DL-Based Physical Tamper Attack Detection in OFDM Systems with Multiple Receiver Antennas: A Performance-Complexity Trade-Off.

This paper proposes two deep-learning (DL)-based approaches to a physical tamper attack detection problem in orthogonal frequency division multiplexing (OFDM) systems with multiple receiver antennas based on channel state information (CSI) estimates. The physical tamper attack is considered as the unwanted change of antenna orientation at the transmitter or receiver. Approaching the tamper attack scenario as a semi-supervised anomaly detection problem, the algorithms are trained solely based on tamper-attack-free measurements, while operating in general scenarios that may include physical tamper attacks. Two major challenges in the algorithm design are environmental changes, e.g., moving persons, that are not due to an attack and evaluating the trade-off between detection performance and complexity. Our experimental results from two different environments, comprising an office and a hall, show the proper detection performances of the proposed methods with different complexity levels. The optimal proposed method achieves a 93.32% true positive rate and a 10% false positive rate with a suitable level of complexity.

Drug combinatorics and side effect estimation on the signed human drug-target network.

BACKGROUND: The mode of action of a drug on its targets can often be classified as being positive (activator, potentiator, agonist, etc.) or negative (inhibitor, blocker, antagonist, etc.). The signed edges of a drug-target network can be used to investigate the combined mechanisms of action of multiple drugs on the ensemble of common targets. RESULTS: In this paper it is shown that for the signed human drug-target network the majority of drug pairs tend to have synergistic effects on the common targets, i.e., drug pairs tend to have modes of action with the same sign on most of the shared targets, especially for the principal pharmacological targets of a drug. Methods are proposed to compute this synergism, as well as to estimate the influence of the drugs on the side effect of another drug. CONCLUSIONS: Enriching a drug-target network with information of functional nature like the sign of the interactions allows to explore in a systematic way a series of network properties of key importance in the context of computational drug combinatorics.