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IoT devices fundamentally lack built-in security mechanisms to protect themselves from security attacks. Existing works on improving IoT security mostly focus on detecting anomalous behaviors of IoT devices. However, these existing anomaly detection schemes may trigger an overwhelmingly large number of false alerts, rendering them unusable in detecting compromised IoT devices. In this paper we develop an effective and efficient framework, named CUMAD, to detect compromised IoT devices. Instead of directly relying on individual anomalous events, CUMAD aims to accumulate sufficient evidence in detecting compromised IoT devices, by integrating an autoencoder-based anomaly detection subsystem with a sequential probability ratio test (SPRT)-based sequential hypothesis testing subsystem. CUMAD can effectively reduce the number of false alerts in detecting compromised IoT devices, and moreover, it can detect compromised IoT devices quickly. Our evaluation studies based on the public-domain N-BaIoT dataset show that CUMAD can on average reduce the false positive rate from about 3.57% using only the autoencoder-based anomaly detection scheme to about 0.5%; in addition, CUMAD can detect compromised IoT devices quickly, with less than 5 observations on average. 

IoT devices fundamentally lack built-in security mechanisms to protect themselves from security attacks. Existing works on improving IoT security mostly focus on detecting anomalous behaviors of IoT devices. However, these existing anomaly detection schemes may trigger an overwhelmingly large number of false alerts, rendering them unusable in detecting compromised IoT devices. In this paper we develop an effective and efficient framework, named CUMAD, to detect compromised IoT devices. Instead of directly relying on individual anomalous events, CUMAD aims to accumulate sufficient evidence in detecting compromised IoT devices, by integrating an autoencoder-based anomaly detection subsystem with a sequential probability ratio test (SPRT)-based sequential hypothesis testing subsystem. CUMAD can effectively reduce the number of false alerts in detecting compromised IoT devices, and moreover, it can detect compromised IoT devices quickly. Our evaluation studies based on the public-domain N-BaIoT dataset show that CUMAD can on average reduce the false positive rate from about 3.57% using only the autoencoder-based anomaly detection scheme to about 0.5%; in addition, CUMAD can detect compromised IoT devices quickly, with less than 5 observations on average. 

Although some existing counterdrone measures can disrupt the invasion of certain consumer drone, to the best of our knowledge, none of them can accurately redirect it to a given location for defense. In this paper, we proposed a Drone Position Manipulation (DPM) attack to address this issue by utilizing the vulnerabilities of control and navigation algorithms used on consumer drones. As such drones usually depend on GPS for autopiloting, we carefully spoof GPS signals based on where we want to redirect a drone to, such that we indirectly affect its position estimates that are used by its navigation algorithm. By carefully manipulating these states, we make a drone gradually move to a path based on our requirements. This unique attack exploits the entire stack of sensing, state estimation, and navigation control together for quantitative manipulation of flight paths, different from all existing methods. In addition, we have formally analyzed the feasible range of redirected destinations for a given target. Our evaluation on open-source ArduPilot system shows that DPM is able to not only accurately lead a drone to a redirected destination but also achieve a large redirection range. 

The Mars Atmosphere and Volatile EvolutioN (MAVEN) mission has been orbiting Mars since 2014 and now has over 10,000 orbits which we use to characterize Mars' dynamic space environment. Through global field line tracing with MAVEN magnetic field data we find an altitude dependent draping morphology that differs from expectations of induced magnetospheres in the vertical ( Mars Sun‐state, MSO) direction. We quantify this difference from the classical picture of induced magnetospheres with a Bayesian multiple linear regression model to predict the draped field as a function of the upstream interplanetary magnetic field (IMF), remanent crustal fields, and a previously underestimated induced effect. From our model we conclude that unexpected twists in high altitude dayside draping (>800 km) are a result of the IMF component in the MSO direction. We propose that this is a natural outcome of current theories of induced magnetospheres but has been underestimated due to approximations of the IMF as solely directed. We additionally estimate that distortions in low altitude (<800 km) dayside draping along are directly related to remanent crustal fields. We show dayside draping traces down tail and previously reported inner magnetotail twists are likely caused by the crustal field of Mars, while the outer tail morphology is governed by an induced response to the IMF direction. We conclude with an updated understanding of induced magnetospheres which details dayside draping for multiple directions of the incoming IMF and discuss the repercussions of this draping for magnetotail morphology.

 

We present a JWST mid-infrared (MIR) spectrum of the underluminous Type Ia Supernova (SN Ia) 2022xkq, obtained with the medium-resolution spectrometer on the Mid-Infrared Instrument (MIRI) ∼130 days post-explosion. We identify the first MIR lines beyond 14μm in SN Ia observations. We find features unique to underluminous SNe Ia, including the following: isolated emission of stable Ni, strong blends of [Tiii], and large ratios of singly ionized to doubly ionized species in both [Ar] and [Co]. Comparisons to normal-luminosity SNe Ia spectra at similar phases show a tentative trend between the width of the [Coiii] 11.888μm feature and the SN light-curve shape. Using non-LTE-multi-dimensional radiation hydro simulations and the observed electron capture elements, we constrain the mass of the exploding WD. The best-fitting model shows that SN 2022xkq is consistent with an off-center delayed-detonation explosion of a near-Chandrasekhar mass WD ($M_{\mathrm{WD}}$≈1.37M_⊙) of high central density (ρ_c≥ 2.0 × 10⁹g cm⁻³) seen equator-on, which producedM(⁵⁶Ni) =0.324M_⊙andM(⁵⁸Ni) ≥0.06M_⊙. The observed line widths are consistent with the overall abundance distribution; and the narrow stable Ni lines indicate little to no mixing in the central regions, favoring central ignition of subsonic carbon burning followed by an off-center deflagration-to-detonation transition beginning at a single point. Additional observations may further constrain the physics revealing the presence of additional species including Cr and Mn. Our work demonstrates the power of using the full coverage of MIRI in combination with detailed modeling to elucidate the physics of SNe Ia at a level not previously possible.