Cybersecurity, 2020 Deep learning (DL) has exhibited its exceptional performance in fields like intrusion detection. Various augmentation methods have been proposed to improve data quality and eventually to enhance the performance of DL models.
Yixiang Wang +4 more
doaj +1 more source
Synthesis of the common-law accusatorial and the continental mixed criminal procedural justice system: The example of the International Criminal Tribunal for the Former Yugoslavia: Part 2. [PDF]
Pravni Zapisi, 2014 In this paper, the author analyses criminal procedural rules created as an expression of the synthesis of the common-law accusatorial and the continental mixed criminal procedural justice system in International Tribunal for Former Yugoslavia (ICTY). The
Radulović Jovan
doaj
GANG-MAM: GAN based enGine for Modifying Android Malware
SoftwareX, 2022 Malware detectors based on machine learning are vulnerable to adversarial attacks. Generative Adversarial Networks (GAN) are architectures based on Neural Networks that may be used to produce successful adversarial samples.
Renjith G. +4 more
doaj +1 more source
Exploring Diverse Feature Extractions for Adversarial Audio Detection
IEEE Access, 2023 Although deep learning models have exhibited excellent performance in various domains, recent studies have discovered that they are highly vulnerable to adversarial attacks.
Yujin Choi +3 more
doaj +1 more source
Applied SciencesIndustrial control systems (ICSs) are critical components automating the processes and operations of electromechanical systems. These systems are vulnerable to cyberattacks and can be the targets of malicious activities.
Hayriye Tanyıldız +2 more
doaj +1 more source
Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems
, 2019 We show that end-to-end learning of communication systems through deep neural network (DNN) autoencoders can be extremely vulnerable to physical adversarial attacks.
Larsson, Erik G., Sadeghi, Meysam
core +1 more source
Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning
Applied Sciences, 2022 The ever-evolving cybersecurity environment has given rise to sophisticated adversaries who constantly explore new ways to attack cyberinfrastructure. Recently, the use of deep learning-based intrusion detection systems has been on the rise. This rise is
Kudzai Sauka +3 more
doaj +1 more source
Adversarial Learning for Neural Dialogue Generation
, 2017 In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances.
Jean, Sébastien +5 more
core +1 more source
Automatic Design System with Generative Adversarial Network and Convolutional Neural Network for Optimization Design of Interior Permanent Magnet Synchronous Motor [PDF]
, 2021 Yuki Shimizu +3 more
openalex +2 more sources
Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks
, 2018 This work shows that it is possible to fool/attack recent state-of-the-art face detectors which are based on the single-stage networks. Successfully attacking face detectors could be a serious malware vulnerability when deploying a smart surveillance ...
D Chen +5 more
core +1 more source