Biblio

Distributed Denial of Services (DDoS) attacks continue to be one of the most challenging threats to the Internet. The intensity and frequency of these attacks are increasing at an alarming rate. Numerous schemes have been proposed to mitigate the impact of DDoS attacks. This paper presents a comprehensive empirical evaluation of Machine Learning (ML)based DDoS detection techniques, to gain better understanding of their performance in different types of environments. To this end, a framework is developed, focusing on different attack scenarios, to investigate the performance of a class of ML-based techniques. The evaluation uses different performance metrics, including the impact of the “Class Imbalance Problem” on ML-based DDoS detection. The results of the comparative analysis show that no one technique outperforms all others in all test cases. Furthermore, the results underscore the need for a method oriented feature selection model to enhance the capabilities of ML-based detection techniques. Finally, the results show that the class imbalance problem significantly impacts performance, underscoring the need to address this problem in order to enhance ML-based DDoS detection capabilities.

For a computing platform that is compliant with the Trusted Platform Module (TPM) standard, direct anonymous attestation (DAA) is an appropriate cryptographic protocol for realizing an anonymous subscription system. This approach takes advantage of a cryptographic key that is securely embedded in the platform's hardware, and enables privacy-preserving authentication of the platform. In all of the existing DAA schemes, the platform suffers from significant computational and communication costs that increase proportionally to the size of the revocation list. This drawback renders the existing schemes to be impractical when the size of the revocation list grows beyond a relatively modest size. In this paper, we propose a novel scheme called Lightweight Anonymous Subscription with Efficient Revocation (LASER) that addresses this very problem. In LASER, the computational and communication costs of the platform's signature are multiple orders of magnitude lower than the prior art. LASER achieves this significant performance improvement by shifting most of the computational and communication costs from the DAA's online procedure (i.e., signature generation) to its offline procedure (i.e., acquisition of keys/credentials). We have conducted a thorough analysis of LASER's performance related features. We have implemented LASER on a laptop with an on-board TPM. To the best of our knowledge, this is the first implementation of a DAA scheme on an actual TPM cryptoprocessor that is compliant with the most recent TPM specification, viz., TPM 2.0.