2,196 research outputs found
Trick or Heat? Manipulating Critical Temperature-Based Control Systems Using Rectification Attacks
Temperature sensing and control systems are widely used in the closed-loop
control of critical processes such as maintaining the thermal stability of
patients, or in alarm systems for detecting temperature-related hazards.
However, the security of these systems has yet to be completely explored,
leaving potential attack surfaces that can be exploited to take control over
critical systems.
In this paper we investigate the reliability of temperature-based control
systems from a security and safety perspective. We show how unexpected
consequences and safety risks can be induced by physical-level attacks on
analog temperature sensing components. For instance, we demonstrate that an
adversary could remotely manipulate the temperature sensor measurements of an
infant incubator to cause potential safety issues, without tampering with the
victim system or triggering automatic temperature alarms. This attack exploits
the unintended rectification effect that can be induced in operational and
instrumentation amplifiers to control the sensor output, tricking the internal
control loop of the victim system to heat up or cool down. Furthermore, we show
how the exploit of this hardware-level vulnerability could affect different
classes of analog sensors that share similar signal conditioning processes.
Our experimental results indicate that conventional defenses commonly
deployed in these systems are not sufficient to mitigate the threat, so we
propose a prototype design of a low-cost anomaly detector for critical
applications to ensure the integrity of temperature sensor signals.Comment: Accepted at the ACM Conference on Computer and Communications
Security (CCS), 201
A Study of IEEE 802.15.4 Security Framework for Wireless Body Area Network
A Wireless Body Area Network (WBAN) is a collection of low-power and
lightweight wireless sensor nodes that are used to monitor the human body
functions and the surrounding environment. It supports a number of innovative
and interesting applications, including ubiquitous healthcare and Consumer
Electronics (CE) applications. Since WBAN nodes are used to collect sensitive
(life-critical) information and may operate in hostile environments, they
require strict security mechanisms to prevent malicious interaction with the
system. In this paper, we first highlight major security requirements and
Denial of Service (DoS) attacks in WBAN at Physical, Medium Access Control
(MAC), Network, and Transport layers. Then we discuss the IEEE 802.15.4
security framework and identify the security vulnerabilities and major attacks
in the context of WBAN. Different types of attacks on the Contention Access
Period (CAP) and Contention Free Period (CFP) parts of the superframe are
analyzed and discussed. It is observed that a smart attacker can successfully
corrupt an increasing number of GTS slots in the CFP period and can
considerably affect the Quality of Service (QoS) in WBAN (since most of the
data is carried in CFP period). As we increase the number of smart attackers
the corrupted GTS slots are eventually increased, which prevents the legitimate
nodes to utilize the bandwidth efficiently. This means that the direct
adaptation of IEEE 802.15.4 security framework for WBAN is not totally secure
for certain WBAN applications. New solutions are required to integrate high
level security in WBAN.Comment: 14 pages, 7 figures, 2 table
KeyForge: Mitigating Email Breaches with Forward-Forgeable Signatures
Email breaches are commonplace, and they expose a wealth of personal,
business, and political data that may have devastating consequences. The
current email system allows any attacker who gains access to your email to
prove the authenticity of the stolen messages to third parties -- a property
arising from a necessary anti-spam / anti-spoofing protocol called DKIM. This
exacerbates the problem of email breaches by greatly increasing the potential
for attackers to damage the users' reputation, blackmail them, or sell the
stolen information to third parties.
In this paper, we introduce "non-attributable email", which guarantees that a
wide class of adversaries are unable to convince any third party of the
authenticity of stolen emails. We formally define non-attributability, and
present two practical system proposals -- KeyForge and TimeForge -- that
provably achieve non-attributability while maintaining the important protection
against spam and spoofing that is currently provided by DKIM. Moreover, we
implement KeyForge and demonstrate that that scheme is practical, achieving
competitive verification and signing speed while also requiring 42% less
bandwidth per email than RSA2048
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks
Intrusion detection has become one of the most critical tasks in a wireless
network to prevent service outages that can take long to fix. The sheer variety
of anomalous events necessitates adopting cognitive anomaly detection methods
instead of the traditional signature-based detection techniques. This paper
proposes an anomaly detection methodology for wireless systems that is based on
monitoring and analyzing radio frequency (RF) spectrum activities. Our
detection technique leverages an existing solution for the video prediction
problem, and uses it on image sequences generated from monitoring the wireless
spectrum. The deep predictive coding network is trained with images
corresponding to the normal behavior of the system, and whenever there is an
anomaly, its detection is triggered by the deviation between the actual and
predicted behavior. For our analysis, we use the images generated from the
time-frequency spectrograms and spectral correlation functions of the received
RF signal. We test our technique on a dataset which contains anomalies such as
jamming, chirping of transmitters, spectrum hijacking, and node failure, and
evaluate its performance using standard classifier metrics: detection ratio,
and false alarm rate. Simulation results demonstrate that the proposed
methodology effectively detects many unforeseen anomalous events in real time.
We discuss the applications, which encompass industrial IoT, autonomous vehicle
control and mission-critical communications services.Comment: 7 pages, 7 figures, Communications Workshop ICC'1
Secure Trajectory Planning Against Undetectable Spoofing Attacks
This paper studies, for the first time, the trajectory planning problem in
adversarial environments, where the objective is to design the trajectory of a
robot to reach a desired final state despite the unknown and arbitrary action
of an attacker. In particular, we consider a robot moving in a two-dimensional
space and equipped with two sensors, namely, a Global Navigation Satellite
System (GNSS) sensor and a Radio Signal Strength Indicator (RSSI) sensor. The
attacker can arbitrarily spoof the readings of the GNSS sensor and the robot
control input so as to maximally deviate his trajectory from the nominal
precomputed path. We derive explicit and constructive conditions for the
existence of undetectable attacks, through which the attacker deviates the
robot trajectory in a stealthy way. Conversely, we characterize the existence
of secure trajectories, which guarantee that the robot either moves along the
nominal trajectory or that the attack remains detectable. We show that secure
trajectories can only exist between a subset of states, and provide a numerical
mechanism to compute them. We illustrate our findings through several numerical
studies, and discuss that our methods are applicable to different models of
robot dynamics, including unicycles. More generally, our results show how
control design affects security in systems with nonlinear dynamics.Comment: Accepted for publication in Automatic
CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping
With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving 25 subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over 99% average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as 4%
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