4,223 research outputs found
Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm
Dendritic cells are antigen presenting cells that provide a vital link
between the innate and adaptive immune system, providing the initial detection
of pathogenic invaders. Research into this family of cells has revealed that
they perform information fusion which directs immune responses. We have derived
a Dendritic Cell Algorithm based on the functionality of these cells, by
modelling the biological signals and differentiation pathways to build a
control mechanism for an artificial immune system. We present algorithmic
details in addition to experimental results, when the algorithm was applied to
anomaly detection for the detection of port scans. The results show the
Dendritic Cell Algorithm is sucessful at detecting port scans.Comment: 21 pages, 17 figures, Information Fusio
Unmasking the imposters: towards improving the generalisation of deep learning methods for face presentation attack detection.
Identity theft has had a detrimental impact on the reliability of face recognition, which has been extensively employed in security applications. The most prevalent are presentation attacks. By using a photo, video, or mask of an authorized user, attackers can bypass face recognition systems. Fake presentation attacks are detected by the camera sensors of face recognition systems using face presentation attack detection. Presentation attacks can be detected using convolutional neural networks, commonly used in computer vision applications. An in-depth analysis of current deep learning methods is used in this research to examine various aspects of detecting face presentation attacks. A number of new techniques are implemented and evaluated in this study, including pre-trained models, manual feature extraction, and data aggregation. The thesis explores the effectiveness of various machine learning and deep learning models in improving detection performance by using publicly available datasets with different dataset partitions than those specified in the official dataset protocol. Furthermore, the research investigates how deep models and data aggregation can be used to detect face presentation attacks, as well as a novel approach that combines manual features with deep features in order to improve detection accuracy. Moreover, task-specific features are also extracted using pre-trained deep models to enhance the performance of detection and generalisation further. This problem is motivated by the need to achieve generalization against new and rapidly evolving attack variants. It is possible to extract identifiable features from presentation attack variants in order to detect them. However, new methods are needed to deal with emerging attacks and improve the generalization capability. This thesis examines the necessary measures to detect face presentation attacks in a more robust and generalised manner
Forensic Data Analytics for Anomaly Detection in Evolving Networks
In the prevailing convergence of traditional infrastructure-based deployment
(i.e., Telco and industry operational networks) towards evolving deployments
enabled by 5G and virtualization, there is a keen interest in elaborating
effective security controls to protect these deployments in-depth. By
considering key enabling technologies like 5G and virtualization, evolving
networks are democratized, facilitating the establishment of point presences
integrating different business models ranging from media, dynamic web content,
gaming, and a plethora of IoT use cases. Despite the increasing services
provided by evolving networks, many cybercrimes and attacks have been launched
in evolving networks to perform malicious activities. Due to the limitations of
traditional security artifacts (e.g., firewalls and intrusion detection
systems), the research on digital forensic data analytics has attracted more
attention. Digital forensic analytics enables people to derive detailed
information and comprehensive conclusions from different perspectives of
cybercrimes to assist in convicting criminals and preventing future crimes.
This chapter presents a digital analytics framework for network anomaly
detection, including multi-perspective feature engineering, unsupervised
anomaly detection, and comprehensive result correction procedures. Experiments
on real-world evolving network data show the effectiveness of the proposed
forensic data analytics solution.Comment: Electronic version of an article published as [Book Series: World
Scientific Series in Digital Forensics and Cybersecurity, Volume 2,
Innovations in Digital Forensics, 2023, Pages 99-137]
[DOI:10.1142/9789811273209_0004] \c{opyright} copyright World Scientific
Publishing Company [https://doi.org/10.1142/9789811273209_0004
Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks
Face recognition has evolved as a widely used biometric modality. However,
its vulnerability against presentation attacks poses a significant security
threat. Though presentation attack detection (PAD) methods try to address this
issue, they often fail in generalizing to unseen attacks. In this work, we
propose a new framework for PAD using a one-class classifier, where the
representation used is learned with a Multi-Channel Convolutional Neural
Network (MCCNN). A novel loss function is introduced, which forces the network
to learn a compact embedding for bonafide class while being far from the
representation of attacks. A one-class Gaussian Mixture Model is used on top of
these embeddings for the PAD task. The proposed framework introduces a novel
approach to learn a robust PAD system from bonafide and available (known)
attack classes. This is particularly important as collecting bonafide data and
simpler attacks are much easier than collecting a wide variety of expensive
attacks. The proposed system is evaluated on the publicly available WMCA
multi-channel face PAD database, which contains a wide variety of 2D and 3D
attacks. Further, we have performed experiments with MLFP and SiW-M datasets
using RGB channels only. Superior performance in unseen attack protocols shows
the effectiveness of the proposed approach. Software, data, and protocols to
reproduce the results are made available publicly.Comment: 15 page
Deep Learning for Face Anti-Spoofing: A Survey
Face anti-spoofing (FAS) has lately attracted increasing attention due to its
vital role in securing face recognition systems from presentation attacks
(PAs). As more and more realistic PAs with novel types spring up, traditional
FAS methods based on handcrafted features become unreliable due to their
limited representation capacity. With the emergence of large-scale academic
datasets in the recent decade, deep learning based FAS achieves remarkable
performance and dominates this area. However, existing reviews in this field
mainly focus on the handcrafted features, which are outdated and uninspiring
for the progress of FAS community. In this paper, to stimulate future research,
we present the first comprehensive review of recent advances in deep learning
based FAS. It covers several novel and insightful components: 1) besides
supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also
investigate recent methods with pixel-wise supervision (e.g., pseudo depth
map); 2) in addition to traditional intra-dataset evaluation, we collect and
analyze the latest methods specially designed for domain generalization and
open-set FAS; and 3) besides commercial RGB camera, we summarize the deep
learning applications under multi-modal (e.g., depth and infrared) or
specialized (e.g., light field and flash) sensors. We conclude this survey by
emphasizing current open issues and highlighting potential prospects.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI
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