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False data injection attacks (FDIAs) on smart power grids' measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance.

Probabilistic context-free grammars (PCFGs) have been proposed to capture password distributions, and further been used in password guessing attacks and password strength meters. However, current PCFGs suffer from the limitation of inaccurate segmentation of password, which leads to misestimation of password probability and thus seriously affects their performance. In this paper, we propose a word extraction approach for passwords, and further present an improved PCFG model, called WordPCFG.

We consider the problem of identifying the members of a botnet under an application-layer (L7) DDoS attack, where a target site is flooded with a large number of requests that emulate legitimate users’ patterns. This challenging problem has been recently addressed with reference to two simplified scenarios, where either all bots pick requests from the same emulation dictionary (total overlap), or they are divided in separate clusters corresponding to distinct emulation dictionaries (no overlap at all).

Recently, a hybrid analog-digital architecture has been proposed for multiuser MIMO transmission in the millimeter-wave spectrum using reflect-arrays. The architecture exhibits scalability and high energy-efficiency while keeping the transmitter cost-efficient. Inspired by this architecture, we design a secure multiuser hybrid analog-digital precoding scheme. This scheme utilizes the method of regularized least-squares to shape the downlink beamformers, such that the signal received via malicious terminals is effectively suppressed.

Noisy measurements of a physical unclonable function (PUF) are used to store secret keys with reliability, security, privacy, and complexity constraints. A new set of low-complexity and orthogonal transforms with no multiplication is proposed to obtain bit-error probability results significantly better than all methods previously proposed for key binding with PUFs. The uniqueness and security performance of a transform selected from the proposed set is shown to be close to optimal.

Secret key generation refers to the problem of generating a common secret key without revealing any information about it to an eavesdropper. All users observe correlated components of a common source and can further use a rate-limited public channel for discussion which is open to eavesdroppers. This paper studies the Turing computability of the secret key capacity with a single rate-limited public forward transmission. Turing computability provides fundamental performance limits for today’s digital computers.

Wireless communication systems are inherently vulnerable to adversarial attacks since malevolent jammers might jam and disrupt the legitimate transmission intentionally. Accordingly it is of crucial interest for the legitimate users to detect such adversarial attacks. This paper develops a detection framework based on Turing machines and studies the detectability of adversarial attacks. Of particular interest are so-called denial-of-service attacks in which the jammer is able to completely prevent any transmission.