33 research outputs found
Classification with Costly Features using Deep Reinforcement Learning
We study a classification problem where each feature can be acquired for a
cost and the goal is to optimize a trade-off between the expected
classification error and the feature cost. We revisit a former approach that
has framed the problem as a sequential decision-making problem and solved it by
Q-learning with a linear approximation, where individual actions are either
requests for feature values or terminate the episode by providing a
classification decision. On a set of eight problems, we demonstrate that by
replacing the linear approximation with neural networks the approach becomes
comparable to the state-of-the-art algorithms developed specifically for this
problem. The approach is flexible, as it can be improved with any new
reinforcement learning enhancement, it allows inclusion of pre-trained
high-performance classifier, and unlike prior art, its performance is robust
across all evaluated datasets.Comment: AAAI 201
Family house Rozvadze
Predmetom tejto bakalárskej práce je vypracovanie projektovej dokumentácie k vytvoreniu novostavby rodinného domu v katastrálnom území obce Trenčianske Stankovce, časť Rozvadze. Rodinný dom je samostatne stojaci dvojpodlažný objekt na okraji obce. Objekt je jednogeneračný, navrhnutý pre 4–5 osôb. Súčasťou objektu je garáž s jedným státím. Pôdorysný tvar objektu pripomína písmeno T. Konštrukčný systém je pozdĺžny, stenový. Zvislé a vodorovné konštrukcie sú zo systému HELUZ. Zastrešenie objektu je riešené plochými strechami. Nad časťou objektu je extenzívna zelená strecha. Výkresová časť práce je spracovaná počítačovým programom AutoCAD.The subject of this bachelor thesis is working out the project documentation to execution of a new detached house in cadastral area of Trenčianske Stankovce, part Rozvadze. Family house is detached two-storeyed building, located at the edge of the village. Object is meant for one generation, designed for 4-5 people. The building includes a garage with one car stand. Floor plan is designed in shape of a latter T. Construction system is longitudinal, wall made. Vertical and horizontal supporting structures of the house are made by the system HELUZ. Roofing of the building is designed with flat roofs. On the part of the object is extensive green roof. The drawing part is handled by the AutoCAD software.
Challenges and Open Questions of Machine Learning in Computer Security
This habilitation thesis presents advancements in machine learning for computer security,
arising from problems in network intrusion detection and steganography.
The thesis put an emphasis on explanation of traits shared by steganalysis, network intrusion
detection, and other security domains, which makes these domains different from
computer vision, speech recognition, and other fields where machine learning is typically
studied. Then, the thesis presents methods developed to at least partially solve the identified
problems with an overall goal to make machine learning based intrusion detection
system viable. Most of them are general in the sense that they can be used outside intrusion
detection and steganalysis on problems with similar constraints.
A common feature of all methods is that they are generally simple, yet surprisingly
effective. According to large-scale experiments they almost always improve the prior art,
which is likely caused by being tailored to security problems and designed for large volumes
of data.
Specifically, the thesis addresses following problems:
anomaly detection with low computational and memory complexity such that efficient
processing of large data is possible;
multiple-instance anomaly detection improving signal-to-noise ration by classifying
larger group of samples;
supervised classification of tree-structured data simplifying their encoding in neural
networks;
clustering of structured data;
supervised training with the emphasis on the precision in top p% of returned data;
and finally explanation of anomalies to help humans understand the nature of anomaly
and speed-up their decision.
Many algorithms and method presented in this thesis are deployed in the real intrusion
detection system protecting millions of computers around the globe
Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks
We focus on reinforcement learning (RL) in relational problems that are
naturally defined in terms of objects, their relations, and manipulations.
These problems are characterized by variable state and action spaces, and
finding a fixed-length representation, required by most existing RL methods, is
difficult, if not impossible. We present a deep RL framework based on graph
neural networks and auto-regressive policy decomposition that naturally works
with these problems and is completely domain-independent. We demonstrate the
framework in three very distinct domains and we report the method's competitive
performance and impressive zero-shot generalization over different problem
sizes. In goal-oriented BlockWorld, we demonstrate multi-parameter actions with
pre-conditions. In SysAdmin, we show how to select multiple objects
simultaneously. In the classical planning domain of Sokoban, the method trained
exclusively on 10x10 problems with three boxes solves 89% of 15x15 problems
with five boxes.Comment: RL4RealLife @ ICML2021; code available at
https://github.com/jaromiru/sr-dr
NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios
Current frameworks for training offensive penetration testing agents with
deep reinforcement learning struggle to produce agents that perform well in
real-world scenarios, due to the reality gap in simulation-based frameworks and
the lack of scalability in emulation-based frameworks. Additionally, existing
frameworks often use an unrealistic metric that measures the agents'
performance on the training data. NASimEmu, a new framework introduced in this
paper, addresses these issues by providing both a simulator and an emulator
with a shared interface. This approach allows agents to be trained in
simulation and deployed in the emulator, thus verifying the realism of the used
abstraction. Our framework promotes the development of general agents that can
transfer to novel scenarios unseen during their training. For the simulation
part, we adopt an existing simulator NASim and enhance its realism. The
emulator is implemented with industry-level tools, such as Vagrant, VirtualBox,
and Metasploit. Experiments demonstrate that a simulation-trained agent can be
deployed in emulation, and we show how to use the framework to train a general
agent that transfers into novel, structurally different scenarios. NASimEmu is
available as open-source.Comment: NASimEmu is available at https://github.com/jaromiru/NASimEmu and the
baseline agents at https://github.com/jaromiru/NASimEmu-agent
Hierarchical Multiple-Instance Data Classification with Costly Features
We extend the framework of Classification with Costly Features (CwCF) that
works with samples of fixed dimensions to trees of varying depth and breadth
(similar to a JSON/XML file). In this setting, the sample is a tree - sets of
sets of features. Individually for each sample, the task is to sequentially
select informative features that help the classification. Each feature has a
real-valued cost, and the objective is to maximize accuracy while minimizing
the total cost. The process is modeled as an MDP where the states represent the
acquired features, and the actions select unknown features. We present a
specialized neural network architecture trained through deep reinforcement
learning that naturally fits the data and directly selects features in the
tree. We demonstrate our method in seven datasets and compare it to two
baselines.Comment: RL4RealLife @ ICML2021; code available at
https://github.com/jaromiru/rcwc