Search Results for author:
Found 19 papers, 7 papers with code
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
In this paper, we present Kitsune: a plug and play NIDS which can learn to detect attacks on the local network, without supervision, and in an efficient online manner.
CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning
In this paper, we show how an attacker can use deep-learning to add or remove evidence of medical conditions from volumetric (3D) medical scans.
N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders
The proliferation of IoT devices which can be more easily compromised than desktop computers has led to an increase in the occurrence of IoT based botnet attacks.
Lightweight Collaborative Anomaly Detection for the IoT using Blockchain
Due to their rapid growth and deployment, the Internet of things (IoT) have become a central aspect of our daily lives.
Physical Layer Encryption using a Vernam Cipher
In this paper, we present the Vernam Physical Signal Cipher (VPSC): a novel cipher which can encrypt the harmonic composition of any analog waveform.
Cryptography and Security
Transpose Attack: Stealing Datasets with Bidirectional Training
In addition, in this work we show that neural networks can be taught to systematically memorize and retrieve specific samples from datasets.
CIoTA: Collaborative IoT Anomaly Detection via Blockchain
However, an anomaly detection model must be trained for a long time in order to capture all benign behaviors.
Online Budgeted Learning for Classifier Induction
In online learning, the challenge is to find the optimum set of features to be acquired from each instance upon arrival from a data stream.
DANTE: A framework for mining and monitoring darknet traffic
In this paper, we present DANTE: a framework and algorithm for mining darknet traffic.
The Creation and Detection of Deepfakes: A Survey
Generative deep learning algorithms have progressed to a point where it is difficult to tell the difference between what is real and what is fake.
IPatch: A Remote Adversarial Patch
In this paper, we introduce a new type of adversarial patch which alters a model's perception of an image's semantics.
The Threat of Offensive AI to Organizations
Although offensive AI has been discussed in the past, there is a need to analyze and understand the threat in the context of organizations.
The Security of Deep Learning Defences for Medical Imaging
Deep learning has shown great promise in the domain of medical image analysis.
DF-Captcha: A Deepfake Captcha for Preventing Fake Calls
In this paper, we propose a lightweight application which can protect organizations and individuals from deepfake SE attacks.
Transferability Ranking of Adversarial Examples
However, to the best of our knowledge, there are no works which propose a means for ranking the transferability of an adversarial example in the perspective of a blackbox attacker.
Deepfake CAPTCHA: A Method for Preventing Fake Calls
In this work we focus on real-time audio deepfakes and present preliminary results on video.
Discussion Paper: The Threat of Real Time Deepfakes
Generative deep learning models are able to create realistic audio and video.
What Was Your Prompt? A Remote Keylogging Attack on AI Assistants
In this paper, we unveil a novel side-channel that can be used to read encrypted responses from AI Assistants over the web: the token-length side-channel.
Counter-Samples: A Stateless Strategy to Neutralize Black Box Adversarial Attacks
Our paper presents a novel defence against black box attacks, where attackers use the victim model as an oracle to craft their adversarial examples.
