690 research outputs found
Advanced Machine Learning Techniques and Meta-Heuristic Optimization for the Detection of Masquerading Attacks in Social Networks
According to the report published by the online protection firm Iovation in 2012,
cyber fraud ranged from 1 percent of the Internet transactions in North America
Africa to a 7 percent in Africa, most of them involving credit card fraud, identity
theft, and account takeover or hÂĽacking attempts. This kind of crime is still growing
due to the advantages offered by a non face-to-face channel where a increasing
number of unsuspecting victims divulges sensitive information. Interpol classifies
these illegal activities into 3 types:
• Attacks against computer hardware and software.
• Financial crimes and corruption.
• Abuse, in the form of grooming or “sexploitation”.
Most research efforts have been focused on the target of the crime developing different
strategies depending on the casuistic. Thus, for the well-known phising, stored
blacklist or crime signals through the text are employed eventually designing adhoc
detectors hardly conveyed to other scenarios even if the background is widely
shared. Identity theft or masquerading can be described as a criminal activity oriented
towards the misuse of those stolen credentials to obtain goods or services by
deception. On March 4, 2005, a million of personal and sensitive information such
as credit card and social security numbers was collected by White Hat hackers at
Seattle University who just surfed the Web for less than 60 minutes by means of
the Google search engine. As a consequence they proved the vulnerability and lack
of protection with a mere group of sophisticated search terms typed in the engine
whose large data warehouse still allowed showing company or government websites
data temporarily cached.
As aforementioned, platforms to connect distant people in which the interaction is
undirected pose a forcible entry for unauthorized thirds who impersonate the licit
user in a attempt to go unnoticed with some malicious, not necessarily economic,
interests. In fact, the last point in the list above regarding abuses has become a
major and a terrible risk along with the bullying being both by means of threats,
harassment or even self-incrimination likely to drive someone to suicide, depression
or helplessness. California Penal Code Section 528.5 states:
“Notwithstanding any other provision of law, any person who knowingly
and without consent credibly impersonates another actual person through
or on an Internet Web site or by other electronic means for purposes of
harming, intimidating, threatening, or defrauding another person is guilty
of a public offense punishable pursuant to subdivision [...]”.
IV
Therefore, impersonation consists of any criminal activity in which someone assumes
a false identity and acts as his or her assumed character with intent to get
a pecuniary benefit or cause some harm. User profiling, in turn, is the process of
harvesting user information in order to construct a rich template with all the advantageous
attributes in the field at hand and with specific purposes. User profiling is
often employed as a mechanism for recommendation of items or useful information
which has not yet considered by the client. Nevertheless, deriving user tendency or
preferences can be also exploited to define the inherent behavior and address the
problem of impersonation by detecting outliers or strange deviations prone to entail
a potential attack.
This dissertation is meant to elaborate on impersonation attacks from a profiling
perspective, eventually developing a 2-stage environment which consequently embraces
2 levels of privacy intrusion, thus providing the following contributions:
• The inference of behavioral patterns from the connection time traces aiming at
avoiding the usurpation of more confidential information. When compared to
previous approaches, this procedure abstains from impinging on the user privacy
by taking over the messages content, since it only relies on time statistics
of the user sessions rather than on their content.
• The application and subsequent discussion of two selected algorithms for the
previous point resolution:
– A commonly employed supervised algorithm executed as a binary classifier
which thereafter has forced us to figure out a method to deal with the
absence of labeled instances representing an identity theft.
– And a meta-heuristic algorithm in the search for the most convenient parameters
to array the instances within a high dimensional space into properly
delimited clusters so as to finally apply an unsupervised clustering
algorithm.
• The analysis of message content encroaching on more private information but
easing the user identification by mining discriminative features by Natural
Language Processing (NLP) techniques. As a consequence, the development of
a new feature extraction algorithm based on linguistic theories motivated by
the massive quantity of features often gathered when it comes to texts.
In summary, this dissertation means to go beyond typical, ad-hoc approaches
adopted by previous identity theft and authorship attribution research. Specifically
it proposes tailored solutions to this particular and extensively studied paradigm
with the aim at introducing a generic approach from a profiling view, not tightly
bound to a unique application field. In addition technical contributions have been
made in the course of the solution formulation intending to optimize familiar methods
for a better versatility towards the problem at hand. In summary: this Thesis
establishes an encouraging research basis towards unveiling subtle impersonation
attacks in Social Networks by means of intelligent learning techniques
A Systematic Literature Review of Quantum Computing for Routing Problems
Quantum Computing is drawing a significant attention from the current scientific community. The potential advantages offered by this revolutionary paradigm has led to an upsurge of scientific production in different fields such as economics, industry, or logistics. The main purpose of this paper is to collect, organize and systematically examine the literature published so far on the application of Quantum Computing to routing problems. To do this, we embrace the well-established procedure named as Systematic Literature Review. Specifically, we provide a unified, self-contained, and end-to-end review of 18 years of research (from 2004 to 2021) in the intersection of Quantum Computing and routing problems through the analysis of 53 different papers. Several interesting conclusions have been drawn from this analysis, which has been formulated to give a comprehensive summary of the current state of the art by providing answers related to the most recurrent type of study (practical or theoretical), preferred solving approaches (dedicated or hybrid), detected open challenges or most used Quantum Computing device, among others
Hybrid classical-quantum computing: are we forgetting the classical part in the binomial?
The expectations arising from the latest achievements in the quantum
computing field are causing that researchers coming from classical artificial
intelligence to be fascinated by this new paradigm. In turn, quantum computing,
on the road towards usability, needs classical procedures. Hybridization is, in
these circumstances, an indispensable step but can also be seen as a promising
new avenue to get the most from both computational worlds. Nonetheless, hybrid
approaches have now and will have in the future many challenges to face, which,
if ignored, will threaten the viability or attractiveness of quantum computing
for real-world applications. To identify them and pose pertinent questions, a
proper characterization of the hybrid quantum computing field, and especially
hybrid solvers, is compulsory. With this motivation in mind, the main purpose
of this work is to propose a preliminary taxonomy for classifying hybrid
schemes, and bring to the fore some questions to stir up researchers minds
about the real challenges regarding the application of quantum computing.Comment: 2 pages, 1 figure, paper accepted for being presented in the upcoming
IEEE International Conference on Quantum Computing and Engineering - IEEE QCE
202
Solving Logistic-Oriented Bin Packing Problems Through a Hybrid Quantum-Classical Approach
The Bin Packing Problem is a classic problem with wide industrial
applicability. In fact, the efficient packing of items into bins is one of the
toughest challenges in many logistic corporations and is a critical issue for
reducing storage costs or improving vehicle space allocation. In this work, we
resort to our previously published quantum-classical framework known as
Q4RealBPP, and elaborate on the solving of real-world oriented instances of the
Bin Packing Problem. With this purpose, this paper gravitates on the following
characteristics: i) the existence of heterogeneous bins, ii) the extension of
the framework to solve not only three-dimensional, but also one- and
two-dimensional instances of the problem, iii) requirements for item-bin
associations, and iv) delivery priorities. All these features have been tested
in this paper, as well as the ability of Q4RealBPP to solve real-world oriented
instances.Comment: 7 pages, 7 figures, paper accepted for being presented in the
upcoming 26th IEEE International Conference on Intelligent Transportation
Systems - ITSC 202
Hybrid Approach for Solving Real-World Bin Packing Problem Instances Using Quantum Annealers
Efficient packing of items into bins is a common daily task. Known as Bin
Packing Problem, it has been intensively studied in the field of artificial
intelligence, thanks to the wide interest from industry and logistics. Since
decades, many variants have been proposed, with the three-dimensional Bin
Packing Problem as the closest one to real-world use cases. We introduce a
hybrid quantum-classical framework for solving real-world three-dimensional Bin
Packing Problems (Q4RealBPP), considering different realistic characteristics,
such as: i) package and bin dimensions, ii) overweight restrictions, iii)
affinities among item categories and iv) preferences for item ordering.
Q4RealBPP permits the solving of real-world oriented instances of 3dBPP,
contemplating restrictions well appreciated by industrial and logistics
sectors.Comment: 9 pages, 24 figure
Comparative Benchmark of a Quantum Algorithm for the Bin Packing Problem
The Bin Packing Problem (BPP) stands out as a paradigmatic combinatorial
optimization problem in logistics. Quantum and hybrid quantum-classical
algorithms are expected to show an advantage over their classical counterparts
in obtaining approximate solutions for optimization problems. We have recently
proposed a hybrid approach to the one dimensional BPP in which a quantum
annealing subroutine is employed to sample feasible solutions for single
containers. From this reduced search space, a classical optimization subroutine
can find the solution to the problem. With the aim of going a step further in
the evaluation of our subroutine, in this paper we compare the performance of
our procedure with other classical approaches. Concretely we test a random
sampling and a random-walk-based heuristic. Employing a benchmark comprising 18
instances, we show that the quantum approach lacks the stagnation behaviour
that slows down the classical algorithms. Based on this, we conclude that the
quantum strategy can be employed jointly with the random walk to obtain a full
sample of feasible solutions in fewer iterations. This work improves our
intuition about the benefits of employing the scarce quantum resources to
improve the results of a diminishingly efficient classical strategy.Comment: 8 pages, 2 figures, submitted to the IEEE Symposium Series On
Computational Intelligence 202
Using Offline Data to Speed-up Reinforcement Learning in Procedurally Generated Environments
One of the key challenges of Reinforcement Learning (RL) is the ability of
agents to generalise their learned policy to unseen settings. Moreover,
training RL agents requires large numbers of interactions with the environment.
Motivated by the recent success of Offline RL and Imitation Learning (IL), we
conduct a study to investigate whether agents can leverage offline data in the
form of trajectories to improve the sample-efficiency in procedurally generated
environments. We consider two settings of using IL from offline data for RL:
(1) pre-training a policy before online RL training and (2) concurrently
training a policy with online RL and IL from offline data. We analyse the
impact of the quality (optimality of trajectories) and diversity (number of
trajectories and covered level) of available offline trajectories on the
effectiveness of both approaches. Across four well-known sparse reward tasks in
the MiniGrid environment, we find that using IL for pre-training and
concurrently during online RL training both consistently improve the
sample-efficiency while converging to optimal policies. Furthermore, we show
that pre-training a policy from as few as two trajectories can make the
difference between learning an optimal policy at the end of online training and
not learning at all. Our findings motivate the widespread adoption of IL for
pre-training and concurrent IL in procedurally generated environments whenever
offline trajectories are available or can be generated.Comment: Presented at the Adaptive and Learning Agents Workshop (ALA) at the
AAMAS conference 202
Digital Quantum Simulation and Circuit Learning for the Generation of Coherent States
Coherent states, known as displaced vacuum states, play an important role in quantum information processing, quantum machine learning, and quantum optics. In this article, two ways to digitally prepare coherent states in quantum circuits are introduced. First, we construct the displacement operator by decomposing it into Pauli matrices via ladder operators, i.e., creation and annihilation operators. The high fidelity of the digitally generated coherent states is verified compared with the Poissonian distribution in Fock space. Secondly, by using Variational Quantum Algorithms, we choose different ansatzes to generate coherent states. The quantum resources—such as numbers of quantum gates, layers and iterations—are analyzed for quantum circuit learning. The simulation results show that quantum circuit learning can provide high fidelity on learning coherent states by choosing appropriate ansatzes.This research is funded by the QUANTEK project (ELKARTEK program from the Basque Government, expedient No. KK-2021/00070), the project “BRTA QUANTUM: Hacia una especializaciĂłn armonizada en tecnologĂas cuánticas en BRTA” (expedient No. KK-2022/00041)
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