11,346 research outputs found
Review of Cyber-Physical Attacks and Counter Defense Mechanisms for Advanced Metering Infrastructure in Smart Grid
The Advanced Metering Infrastructure (AMI) is a vital element in the current
development of the smart grid. AMI technologies provide electric utilities with
an effective way of continuous monitoring and remote control of smart grid
components. However, owing to its increasing scale and cyber-physical nature,
the AMI has been faced with security threats in both cyber and physical
domains. This paper provides a comprehensive review of the crucial
cyber-physical attacks and counter defense mechanisms in the AMI. First, two
attack surfaces are surveyed in the AMI including the communication network and
smart meters. The potential cyber-physical attacks are then reviewed for each
attack surface. Next, the attack models and their cyber and physical impacts on
the smart grid are studied for comparison. Counter defense mechanisms that help
mitigate these security threats are discussed. Finally, several mathematical
tools which may help in analysis and implementation of security solutions are
summarized
Cyber-Physical Security and Safety of Autonomous Connected Vehicles: Optimal Control Meets Multi-Armed Bandit Learning
Autonomous connected vehicles (ACVs) rely on intra-vehicle sensors such as
camera and radar as well as inter-vehicle communication to operate effectively.
This reliance on cyber components exposes ACVs to cyber and physical attacks in
which an adversary can manipulate sensor readings and physically take control
of an ACV. In this paper, a comprehensive framework is proposed to thwart cyber
and physical attacks on ACV networks. First, an optimal safe controller for
ACVs is derived to maximize the street traffic flow while minimizing the risk
of accidents by optimizing ACV speed and inter-ACV spacing. It is proven that
the proposed controller is robust to physical attacks which aim at making ACV
systems instable. To improve the cyber-physical security of ACV systems, next,
data injection attack (DIA) detection approaches are proposed to address cyber
attacks on sensors and their physical impact on the ACV system. To
comprehensively design the DIA detection approaches, ACV sensors are
characterized in two subsets based on the availability of a-priori information
about their data. For sensors having a prior information, a DIA detection
approach is proposed and an optimal threshold level is derived for the
difference between the actual and estimated values of sensors data which
enables ACV to stay robust against cyber attacks. For sensors having no prior
information, a novel multi-armed bandit (MAB) algorithm is proposed to enable
ACV to securely control its motion. Simulation results show that the proposed
optimal safe controller outperforms current state of the art controllers by
maximizing the robustness of ACVs to physical attacks. The results also show
that the proposed DIA detection approaches, compared to Kalman filtering, can
improve the security of ACV sensors against cyber attacks and ultimately
improve the physical robustness of an ACV system.Comment: 30 pages, 11 figure
Model-free Reinforcement Learning for Non-stationary Mean Field Games
In this paper, we consider a finite horizon, non-stationary, mean field games
(MFG) with a large population of homogeneous players, sequentially making
strategic decisions, where each player is affected by other players through an
aggregate population state termed as mean field state. Each player has a
private type that only it can observe, and a mean field population state
representing the empirical distribution of other players' types, which is
shared among all of them. Recently, authors in [1] provided a sequential
decomposition algorithm to compute mean field equilibrium (MFE) for such games
which allows for the computation of equilibrium policies for them in linear
time than exponential, as before. In this paper, we extend it for the case when
state transitions are not known, to propose a reinforcement learning algorithm
based on Expected Sarsa with a policy gradient approach that learns the MFE
policy by learning the dynamics of the game simultaneously. We illustrate our
results using cyber-physical security example.Comment: 7 pages, 2 figure
Game-Theoretic Analysis of Cyber Deception: Evidence-Based Strategies and Dynamic Risk Mitigation
Deception is a technique to mislead human or computer systems by manipulating
beliefs and information. For the applications of cyber deception,
non-cooperative games become a natural choice of models to capture the
adversarial interactions between the players and quantitatively characterizes
the conflicting incentives and strategic responses. In this chapter, we provide
an overview of deception games in three different environments and extend the
baseline signaling game models to include evidence through side-channel
knowledge acquisition to capture the information asymmetry, dynamics, and
strategic behaviors of deception. We analyze the deception in binary
information space based on a signaling game framework with a detector that
gives off probabilistic evidence of the deception when the sender acts
deceptively. We then focus on a class of continuous one-dimensional information
space and take into account the cost of deception in the signaling game. We
finally explore the multi-stage incomplete-information Bayesian game model for
defensive deception for advanced persistent threats (APTs). We use the perfect
Bayesian Nash equilibrium (PBNE) as the solution concept for the deception
games and analyze the strategic equilibrium behaviors for both the deceivers
and the deceivees.Comment: arXiv admin note: text overlap with arXiv:1810.0075
Counter-Factual Reinforcement Learning: How to Model Decision-Makers That Anticipate The Future
This paper introduces a novel framework for modeling interacting humans in a
multi-stage game. This "iterated semi network-form game" framework has the
following desirable characteristics: (1) Bounded rational players, (2)
strategic players (i.e., players account for one another's reward functions
when predicting one another's behavior), and (3) computational tractability
even on real-world systems. We achieve these benefits by combining concepts
from game theory and reinforcement learning. To be precise, we extend the
bounded rational "level-K reasoning" model to apply to games over multiple
stages. Our extension allows the decomposition of the overall modeling problem
into a series of smaller ones, each of which can be solved by standard
reinforcement learning algorithms. We call this hybrid approach "level-K
reinforcement learning". We investigate these ideas in a cyber battle scenario
over a smart power grid and discuss the relationship between the behavior
predicted by our model and what one might expect of real human defenders and
attackers.Comment: Decision Making with Multiple Imperfect Decision Makers; Springer. 29
Pages, 6 Figure
A Game-Theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy
Cyberattacks on both databases and critical infrastructure have threatened
public and private sectors. Ubiquitous tracking and wearable computing have
infringed upon privacy. Advocates and engineers have recently proposed using
defensive deception as a means to leverage the information asymmetry typically
enjoyed by attackers as a tool for defenders. The term deception, however, has
been employed broadly and with a variety of meanings. In this paper, we survey
24 articles from 2008-2018 that use game theory to model defensive deception
for cybersecurity and privacy. Then we propose a taxonomy that defines six
types of deception: perturbation, moving target defense, obfuscation, mixing,
honey-x, and attacker engagement. These types are delineated by their
information structures, agents, actions, and duration: precisely concepts
captured by game theory. Our aims are to rigorously define types of defensive
deception, to capture a snapshot of the state of the literature, to provide a
menu of models which can be used for applied research, and to identify
promising areas for future work. Our taxonomy provides a systematic foundation
for understanding different types of defensive deception commonly encountered
in cybersecurity and privacy.Comment: To Appear in ACM Cumputing Surveys (CSUR
Cyber-Physical War Gaming
This paper presents general strategies for cyber war gaming of Cyber-Physical
Systems (CPSs) that are used for cyber security research at the U.S. Army
Research Laboratory (ARL). Since Supervisory Control and Data Acquisition
(SCADA) and other CPSs are operational systems, it is difficult or impossible
to perform security experiments on actual systems. The authors describe how
table-top strategy sessions and realistic, live CPS war games are conducted at
ARL. They also discuss how the recorded actions of the war game activity can be
used to test and validate cyber-defence models, such as game-theoretic security
models.Comment: To appear in Journal of Information Warfare, Volume 1
Persuasion-based Robust Sensor Design Against Attackers with Unknown Control Objectives
In this paper, we introduce a robust sensor design framework to provide
"persuasion-based" defense in stochastic control systems against an unknown
type attacker with a control objective exclusive to its type. For effective
control, such an attacker's actions depend on its belief on the underlying
state of the system. We design a robust "linear-plus-noise" signaling strategy
to encode sensor outputs in order to shape the attacker's belief in a strategic
way and correspondingly to persuade the attacker to take actions that lead to
minimum damage with respect to the system's objective. The specific model we
adopt is a Gauss-Markov process driven by a controller with a (partially)
"unknown" malicious/benign control objective. We seek to defend against the
worst possible distribution over control objectives in a robust way under the
solution concept of Stackelberg equilibrium, where the sensor is the leader. We
show that a necessary and sufficient condition on the covariance matrix of the
posterior belief is a certain linear matrix inequality and we provide a
closed-form solution for the associated signaling strategy. This enables us to
formulate an equivalent tractable problem, indeed a semi-definite program, to
compute the robust sensor design strategies "globally" even though the original
optimization problem is non-convex and highly nonlinear. We also extend this
result to scenarios where the sensor makes noisy or partial measurements.
Finally, we analyze the ensuing performance numerically for various scenarios
Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems
To operate effectively in tomorrow's smart cities, autonomous vehicles (AVs)
must rely on intra-vehicle sensors such as camera and radar as well as
inter-vehicle communication. Such dependence on sensors and communication links
exposes AVs to cyber-physical (CP) attacks by adversaries that seek to take
control of the AVs by manipulating their data. Thus, to ensure safe and optimal
AV dynamics control, the data processing functions at AVs must be robust to
such CP attacks. To this end, in this paper, the state estimation process for
monitoring AV dynamics, in presence of CP attacks, is analyzed and a novel
adversarial deep reinforcement learning (RL) algorithm is proposed to maximize
the robustness of AV dynamics control to CP attacks. The attacker's action and
the AV's reaction to CP attacks are studied in a game-theoretic framework. In
the formulated game, the attacker seeks to inject faulty data to AV sensor
readings so as to manipulate the inter-vehicle optimal safe spacing and
potentially increase the risk of AV accidents or reduce the vehicle flow on the
roads. Meanwhile, the AV, acting as a defender, seeks to minimize the
deviations of spacing so as to ensure robustness to the attacker's actions.
Since the AV has no information about the attacker's action and due to the
infinite possibilities for data value manipulations, the outcome of the
players' past interactions are fed to long-short term memory (LSTM) blocks.
Each player's LSTM block learns the expected spacing deviation resulting from
its own action and feeds it to its RL algorithm. Then, the the attacker's RL
algorithm chooses the action which maximizes the spacing deviation, while the
AV's RL algorithm tries to find the optimal action that minimizes such
deviation.Comment: 8 pages, 4 figure
Stovepiping and Malicious Software: A Critical Review of AGI Containment
Awareness of the possible impacts associated with artificial intelligence has
risen in proportion to progress in the field. While there are tremendous
benefits to society, many argue that there are just as many, if not more,
concerns related to advanced forms of artificial intelligence. Accordingly,
research into methods to develop artificial intelligence safely is increasingly
important. In this paper, we provide an overview of one such safety paradigm:
containment with a critical lens aimed toward generative adversarial networks
and potentially malicious artificial intelligence. Additionally, we illuminate
the potential for a developmental blindspot in the stovepiping of containment
mechanisms
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