Detecting ADS-B Spoofing Attacks using Deep Neural Networks

The Automatic Dependent Surveillance-Broadcast (ADS-B) system is a key component of the Next Generation Air Transportation System (NextGen) that manages the increasingly congested airspace. It provides accurate aircraft localization and efficient air traffic management and also improves the safety of billions of current and future passengers. While the benefits of ADS-B are well known, the lack of basic security measures like encryption and authentication introduces various exploitable security vulnerabilities. One practical threat is the ADS-B spoofing attack that targets the ADS-B ground station, in which the ground-based or aircraft-based attacker manipulates the International Civil Aviation Organization (ICAO) address (a unique identifier for each aircraft) in the ADS-B messages to fake the appearance of non-existent aircraft or masquerade as a trusted aircraft. As a result, this attack can confuse the pilots or the air traffic control personnel and cause dangerous maneuvers. In this paper, we introduce SODA - a two-stage Deep Neural Network (DNN)-based spoofing detector for ADS-B that consists of a message classifier and an aircraft classifier. It allows a ground station to examine each incoming message based on the PHY-layer features (e.g., IQ samples and phases) and flag suspicious messages. Our experimental results show that SODA detects ground-based spoofing attacks with a probability of 99.34%, while having a very small false alarm rate (i.e., 0.43%). It outperforms other machine learning techniques such as XGBoost, Logistic Regression, and Support Vector Machine. It further identifies individual aircraft with an average F-score of 96.68% and an accuracy of 96.66%, with a significant improvement over the state-of-the-art detector.Comment: Accepted to IEEE CNS 201

TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution

The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A ton-level liquid scintillator detector will be placed at about 30 m from a core of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be measured with sub-percent energy resolution, to provide a reference spectrum for future reactor neutrino experiments, and to provide a benchmark measurement to test nuclear databases. A spherical acrylic vessel containing 2.8 ton gadolinium-doped liquid scintillator will be viewed by 10 m^2 Silicon Photomultipliers (SiPMs) of >50% photon detection efficiency with almost full coverage. The photoelectron yield is about 4500 per MeV, an order higher than any existing large-scale liquid scintillator detectors. The detector operates at -50 degree C to lower the dark noise of SiPMs to an acceptable level. The detector will measure about 2000 reactor antineutrinos per day, and is designed to be well shielded from cosmogenic backgrounds and ambient radioactivities to have about 10% background-to-signal ratio. The experiment is expected to start operation in 2022

Augmenting white cane reliability using smart glove for visually impaired people

The independent mobility problem of visually impaired people has been an active research topic in biomedical engineering: although many smart tools have been proposed, traditional tools (e.g., the white cane) continue to play a prominent role. In this paper a low cost smart glove is presented: the key idea is to minimize the impact in using it by combining the traditional tools with a technological device able to improve the movement performance of the visually impaired people

Network anomaly detection in critical infrastructure based on mininet network simulator

In this paper, a highly-configurable network anomaly detection system for Critical Infrastructure scenarios is presented. The Mininet virtual machine environment has been used in this framework to simulate an Industrial Control System network and to replicate both physical and cyber components. Finally, a cyber-attack has been implemented for showing both the effectiveness and capability of the proposed network security system

A Novel Architecture for Cyber-Physical Security in Industrial Control Networks

Over the last decades, the evolution of information and communication technology has joined the automation and control systems development, leading to Cyber-Physical Systems integration in industrial environments. Since complex threats targeting physical processes require advanced and interdisciplinary security approaches, classic cyber-security tools are ineffective in industrial scenarios. In this paper, we present a modular framework able to provide cyber-physical security for industrial control systems. Our Deep Detection Architecture (DDA) fills the gap between computer science and control theoretic approaches. Moreover, we present an innovative cyber-physical simulation methodology as a baseline for validation purposes

A low cost smart glove for visually impaired people mobility

Degradation of the visual system reduces the mobility of a person that relies only on his sense of touch and hearing. This paper presents the prototype of a low cost smart glove to improve the mobility of the visually impaired people. The glove is equipped with rangefinders to explore the surroundings: it provides a vibro-tactile feedback on the position of the closest obstacles in range by means of vibration motors. The system is designed to operate with the white cane, enhancing the reliability of this traditional tool