2,485 research outputs found
Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
In this paper, we present a hierarchical path planning framework called SG-RL
(subgoal graphs-reinforcement learning), to plan rational paths for agents
maneuvering in continuous and uncertain environments. By "rational", we mean
(1) efficient path planning to eliminate first-move lags; (2) collision-free
and smooth for agents with kinematic constraints satisfied. SG-RL works in a
two-level manner. At the first level, SG-RL uses a geometric path-planning
method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract
paths, also called subgoal sequences. At the second level, SG-RL uses an RL
method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal
motion-planning policies which can generate kinematically feasible and
collision-free trajectories between adjacent subgoals. The first advantage of
the proposed method is that SSG can solve the limitations of sparse reward and
local minima trap for RL agents; thus, LSPI can be used to generate paths in
complex environments. The second advantage is that, when the environment
changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to
reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI
can deal with uncertainties by exploiting its generalization ability to handle
changes in environments. Simulation experiments in representative scenarios
demonstrate that, compared with existing methods, SG-RL can work well on
large-scale maps with relatively low action-switching frequencies and shorter
path lengths, and SG-RL can deal with small changes in environments. We further
demonstrate that the design of reward functions and the types of training
environments are important factors for learning feasible policies.Comment: 20 page
Constructing level-2 phylogenetic networks from triplets
Jansson and Sung showed that, given a dense set of input triplets T
(representing hypotheses about the local evolutionary relationships of triplets
of species), it is possible to determine in polynomial time whether there
exists a level-1 network consistent with T, and if so to construct such a
network. They also showed that, unlike in the case of trees (i.e. level-0
networks), the problem becomes NP-hard when the input is non-dense. Here we
further extend this work by showing that, when the set of input triplets is
dense, the problem is even polynomial-time solvable for the construction of
level-2 networks. This shows that, assuming density, it is tractable to
construct plausible evolutionary histories from input triplets even when such
histories are heavily non-tree like. This further strengthens the case for the
use of triplet-based methods in the construction of phylogenetic networks. We
also show that, in the non-dense case, the level-2 problem remains NP-hard
ANANAS - A Framework For Analyzing Android Applications
Android is an open software platform for mobile devices with a large market
share in the smartphone sector. The openness of the system as well as its wide
adoption lead to an increasing amount of malware developed for this platform.
ANANAS is an expandable and modular framework for analyzing Android
applications. It takes care of common needs for dynamic malware analysis and
provides an interface for the development of plugins. Adaptability and
expandability have been main design goals during the development process. An
abstraction layer for simple user interaction and phone event simulation is
also part of the framework. It allows an analyst to script the required user
simulation or phone events on demand or adjust the simulation to his needs. Six
plugins have been developed for ANANAS. They represent well known techniques
for malware analysis, such as system call hooking and network traffic analysis.
The focus clearly lies on dynamic analysis, as five of the six plugins are
dynamic analysis methods.Comment: Paper accepted at First Int. Workshop on Emerging Cyberthreats and
Countermeasures ECTCM 201
Control Flow Graph-based Path Reconstruction in Android applications
openOver the years, the field of Android security research has faced significant limitations due to the absence of reliable methods for achieving automated interaction with mobile applications. The lack of such tools has resulted in the widespread use of automatic exercising software, which randomly interfaces with apps in the hopes of obtaining desired outcomes. However, this approach cannot always be considered a satisfactory solution, as it lacks solid criteria and fails to provide any Proof-of-Reachability.
In the context of my thesis, I employed Control Flow Graphs to reconstruct pathways that lead to specified target methods within Android applications. This approach allowed me to extract high-level instructions that automatic interaction software can accurately and reliably execute in order to reach a designated endpoint.
Tests and evaluations conducted on this technique demonstrate its potential to facilitate more precise and goal-oriented testing. Its applications in the future could span from fuzzing and exploitation to aiding in the disclosure of privacy violations.Over the years, the field of Android security research has faced significant limitations due to the absence of reliable methods for achieving automated interaction with mobile applications. The lack of such tools has resulted in the widespread use of automatic exercising software, which randomly interfaces with apps in the hopes of obtaining desired outcomes. However, this approach cannot always be considered a satisfactory solution, as it lacks solid criteria and fails to provide any Proof-of-Reachability.
In the context of my thesis, I employed Control Flow Graphs to reconstruct pathways that lead to specified target methods within Android applications. This approach allowed me to extract high-level instructions that automatic interaction software can accurately and reliably execute in order to reach a designated endpoint.
Tests and evaluations conducted on this technique demonstrate its potential to facilitate more precise and goal-oriented testing. Its applications in the future could span from fuzzing and exploitation to aiding in the disclosure of privacy violations
PAGAI: a path sensitive static analyzer
We describe the design and the implementation of PAGAI, a new static analyzer
working over the LLVM compiler infrastructure, which computes inductive
invariants on the numerical variables of the analyzed program. PAGAI implements
various state-of-the-art algorithms combining abstract interpretation and
decision procedures (SMT-solving), focusing on distinction of paths inside the
control flow graph while avoiding systematic exponential enumerations. It is
parametric in the abstract domain in use, the iteration algorithm, and the
decision procedure. We compared the time and precision of various combinations
of analysis algorithms and abstract domains, with extensive experiments both on
personal benchmarks and widely available GNU programs.Comment: Tools for Automatic Program AnalysiS (TAPAS 2012), Deauville : France
(2012
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