Faster-than-Nyquist (FTN) is a promising paradigm to improve bandwidth utilization at the expense of additional intersymbol interference (ISI). In this paper, we apply state-of-the-art deep learning (DL) technology into receiver design for FTN signaling and propose two DL-based new architectures. Firstly, we propose an FTN signal detection based on DL and connect it with the successive interference cancellation (SIC) to replace traditional detection algorithms. Simulation results show that this architecture can achieve near-optimal performance in both uncoded and coded scenarios. Additionally, we propose a DL-based joint signal detection and decoding for FTN signaling to replace the complete baseband part in traditional FTN receivers. The performance of this new architecture has also been illustrated by simulation results. Finally, both the proposed DL-based receiver architecture has the robustness to signal to noise ratio (SNR). In a nutshell, DL has been proved to be a powerful tool for the FTN receiver design. INDEX TERMS Faster-than-Nyquist, receiver design, signal detection, deep learning, intersymbol interference, channel coding.
Nowadays, the industrial control system (ICS) plays a vital role in critical infrastructures like the power grid. However, there is an increasing security concern that ICS devices are being vulnerable to malicious users/attackers, where any subtle changing or tampering attack would cause significant damage to industrial manufacturing. In this paper, we propose the iFinger, a novel detection approach designed to mitigate ICS attacks adapting to various industrial scenes. We take advantage of an important insight that industrial protocol packets include register status values that are used to reflect the physical characteristics of ICS controllers. The iFinger utilizes register states to generate ICS fingerprints to detect malicious attacks on industrial networks. Specifically, the boolean logic represents every register state sequence of the ICS controller, and the deterministic finite automaton (DFA) generates a device fingerprint. To discover the ICS attacks, we propose two detection approaches based on device fingerprints, including passive and active detection. We present a prototype of the iFinger and conduct real-world experiments to validate its performance. Results show that our approach achieves 97.1% F1 score in ICS device identification. Furthermore, we simulate two typical ICS attacks (replacement and code modification) to validate the effectiveness of our iFinger in industrial networks. Our device fingerprints would detect those malicious attacks within 2s latency at 98.0% recall.Index Terms-Industrial control system (ICS), fingerprinting, intrusion detection.
I. INTRODUCTIONC YBER-PHYSICAL systems (CPS) intertwine software components and physical processes, which are pervasive in diverse industries [1], such as manufacturing, automotive, energy, and medical monitoring. The control component plays a critical role in CPS, connecting cyberspace with Manuscript
We consider a cooperative two-path relay channel (TPRC) where a data source transmits new packets to a corresponding destination, with the assistance of two intermediate relays alternatively. When the transmitted source packet is successfully decoded by a relay, the relay proceeds to forward this packet in the subsequent time slot, otherwise it simply stays silent. Due to the inter-relay channels, the decoding result at a relay in the current time slot depends on the decoding result at the other relay in the previous time slot and not on that preceded it. In view of this property, we employ a Markov framework to analyze the decoding performance at the relays. The decoding of successive packets received at the destination can be similarly analyzed by using a Markov chain. Closed-form expressions of the outage probability are derived for TPRC by exploiting the properties of a Markov chain. Numerical results demonstrate that with the proposed scheme, a reasonably good performance is achieved with only a single-slot delay and relatively low complexity.Index Terms-Two-path relay channels, decode-and-forward, successive interference cancellation, Markov chain.
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