5,299 research outputs found
Detecting cyberattacks in industrial control systems using online learning algorithms
Industrial control systems are critical to the operation of industrial
facilities, especially for critical infrastructures, such as refineries, power
grids, and transportation systems. Similar to other information systems, a
significant threat to industrial control systems is the attack from
cyberspace---the offensive maneuvers launched by "anonymous" in the digital
world that target computer-based assets with the goal of compromising a
system's functions or probing for information. Owing to the importance of
industrial control systems, and the possibly devastating consequences of being
attacked, significant endeavors have been attempted to secure industrial
control systems from cyberattacks. Among them are intrusion detection systems
that serve as the first line of defense by monitoring and reporting potentially
malicious activities. Classical machine-learning-based intrusion detection
methods usually generate prediction models by learning modest-sized training
samples all at once. Such approach is not always applicable to industrial
control systems, as industrial control systems must process continuous control
commands with limited computational resources in a nonstop way. To satisfy such
requirements, we propose using online learning to learn prediction models from
the controlling data stream. We introduce several state-of-the-art online
learning algorithms categorically, and illustrate their efficacies on two
typically used testbeds---power system and gas pipeline. Further, we explore a
new cost-sensitive online learning algorithm to solve the class-imbalance
problem that is pervasive in industrial intrusion detection systems. Our
experimental results indicate that the proposed algorithm can achieve an
overall improvement in the detection rate of cyberattacks in industrial control
systems
Argument Mining with Structured SVMs and RNNs
We propose a novel factor graph model for argument mining, designed for
settings in which the argumentative relations in a document do not necessarily
form a tree structure. (This is the case in over 20% of the web comments
dataset we release.) Our model jointly learns elementary unit type
classification and argumentative relation prediction. Moreover, our model
supports SVM and RNN parametrizations, can enforce structure constraints (e.g.,
transitivity), and can express dependencies between adjacent relations and
propositions. Our approaches outperform unstructured baselines in both web
comments and argumentative essay datasets.Comment: Accepted for publication at ACL 2017. 11 pages, 5 figures. Code at
https://github.com/vene/marseille and data at http://joonsuk.org
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Constraint breeding during on-line incremental learning
An evolutionary algorithm for simultaneously inducing and weighting phonological constraints (the Winnow-MaxEnt-Subtree Breeder) is described, analyzed, and illustrated. Implementing weights as sub-population sizes, reproduction with selection executes a new variant of Winnow (Littleton 1988), which is shown to converge. A flexible constraint schema, based on the same prosodic and autosegmental trees used in representations, is described, together with algorithms for mutation and recombination (mating). The algorithm is applied to explaining abrupt learning curves, and predicts an empirical connection between abruptness and language-particularity
DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret
Dynamic treatment regimes (DTRs) are personalized, adaptive, multi-stage
treatment plans that adapt treatment decisions both to an individual's initial
features and to intermediate outcomes and features at each subsequent stage,
which are affected by decisions in prior stages. Examples include personalized
first- and second-line treatments of chronic conditions like diabetes, cancer,
and depression, which adapt to patient response to first-line treatment,
disease progression, and individual characteristics. While existing literature
mostly focuses on estimating the optimal DTR from offline data such as from
sequentially randomized trials, we study the problem of developing the optimal
DTR in an online manner, where the interaction with each individual affect both
our cumulative reward and our data collection for future learning. We term this
the DTR bandit problem. We propose a novel algorithm that, by carefully
balancing exploration and exploitation, is guaranteed to achieve rate-optimal
regret when the transition and reward models are linear. We demonstrate our
algorithm and its benefits both in synthetic experiments and in a case study of
adaptive treatment of major depressive disorder using real-world data
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