27,365 research outputs found
Face De-Spoofing: Anti-Spoofing via Noise Modeling
Many prior face anti-spoofing works develop discriminative models for
recognizing the subtle differences between live and spoof faces. Those
approaches often regard the image as an indivisible unit, and process it
holistically, without explicit modeling of the spoofing process. In this work,
motivated by the noise modeling and denoising algorithms, we identify a new
problem of face de-spoofing, for the purpose of anti-spoofing: inversely
decomposing a spoof face into a spoof noise and a live face, and then utilizing
the spoof noise for classification. A CNN architecture with proper constraints
and supervisions is proposed to overcome the problem of having no ground truth
for the decomposition. We evaluate the proposed method on multiple face
anti-spoofing databases. The results show promising improvements due to our
spoof noise modeling. Moreover, the estimated spoof noise provides a
visualization which helps to understand the added spoof noise by each spoof
medium.Comment: To appear in ECCV 2018. The first two authors contributed equally to
this wor
Scalable and Secure Architecture for Distributed IoT Systems
Internet-of-things (IoT) is perpetually revolutionizing our daily life and
rapidly transforming physical objects into an ubiquitous connected ecosystem.
Due to their massive deployment and moderate security levels, those devices
face a lot of security, management, and control challenges. Their classical
centralized architecture is still cloaking vulnerabilities and anomalies that
can be exploited by hackers for spying, eavesdropping, and taking control of
the network. In this paper, we propose to improve the IoT architecture with
additional security features using Artificial Intelligence (AI) and blockchain
technology. We propose a novel architecture based on permissioned blockchain
technology in order to build a scalable and decentralized end-to-end secure IoT
system. Furthermore, we enhance the IoT system security with an AI-component at
the gateway level to detect and classify suspected activities, malware, and
cyber-attacks using machine learning techniques. Simulations and practical
implementation show that the proposed architecture delivers high performance
against cyber-attacks.Comment: This paper is accepted for publication in IEEE Technology &
Engineering Management Conference (TEMSCON'20), Detroit, USA, jun, 202
Security for 4G and 5G Cellular Networks: A Survey of Existing Authentication and Privacy-preserving Schemes
This paper presents a comprehensive survey of existing authentication and
privacy-preserving schemes for 4G and 5G cellular networks. We start by
providing an overview of existing surveys that deal with 4G and 5G
communications, applications, standardization, and security. Then, we give a
classification of threat models in 4G and 5G cellular networks in four
categories, including, attacks against privacy, attacks against integrity,
attacks against availability, and attacks against authentication. We also
provide a classification of countermeasures into three types of categories,
including, cryptography methods, humans factors, and intrusion detection
methods. The countermeasures and informal and formal security analysis
techniques used by the authentication and privacy preserving schemes are
summarized in form of tables. Based on the categorization of the authentication
and privacy models, we classify these schemes in seven types, including,
handover authentication with privacy, mutual authentication with privacy, RFID
authentication with privacy, deniable authentication with privacy,
authentication with mutual anonymity, authentication and key agreement with
privacy, and three-factor authentication with privacy. In addition, we provide
a taxonomy and comparison of authentication and privacy-preserving schemes for
4G and 5G cellular networks in form of tables. Based on the current survey,
several recommendations for further research are discussed at the end of this
paper.Comment: 24 pages, 14 figure
Monocular Depth Estimators: Vulnerabilities and Attacks
Recent advancements of neural networks lead to reliable monocular depth
estimation. Monocular depth estimated techniques have the upper hand over
traditional depth estimation techniques as it only needs one image during
inference. Depth estimation is one of the essential tasks in robotics, and
monocular depth estimation has a wide variety of safety-critical applications
like in self-driving cars and surgical devices. Thus, the robustness of such
techniques is very crucial. It has been shown in recent works that these deep
neural networks are highly vulnerable to adversarial samples for tasks like
classification, detection and segmentation. These adversarial samples can
completely ruin the output of the system, making their credibility in real-time
deployment questionable. In this paper, we investigate the robustness of the
most state-of-the-art monocular depth estimation networks against adversarial
attacks. Our experiments show that tiny perturbations on an image that are
invisible to the naked eye (perturbation attack) and corruption less than about
1% of an image (patch attack) can affect the depth estimation drastically. We
introduce a novel deep feature annihilation loss that corrupts the hidden
feature space representation forcing the decoder of the network to output poor
depth maps. The white-box and black-box test compliments the effectiveness of
the proposed attack. We also perform adversarial example transferability tests,
mainly cross-data transferability
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Machine learning promises methods that generalize well from finite labeled
data. However, the brittleness of existing neural net approaches is revealed by
notable failures, such as the existence of adversarial examples that are
misclassified despite being nearly identical to a training example, or the
inability of recurrent sequence-processing nets to stay on track without
teacher forcing. We introduce a method, which we refer to as \emph{state
reification}, that involves modeling the distribution of hidden states over the
training data and then projecting hidden states observed during testing toward
this distribution. Our intuition is that if the network can remain in a
familiar manifold of hidden space, subsequent layers of the net should be well
trained to respond appropriately. We show that this state-reification method
helps neural nets to generalize better, especially when labeled data are
sparse, and also helps overcome the challenge of achieving robust
generalization with adversarial training.Comment: ICML 2019 [full oral]. arXiv admin note: text overlap with
arXiv:1805.0839
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions
Argumentation is a type of discourse where speakers try to persuade their
audience about the reasonableness of a claim by presenting supportive
arguments. Most work in argument mining has focused on modeling arguments in
monologues. We propose a computational model for argument mining in online
persuasive discussion forums that brings together the micro-level (argument as
product) and macro-level (argument as process) models of argumentation.
Fundamentally, this approach relies on identifying relations between components
of arguments in a discussion thread. Our approach for relation prediction uses
contextual information in terms of fine-tuning a pre-trained language model and
leveraging discourse relations based on Rhetorical Structure Theory. We
additionally propose a candidate selection method to automatically predict what
parts of one's argument will be targeted by other participants in the
discussion. Our models obtain significant improvements compared to recent
state-of-the-art approaches using pointer networks and a pre-trained language
model.Comment: EMNLP 201
Malware triage for early identification of Advanced Persistent Threat activities
In the last decade, a new class of cyber-threats has emerged. This new
cybersecurity adversary is known with the name of "Advanced Persistent Threat"
(APT) and is referred to different organizations that in the last years have
been "in the center of the eye" due to multiple dangerous and effective attacks
targeting financial and politic, news headlines, embassies, critical
infrastructures, TV programs, etc. In order to early identify APT related
malware, a semi-automatic approach for malware samples analysis is needed. In
our previous work we introduced a "malware triage" step for a semi-automatic
malware analysis architecture. This step has the duty to analyze as fast as
possible new incoming samples and to immediately dispatch the ones that deserve
a deeper analysis, among all the malware delivered per day in the cyber-space,
the ones that really worth to be further examined by analysts. Our paper
focuses on malware developed by APTs, and we build our knowledge base, used in
the triage, on known APTs obtained from publicly available reports. In order to
have the triage as fast as possible, we only rely on static malware features,
that can be extracted with negligible delay, and use machine learning
techniques for the identification. In this work we move from multiclass
classification to a group of oneclass classifier, which simplify the training
and allows higher modularity. The results of the proposed framework highlight
high performances, reaching a precision of 100% and an accuracy over 95
Non-Negative Networks Against Adversarial Attacks
Adversarial attacks against neural networks are a problem of considerable
importance, for which effective defenses are not yet readily available. We make
progress toward this problem by showing that non-negative weight constraints
can be used to improve resistance in specific scenarios. In particular, we show
that they can provide an effective defense for binary classification problems
with asymmetric cost, such as malware or spam detection. We also show the
potential for non-negativity to be helpful to non-binary problems by applying
it to image classification
A Survey of Stealth Malware: Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions
As our professional, social, and financial existences become increasingly
digitized and as our government, healthcare, and military infrastructures rely
more on computer technologies, they present larger and more lucrative targets
for malware. Stealth malware in particular poses an increased threat because it
is specifically designed to evade detection mechanisms, spreading dormant, in
the wild for extended periods of time, gathering sensitive information or
positioning itself for a high-impact zero-day attack. Policing the growing
attack surface requires the development of efficient anti-malware solutions
with improved generalization to detect novel types of malware and resolve these
occurrences with as little burden on human experts as possible. In this paper,
we survey malicious stealth technologies as well as existing solutions for
detecting and categorizing these countermeasures autonomously. While machine
learning offers promising potential for increasingly autonomous solutions with
improved generalization to new malware types, both at the network level and at
the host level, our findings suggest that several flawed assumptions inherent
to most recognition algorithms prevent a direct mapping between the stealth
malware recognition problem and a machine learning solution. The most notable
of these flawed assumptions is the closed world assumption: that no sample
belonging to a class outside of a static training set will appear at query
time. We present a formalized adaptive open world framework for stealth malware
recognition and relate it mathematically to research from other machine
learning domains.Comment: Pre-Print of a manuscript Accepted to IEEE Communications Surveys and
Tutorials (COMST) on December 1, 201
StreaMon: a data-plane programming abstraction for Software-defined Stream Monitoring
The fast evolving nature of modern cyber threats and network monitoring needs
calls for new, "software-defined", approaches to simplify and quicken
programming and deployment of online (stream-based) traffic analysis functions.
StreaMon is a carefully designed data-plane abstraction devised to scalably
decouple the "programming logic" of a traffic analysis application (tracked
states, features, anomaly conditions, etc.) from elementary primitives
(counting and metering, matching, events generation, etc), efficiently
pre-implemented in the probes, and used as common instruction set for
supporting the desired logic. Multi-stage multi-step real-time tracking and
detection algorithms are supported via the ability to deploy custom states,
relevant state transitions, and associated monitoring actions and triggering
conditions. Such a separation entails platform-independent, portable, online
traffic analysis tasks written in a high level language, without requiring
developers to access the monitoring device internals and program their custom
monitoring logic via low level compiled languages (e.g., C, assembly, VHDL). We
validate our design by developing a prototype and a set of simple (but
functionally demanding) use-case applications and by testing them over real
traffic traces
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