Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things

Securing the Internet of Things (IoT) is a necessary milestone toward expediting the deployment of its applications and services. In particular, the functionality of the IoT devices is extremely dependent on the reliability of their message transmission. Cyber attacks such as data injection, eavesdropping, and man-in-the-middle threats can lead to security challenges. Securing IoT devices against such attacks requires accounting for their stringent computational power and need for low-latency operations. In this paper, a novel deep learning method is proposed for dynamic watermarking of IoT signals to detect cyber attacks. The proposed learning framework, based on a long short-term memory (LSTM) structure, enables the IoT devices to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal. This method enables the IoT's cloud center, which collects signals from the IoT devices, to effectively authenticate the reliability of the signals. Furthermore, the proposed method prevents complicated attack scenarios such as eavesdropping in which the cyber attacker collects the data from the IoT devices and aims to break the watermarking algorithm. Simulation results show that, with an attack detection delay of under 1 second the messages can be transmitted from IoT devices with an almost 100% reliability.Comment: 6 pages, 9 figure

Fingerprinting Smart Devices Through Embedded Acoustic Components

The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart devices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote identification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an individual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic features and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even possible to fingerprint devices that have the same vendor and model; we were able to accurately distinguish over 93% of all recorded audio clips from 15 different units of the same model. Our study identifies the prominent acoustic features capable of fingerprinting devices with high success rate and examines the effect of background noise and other variables on fingerprinting accuracy

Exploring miscommunication and collaborative behaviour in human-robot interaction

This paper presents the first step in designing a speech-enabled robot that is capable of natural management of miscommunication. It describes the methods and results of two WOz studies, in which dyads of naïve participants interacted in a collaborative task. The first WOz study explored human miscommunication management. The second study investigated how shared visual space and monitoring shape the processes of feedback and communication in task-oriented interactions. The results provide insights for the development of human-inspired and robust natural language interfaces in robots

Detecting and Mitigating Denial-of-Service Attacks on Voice over IP Networks

Voice over IP (VoIP) is more susceptible to Denial of Service attacks than traditional data traffic, due to the former's low tolerance to delay and jitter. We describe the design of our VoIP Vulnerability Assessment Tool (VVAT) with which we demonstrate vulnerabilities to DoS attacks inherent in many of the popular VoIP applications available today. In our threat model we assume an adversary who is not a network administrator, nor has direct control of the channel and key VoIP elements. His aim is to degrade his victim's QoS without giving away his presence by making his attack look like a normal network degradation. Even black-boxed, applications like Skype that use proprietary protocols show poor performance under specially crafted DoS attacks to its media stream. Finally we show how securing Skype relays not only preserves many of its useful features such as seamless traversal of firewalls but also protects its users from DoS attacks such as recording of conversations and disruption of voice quality. We also present our experiences using virtualization to protect VoIP applications from 'insider attacks'. Our contribution is two fold we: 1) Outline a threat model for VoIP, incorporating our attack models in an open-source network simulator/emulator allowing VoIP vendors to check their software for vulnerabilities in a controlled environment before releasing it. 2) We present two promising approaches for protecting the confidentiality, availability and authentication of VoIP Services