2,362 research outputs found
Dual Rate Control for Security in Cyber-physical Systems
We consider malicious attacks on actuators and sensors of a feedback system
which can be modeled as additive, possibly unbounded, disturbances at the
digital (cyber) part of the feedback loop. We precisely characterize the role
of the unstable poles and zeros of the system in the ability to detect stealthy
attacks in the context of the sampled data implementation of the controller in
feedback with the continuous (physical) plant. We show that, if there is a
single sensor that is guaranteed to be secure and the plant is observable from
that sensor, then there exist a class of multirate sampled data controllers
that ensure that all attacks remain detectable. These dual rate controllers are
sampling the output faster than the zero order hold rate that operates on the
control input and as such, they can even provide better nominal performance
than single rate, at the price of higher sampling of the continuous output
Information Flow for Security in Control Systems
This paper considers the development of information flow analyses to support
resilient design and active detection of adversaries in cyber physical systems
(CPS). The area of CPS security, though well studied, suffers from
fragmentation. In this paper, we consider control systems as an abstraction of
CPS. Here, we extend the notion of information flow analysis, a well
established set of methods developed in software security, to obtain a unified
framework that captures and extends system theoretic results in control system
security. In particular, we propose the Kullback Liebler (KL) divergence as a
causal measure of information flow, which quantifies the effect of adversarial
inputs on sensor outputs. We show that the proposed measure characterizes the
resilience of control systems to specific attack strategies by relating the KL
divergence to optimal detection techniques. We then relate information flows to
stealthy attack scenarios where an adversary can bypass detection. Finally,
this article examines active detection mechanisms where a defender
intelligently manipulates control inputs or the system itself in order to
elicit information flows from an attacker's malicious behavior. In all previous
cases, we demonstrate an ability to investigate and extend existing results by
utilizing the proposed information flow analyses
OnionBots: Subverting Privacy Infrastructure for Cyber Attacks
Over the last decade botnets survived by adopting a sequence of increasingly
sophisticated strategies to evade detection and take overs, and to monetize
their infrastructure. At the same time, the success of privacy infrastructures
such as Tor opened the door to illegal activities, including botnets,
ransomware, and a marketplace for drugs and contraband. We contend that the
next waves of botnets will extensively subvert privacy infrastructure and
cryptographic mechanisms. In this work we propose to preemptively investigate
the design and mitigation of such botnets. We first, introduce OnionBots, what
we believe will be the next generation of resilient, stealthy botnets.
OnionBots use privacy infrastructures for cyber attacks by completely
decoupling their operation from the infected host IP address and by carrying
traffic that does not leak information about its source, destination, and
nature. Such bots live symbiotically within the privacy infrastructures to
evade detection, measurement, scale estimation, observation, and in general all
IP-based current mitigation techniques. Furthermore, we show that with an
adequate self-healing network maintenance scheme, that is simple to implement,
OnionBots achieve a low diameter and a low degree and are robust to
partitioning under node deletions. We developed a mitigation technique, called
SOAP, that neutralizes the nodes of the basic OnionBots. We also outline and
discuss a set of techniques that can enable subsequent waves of Super
OnionBots. In light of the potential of such botnets, we believe that the
research community should proactively develop detection and mitigation methods
to thwart OnionBots, potentially making adjustments to privacy infrastructure.Comment: 12 pages, 8 figure
A moving target defense to detect stealthy attacks in cyber-physical systems
Cyber-Physical Systems (CPS) have traditionally been considered more static, with regular communication patterns when compared to classical information technology networks. Because the structure of most CPS remains unchanged during long periods of time, they become vulnerable to adversaries who can tailor their attacks based on their precise knowledge of the system dynamics, communications, and control. Moving Target Defense (MTD) has emerged as a strategy to add uncertainty about the state and execution of a system in order to prevent adversaries from having predictable effects with their attacks. In this work we propose a novel type of MTD strategy that randomly changes the availability of the sensor data, so that it is harder for adversaries to tailor stealthy attacks and at the same time it can minimize the impact of false-data injection attacks. Using tools from switched control systems we formulate an optimization problem to find the probability of the switching signals that increase the visibility of stealthy attacks while decreasing the deviation caused by false data injection attacks
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