Network Intrusion detection systems are essential for the protection of advanced communication networks. Originally, these systems were hard-coded to identify specific signatures, patterns and rule violations; now artificial intelligence and machine learning algorithms provide promising alternatives. However, in the literature, various outdated datasets as well as a plethora of different evaluation metrics are used to prove algorithm efficacy. To enable a global comparison, this study compiles algorithms for different configurations to create common ground and proposes two new evaluation metrics. These metrics, the detection score and the identification score, together reliably present the performance of a network intrusion detection system to allow for practical comparison on a large scale. Additionally, we present a workflow to process raw packet flows into input features for machine learning. This framework quickly implements different algorithms for the various datasets and allows systematic performance comparison between those algorithms. Our experimental results, matching and surpassing the state-of-the-art, indicate the potential of this approach. As raw traffic input features are much easier and cheaper to extract when compared to traditional features, they show promise for application in real-time deep learning-based systems.

Side-channel attacks represent a powerful category of attacks against cryptographic devices. Still, side-channel analysis for lightweight ciphers is much less investigated than for instance for AES. Although intuition may lead to the conclusion that lightweight ciphers are weaker in terms of side-channel resistance, that remains to be confirmed and quantified. In this paper, we consider various side-channel analysis metrics which should provide an insight on the resistance of lightweight ciphers against side-channel attacks. In particular, for the non-profiled scenario we use the theoretical confusion coefficient and empirical optimal distinguisher. Our study considers side-channel attacks on the first, the last, or both rounds simultaneously. Furthermore, we conduct a profiled side-channel analysis using various machine learning attacks to recover 4-bit and 8-bit intermediate states of the cipher. Our results show that the difference between AES and lightweight ciphers is smaller than one would expect, and even find scenarios in which lightweight ciphers may be more resistant. Interestingly, we observe that the studied 4-bit S-boxes have a different side-channel resilience, while the difference in the 8-bit ones is only theoretically present.

This work implements and evaluates the recent complete addition formulae for the prime order elliptic curves of Renes, Costello and Batina on an FPGA platform. We implement three different versions:(1) an unprotected architecture; (2) an architecture protected through coordinate randomization; and (3) an architecture with both coordinate randomization and scalar splitting in place. The evaluation is done through timing analysis and test vector leakage assessment (TVLA). The results show that applying an increasing level of countermeasures leads to an increasing resistance against side-channel attacks. This is the first work looking into side-channel security issues of hardware implementations of the complete formulae.