abstract:
  Bayesian network is a complete model for the variables and their
relationships, it can be used to answer probabilistic queries about them. A
Bayesian network can thus be considered a mechanism for automatically applying
Bayes' theorem to complex problems. In the application of Bayesian networks,
most of the work is related to probabilistic inferences. Any variable updating
in any node of Bayesian networks might result in the evidence propagation
across the Bayesian networks. This paper sums up various inference techniques
in Bayesian networks and provide guidance for the algorithm calculation in
probabilistic inference in Bayesian networks.

comment: 14 pages, 9 figures, book chapter

title: Probabilistic Inferences in Bayesian Networks


abstract:
  Bayesian networks are basic graphical models, used widely both in statistics
and artificial intelligence. These statistical models of conditional
independence structure are described by acyclic directed graphs whose nodes
correspond to (random) variables in consideration. A quite important topic is
the learning of Bayesian network structures, which is determining the best
fitting statistical model on the basis of given data. Although there are
learning methods based on statistical conditional independence tests,
contemporary methods are mainly based on maximization of a suitable quality
criterion that evaluates how good the graph explains the occurrence of the
observed data. This leads to a nonlinear combinatorial optimization problem
that is in general NP-hard to solve. In this paper we deal with the complexity
of learning restricted Bayesian network structures, that is, we wish to find
network structures of highest score within a given subset of all possible
network structures. For this, we introduce a new unique algebraic
representative for these structures, called the characteristic imset. We show
that these imsets are always 0-1-vectors and that they have many nice
properties that allow us to simplify long proofs for some known results and to
easily establish new complexity results for learning restricted Bayes network
structures.

authors:
Raymond Hemmecke
Silvia Lindner
Milan Studený

title: Learning restricted Bayesian network structures


abstract:
  Bayesian networks are directed acyclic graphs representing independence
relationships among a set of random variables. A random variable can be
regarded as a set of exhaustive and mutually exclusive propositions. We argue
that there are several drawbacks resulting from the propositional nature and
acyclic structure of Bayesian networks. To remedy these shortcomings, we
propose a probabilistic network where nodes represent unary predicates and
which may contain directed cycles. The proposed representation allows us to
represent domain knowledge in a single static network even though we cannot
determine the instantiations of the predicates before hand. The ability to deal
with cycles also enables us to handle cyclic causal tendencies and to recognize
recursive plans.

comment:
Appears in Proceedings of the Eighth Conference on Uncertainty in
  Artificial Intelligence (UAI1992)

title: A Probabilistic Network of Predicates


abstract:
  Deep Learning (DL) is the most widely used tool in the contemporary field of
computer vision. Its ability to accurately solve complex problems is employed
in vision research to learn deep neural models for a variety of tasks,
including security critical applications. However, it is now known that DL is
vulnerable to adversarial attacks that can manipulate its predictions by
introducing visually imperceptible perturbations in images and videos. Since
the discovery of this phenomenon in 2013~[1], it has attracted significant
attention of researchers from multiple sub-fields of machine intelligence. In
[2], we reviewed the contributions made by the computer vision community in
adversarial attacks on deep learning (and their defenses) until the advent of
year 2018. Many of those contributions have inspired new directions in this
area, which has matured significantly since witnessing the first generation
methods. Hence, as a legacy sequel of [2], this literature review focuses on
the advances in this area since 2018. To ensure authenticity, we mainly
consider peer-reviewed contributions published in the prestigious sources of
computer vision and machine learning research. Besides a comprehensive
literature review, the article also provides concise definitions of technical
terminologies for non-experts in this domain. Finally, this article discusses
challenges and future outlook of this direction based on the literature
reviewed herein and [2].

authors:
Naveed Akhtar
Ajmal Mian
Navid Kardan
Mubarak Shah

comment: 35 pages, 450+ references

title:
Advances in adversarial attacks and defenses in computer vision: A
  survey


abstract:
  A Bayesian network is a graphical model that encodes probabilistic
relationships among variables of interest. When used in conjunction with
statistical techniques, the graphical model has several advantages for data
analysis. One, because the model encodes dependencies among all variables, it
readily handles situations where some data entries are missing. Two, a Bayesian
network can be used to learn causal relationships, and hence can be used to
gain understanding about a problem domain and to predict the consequences of
intervention. Three, because the model has both a causal and probabilistic
semantics, it is an ideal representation for combining prior knowledge (which
often comes in causal form) and data. Four, Bayesian statistical methods in
conjunction with Bayesian networks offer an efficient and principled approach
for avoiding the overfitting of data. In this paper, we discuss methods for
constructing Bayesian networks from prior knowledge and summarize Bayesian
statistical methods for using data to improve these models. With regard to the
latter task, we describe methods for learning both the parameters and structure
of a Bayesian network, including techniques for learning with incomplete data.
In addition, we relate Bayesian-network methods for learning to techniques for
supervised and unsupervised learning. We illustrate the graphical-modeling
approach using a real-world case study.

comment: Added a note on averaging causal models

title: A Tutorial on Learning With Bayesian Networks


abstract:
  Convolutional Neural Networks (CNNs) models are one of the most frequently
used deep learning networks, and extensively used in both academia and
industry. Recent studies demonstrated that adversarial attacks against such
models can maintain their effectiveness even when used on models other than the
one targeted by the attacker. This major property is known as transferability,
and makes CNNs ill-suited for security applications. In this paper, we provide
the first comprehensive study which assesses the robustness of CNN-based models
for computer networks against adversarial transferability. Furthermore, we
investigate whether the transferability property issue holds in computer
networks applications. In our experiments, we first consider five different
attacks: the Iterative Fast Gradient Method (I-FGSM), the Jacobian-based
Saliency Map (JSMA), the Limited-memory Broyden Fletcher Goldfarb Shanno BFGS
(L- BFGS), the Projected Gradient Descent (PGD), and the DeepFool attack. Then,
we perform these attacks against three well- known datasets: the Network-based
Detection of IoT (N-BaIoT) dataset, the Domain Generating Algorithms (DGA)
dataset, and the RIPE Atlas dataset. Our experimental results show clearly that
the transferability happens in specific use cases for the I- FGSM, the JSMA,
and the LBFGS attack. In such scenarios, the attack success rate on the target
network range from 63.00% to 100%. Finally, we suggest two shielding strategies
to hinder the attack transferability, by considering the Most Powerful Attacks
(MPAs), and the mismatch LSTM architecture.

authors:
Ehsan Nowroozi
Yassine Mekdad
Mohammad Hajian Berenjestanaki
Mauro Conti
Abdeslam EL Fergougui

comment: 14 pages

title:
Demystifying the Transferability of Adversarial Attacks in Computer
  Networks


abstract:
  Artificial intelligence (AI) has become a part of everyday conversation and
our lives. It is considered as the new electricity that is revolutionizing the
world. AI is heavily invested in both industry and academy. However, there is
also a lot of hype in the current AI debate. AI based on so-called deep
learning has achieved impressive results in many problems, but its limits are
already visible. AI has been under research since the 1940s, and the industry
has seen many ups and downs due to over-expectations and related
disappointments that have followed.
  The purpose of this book is to give a realistic picture of AI, its history,
its potential and limitations. We believe that AI is a helper, not a ruler of
humans. We begin by describing what AI is and how it has evolved over the
decades. After fundamentals, we explain the importance of massive data for the
current mainstream of artificial intelligence. The most common representations
for AI, methods, and machine learning are covered. In addition, the main
application areas are introduced. Computer vision has been central to the
development of AI. The book provides a general introduction to computer vision,
and includes an exposure to the results and applications of our own research.
Emotions are central to human intelligence, but little use has been made in AI.
We present the basics of emotional intelligence and our own research on the
topic. We discuss super-intelligence that transcends human understanding,
explaining why such achievement seems impossible on the basis of present
knowledge,and how AI could be improved. Finally, a summary is made of the
current state of AI and what to do in the future. In the appendix, we look at
the development of AI education, especially from the perspective of contents at
our own university.

authors:
Matti Pietikäinen
Olli Silven

comment:
234 pages. Published as an electronic publication at the University
  of Oulu, Finland, in December 2021, ISBN: 978-952-62-3199-0 link
  http://jultika.oulu.fi/Record/isbn978-952-62-3199-0

title:
Challenges of Artificial Intelligence -- From Machine Learning and
  Computer Vision to Emotional Intelligence


abstract:
  Network security has become an area of significant importance more than ever
as highlighted by the eye-opening numbers of data breaches, attacks on critical
infrastructure, and malware/ransomware/cryptojacker attacks that are reported
almost every day. Increasingly, we are relying on networked infrastructure and
with the advent of IoT, billions of devices will be connected to the internet,
providing attackers with more opportunities to exploit. Traditional machine
learning methods have been frequently used in the context of network security.
However, such methods are more based on statistical features extracted from
sources such as binaries, emails, and packet flows.
  On the other hand, recent years witnessed a phenomenal growth in computer
vision mainly driven by the advances in the area of convolutional neural
networks. At a glance, it is not trivial to see how computer vision methods are
related to network security. Nonetheless, there is a significant amount of work
that highlighted how methods from computer vision can be applied in network
security for detecting attacks or building security solutions. In this paper,
we provide a comprehensive survey of such work under three topics; i) phishing
attempt detection, ii) malware detection, and iii) traffic anomaly detection.
Next, we review a set of such commercial products for which public information
is available and explore how computer vision methods are effectively used in
those products. Finally, we discuss existing research gaps and future research
directions, especially focusing on how network security research community and
the industry can leverage the exponential growth of computer vision methods to
build much secure networked systems.

authors:
Jiawei Zhao
Rahat Masood
Suranga Seneviratne

title: A Review of Computer Vision Methods in Network Security