a UI and Automation Framework for a Cybersecurity Research and Experimentation Environment. In Proceedings of the International Conference on Security and Management (SAM) (pp. 208-213). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
Summary: The paper describes the development of a user interface (UI) and automation framework for a cybersecurity research and experimentation environment. The framework aims to simplify the process of configuring and deploying cybersecurity Here is the continued document in MDX format:

Research Paper 7

Title: Extracting rich semantic information about cybersecurity events
Author: Satyapanich, T., Finin, T., and Ferraro, F. (2019, December). Extracting rich semantic information about cybersecurity events. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 5034-5042). IEEE.
Summary: This paper presents an approach for extracting rich semantic information about cybersecurity events from unstructured text data sources. The authors propose a hybrid system that combines machine learning techniques with knowledge-based methods to identify and characterize cybersecurity incidents. Their system utilizes named entity recognition, relation extraction, and event detection models to extract relevant entities, relationships, and event details from text. The extracted information is then represented using semantic web technologies, enabling complex querying and reasoning over cybersecurity event data. Evaluation on real-world datasets demonstrates the system's effectiveness in accurately capturing comprehensive details about cybersecurity incidents from textual reports.

Research Paper 8

Title: An autonomous cybersecurity framework for next-generation digital service chains
Author: Repetto, M., Striccoli, D., Piro, G., Carrega, A., Boggia, G., and Bolla, R. (2021). An autonomous cybersecurity framework for next-generation digital service chains. Journal of Network and Systems Management, 29(4), 37.
Summary: This paper proposes an autonomous cybersecurity framework for securing next-generation digital service chains in 5G and beyond networks. The framework employs machine learning techniques and software-defined networking principles to dynamically deploy and orchestrate virtual security functions based on detected threats and service requirements. It enables proactive and adaptive security management, automating the provisioning of security services while optimizing resource utilization. The authors evaluate the framework's performance, demonstrating its ability to provide effective and efficient cybersecurity protection for complex service chains.

Research Paper 9

Title: Integrating Cybersecurity Into a Big Data Ecosystem
Author: Tall, A. M., Zou, C. C., and Wang, J. (2021, November). Integrating Cybersecurity Into a Big Data Ecosystem. In MILCOM 2021-2021 IEEE Military Communications Conference (MILCOM) (pp. 69-76). IEEE.
Summary: This paper presents an approach to integrate cybersecurity capabilities into a big data ecosystem. The authors propose a framework that leverages big data technologies and machine learning techniques to process and analyze large volumes of security data from various sources. The framework aims to provide real-time threat detection, risk assessment, and incident response capabilities within a unified big data platform, enabling efficient and scalable cybersecurity operations.

Research Paper 10

Title: Web of cybersecurity: Linking, locating, and discovering structured cybersecurity information
Author: Takahashi, T., Panta, B., Kadobayashi, Y., and Nakao, K. (2018). Web of cybersecurity: Linking, locating, and discovering structured cybersecurity information. International Journal of Communication Systems, 31(3), e3470.
Summary: The paper introduces the concept of a "Web of Cybersecurity," a decentralized network for sharing and discovering structured cybersecurity information. The authors propose a linked data approach, where cybersecurity data is represented using semantic web technologies and interconnected through links. This enables efficient discovery, integration, and analysis of cybersecurity information from diverse sources. The paper outlines techniques for linking, locating, and querying cybersecurity data within this web-based ecosystem.