In this paper we consider dynamically sharing the spectrum in the time-domain by exploiting whitespace between the bursty transmissions of a set of users, represented by an 802.11b based wireless LAN (WLAN). Realizing that exploiting the under-utilization of the channel requires a good model of the these users' medium access, we propose a continuous-time semi-Markov model that captures the WLAN's behavior yet remains tractable enough to be used for deriving optimal control strategies within a decision-theoretic framework. Our model is based on actual measurements in the 2.4 GHz ISM band using a vector signal analyzer to collect complex baseband data. We explore two different sensing strategies to identify spectrum opportunities depending on whether the primary user's transmission scheme is known. The collected data is used to statistically characterize the idle and busy periods of the channel. Furthermore, we show that a continuous-time semi-Markov model is able to capture the data with good accuracy. The Kolmogorov-Smirnov test is used to validate the model and to assess the model's goodness-of-fit quantitatively. A conclusion summarizes the main results of the paper

Unmanned aerial vehicles (UAVs) have increasingly shown to be useful in civilian applications (such as agriculture, public safety, surveillance) and mission critical military applications. Despite the growth in popularity and applications, UAVs have also been used for malicious purposes. In such instances, their timely detection and identification has garnished rising interest from government, industry and academia. While much work has been done for detecting UAVs, there still exist limitations related to the impact of extreme environmental conditions and big dataset requirements. This paper proposes a novel ensemble deep learning framework that has hybrid synthetic and deep features to detect unauthorized or malicious UAVs by using acoustic, image/video and wireless radio frequency (RF) signals for robust UAV detection and classification. We present the performance evaluation of the proposed approach using numerical results obtained from experiments using acoustic, image/video and wireless RF signals. The proposed approach outperforms the existing related approaches for detecting malicious UAVs.

Authentication via fingerprint embedding at the physical layer utilizes noise in the wireless channel to attain a certain degree of information theoretic security that traditional HMAC methods cannot provide. Fingerprint embedding refers to a key-aided process of superimposing a low-power tag to the primary message waveform for the purpose of authenticating the transmission. The tag is uniquely created from the message and key and successful authentication is achieved when the correct tag is detected by the receiver. This paper generalizes a framework for embedding physical layer fingerprints to create an authenticated side-channel for minimal cost. Side-channel information is conveyed to the receiver through the transmitter's choice of tag from a secret codebook generated by the primary message and a shared secret key. In addition, a new linear coding scheme is introduced which enhances the ability to trade off the performance goals of authentication, side-channel rate, secrecy, and privacy.

Causal inference is the idea of cause and effect; this fundamental area of sciences can be applied to problem space associated with Newton’s laws or the devastating COVID-19 pandemic. The cause explains the “why,” whereas the effect describes the “what.” The domain itself encompasses a plethora of disciplines from statistics and computer science to economics and philosophy. Recent advancements in machine learning and artificial intelligence systems have nourished a renewed interest in identifying and estimating the cause-and-effect relationship from the substantial amount of available observational data. This has resulted in various new studies aimed at providing novel methods for identifying and estimating causal inference. We include a detailed taxonomy of causal inference frameworks, methods, and evaluation. An overview of causality for security is also provided. Open challenges are detailed, and approaches for evaluating the robustness of causal inference methods are described. This article aims to provide a comprehensive survey on such studies of causality. We provide an in-depth review of causality frameworks and describe the different methods.

Fingerprint embedding at the physical layer is a highly tunable authentication framework for wireless communication that achieves information-theoretic security by hiding a traditional HMAC tag in noise. In a multiantenna scenario, artificial noise (AN) can be transmitted to obscure the tag even further. The AN strategy, however, relies on perfect knowledge of the channel state information (CSI) between the legitimate users. When the CSI is not perfectly known, the added noise leaks into the receiver's observations. In this article, we explore whether AN still improves security in the fingerprint embedding authentication framework with only imperfect CSI available at the transmitter and receiver. Specifically, we discuss and design detectors that account for AN leakage and analyze the adversary's ability to recover the key from observed transmissions. We compare the detection and security performance of the optimal perfect CSI detector with the imperfect CSI robust matched filter test and a generalized likelihood ratio test (GLRT). We find that utilizing AN can greatly improve security, but suffers from diminishing returns when the quality of CSI knowledge is poor. In fact, we find that in some cases allocating additional power to AN can begin to decrease key security.

Configuration and maintenance of wireless sensor networks are complicated by random deployments, random finite sensor lifetimes, addition of new nodes, the desire to adhere to strict power and energy constraints, and perhaps mobility. The desire to maximize network lifetime, in the face of finite energy resources, leads to duty cycled radios that save energy by listening only at appropriate times. This implies a level of synchronization across the network. Synchronization is also needed to support the network goals, such as event detection and reporting. In this paper, we review the problem of wireless sensor network synchronization and associated key issues, including the fundamental signal processing tasks needed, such as estimation of the skew and offset between two clocks, round trip delay estimation, extension of synchronization to multiple nodes, as well as performance analysis and bounds