The wisdom of the crowd has long become the de facto approach for eliciting information from individuals or experts in order to predict the ground truth. However, classical democratic approaches for aggregating individual \emph{votes} only work when the opinion of the majority of the crowd is relatively accurate. A clever recent approach, \emph{surprisingly popular voting}, elicits additional information from the individuals, namely their \emph{prediction} of other individuals' votes, and provably recovers the ground truth even when experts are in minority. This approach works well when the goal is to pick the correct option from a small list, but when the goal is to recover a true ranking of the alternatives, a direct application of the approach requires eliciting too much information. We explore practical techniques for extending the surprisingly popular algorithm to ranked voting by partial votes and predictions and designing robust aggregation rules. We experimentally demonstrate that even a little prediction information helps surprisingly popular voting outperform classical approaches.

APT, known as Advanced Persistent Threat, is a difficult challenge for cyber defence. These threats make many traditional defences ineffective as the vulnerabilities exploited by these threats are insiders who have access to and are within the network. This paper proposes DeepTaskAPT, a heterogeneous task-tree based deep learning method to construct a baseline model based on sequences of tasks using a Long Short-Term Memory (LSTM) neural network that can be applied across different users to identify anomalous behaviour. Rather than applying the model to sequential log entries directly, as most current approaches do, DeepTaskAPT applies a process tree based task generation method to generate sequential log entries for the deep learning model. To assess the performance of DeepTaskAPT, we use a recently released synthetic dataset, DARPA Operationally Transparent Computing (OpTC) dataset and a real-world dataset, Los Alamos National Laboratory (LANL) dataset. Both of them are composed of host-based data collected from sensors. Our results show that DeepTaskAPT outperforms similar approaches e.g. DeepLog and the DeepTaskAPT baseline model demonstrate its capability to detect malicious traces in various attack scenarios while having high accuracy and low false-positive rates. To the best of knowledge this is the very first attempt of using recently introduced OpTC dataset for cyber threat detection.

This article studies the correspondence retrieval problem: a set of $k$ distinct but unknown points $\mathbf{x}_1, \mathbf{x}_2, \dotsc, \mathbf{x}_k ∈\mathbb{R}^d$ are to be recovered from the unordered collection of projection values $⟨\mathbf{w}_i,\mathbf{x}_1 ⟩, ⟨\mathbf{w}_i,\mathbf{x}_2 ⟩, \dotsc, ⟨\mathbf{w}_i,\mathbf{x}_k ⟩$ onto $n$ known measurement vectors $\mathbf{w}_1, \mathbf{w}_2, \dotsc, \mathbf{w}_n$. Importantly, the correspondence of the $k$ projections ${⟨\mathbf{w}_i,\mathbf{x}_j ⟩}_j=1^k$ across different measurements is unknown. A special case of this problem is the well-studied problem of (real-valued) phase retrieval. In the case of independent standard Gaussian measurement vectors, the main algorithm proposed in this work requires $n = d+1$ measurements to correctly return the $k$ unknown points with high probability. This number of measurements is optimal, and it is smaller than the number of measurements required for a stronger “for all” guarantee even in the phase retrieval setting. The algorithm is based on reductions to the Shortest Vector Problem on certain random lattices, and employs the Lenstra, Lenstra, and Lovász (1982) basis reduction algorithm in a manner similar to the Lagarias & Odlyzko (1985) algorithm for solving random instances of Subset Sum. Another algorithm, essentially due to Yi, Caramanis, & Sanghavi (2016), based on higher-order moments and tensor decompositions is shown to work in a setting where the projection values are corrupted by additive Gaussian noise, but it requires a significantly larger number of measurements.

This article considers algorithmic and statistical aspects of linear regression when the correspondence between the covariates and the responses is unknown. First, a fully polynomial-time approximation scheme is given for the natural least squares optimization problem in any constant dimension. Next, in an average-case and noise-free setting where the responses exactly correspond to a linear function of i.i.d. draws from a standard multivariate normal distribution, an efficient algorithm based on lattice basis reduction is shown to exactly recover the unknown linear function in arbitrary dimension. Finally, lower bounds on the signal-to-noise ratio are established for approximate recovery of the unknown linear function by any estimator.