Forgery attacks on ++AE authenticated encryption mode

In this paper, we analyse a block cipher mode of operation submitted in 2014 to the cryptographic competition for authenticated encryption (CAESAR). This mode is designed by Recacha and called ++AE (plus-plus-ae). We propose a chosen plaintext forgery attack on ++AE that requires only a single chosen message query to allow an attacker to construct multiple forged messages. Our attack is deterministic and guaranteed to pass ++AE integrity check. We demonstrate the forgery attack using 128-bit AES as the underlying block cipher. Hence, ++AE is insecure as an authenticated encryption mode of operation.

R1SVM: A randomised nonlinear approach to large-scale anomaly detection

The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and sup-port vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep auto encoder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.