Biblio

The legacy security defense mechanisms cannot resist where emerging sophisticated threats such as zero-day and malware campaigns have profoundly changed the dimensions of cyber-attacks. Recent studies indicate that cyber threat intelligence plays a crucial role in implementing proactive defense operations. It provides a knowledge-sharing platform that not only increases security awareness and readiness but also enables the collaborative defense to diminish the effectiveness of potential attacks. In this paper, we propose a secure distributed model to facilitate cyber threat intelligence sharing among diverse participants. The proposed model uses blockchain technology to assure tamper-proof record-keeping and smart contracts to guarantee immutable logic. We use an open-source permissioned blockchain platform, Hyperledger Fabric, to implement the blockchain application. We also utilize the flexibility and management capabilities of Software-Defined Networking to be integrated with the proposed sharing platform to enhance defense perspectives against threats in the system. In the end, collaborative DDoS attack mitigation is taken as a case study to demonstrate our approach.

Cloud-hosted applications are prone to targeted attacks such as DDoS, advanced persistent threats, cryptojacking which threaten service availability. Recently, methods for threat information sharing and defense require co-operation and trust between multiple domains/entities. There is a need for mechanisms that establish distributed trust to allow for such a collective defense. In this paper, we present a novel threat intelligence sharing and defense system, namely “DefenseChain”, to allow organizations to have incentive-based and trustworthy co-operation to mitigate the impact of cyber attacks. Our solution approach features a consortium Blockchain platform to obtain threat data and select suitable peers to help with attack detection and mitigation. We propose an economic model for creation and sustenance of the consortium with peers through a reputation estimation scheme that uses `Quality of Detection' and `Quality of Mitigation' metrics. Our evaluation experiments with DefenseChain implementation are performed on an Open Cloud testbed with Hyperledger Composer and in a simulation environment. Our results show that the DefenseChain system overall performs better than state-of-the-art decision making schemes in choosing the most appropriate detector and mitigator peers. In addition, we show that our DefenseChain achieves better performance trade-offs in terms of metrics such as detection time, mitigation time and attack reoccurence rate. Lastly, our validation results demonstrate that our DefenseChain can effectively identify rational/irrational service providers.

With the rapid growth of the cyber attacks, cyber threat intelligence (CTI) sharing becomes essential for providing advance threat notice and enabling timely response to cyber attacks. Our goal in this paper is to develop an approach to extract low-level cyber threat actions from publicly available CTI sources in an automated manner to enable timely defense decision making. Specifically, we innovatively and successfully used the metrics of entropy and mutual information from Information Theory to analyze the text in the cybersecurity domain. Combined with some basic NLP techniques, our framework, called ActionMiner has achieved higher precision and recall than the state-of-the-art Stanford typed dependency parser, which usually works well in general English but not cybersecurity texts.