Sökning: "adversarial attacker"

Visar resultat 1 - 5 av 20 uppsatser innehållade orden adversarial attacker.

1. 1. Adversarial robustness of STDP-trained spiking neural networks

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Karl Lindblad; Axel Nilsson; [2023]
   Nyckelord :;

   Sammanfattning : Adversarial attacks on machine learning models are designed to elicit the wrong behavior from the model. One such attack on image classifiers are maliciously crafted inputs that, to the human eye, look untampered with but have been carefully altered to cause misclassification. LÄS MER

3. 2. AI for Cybersecurity : A Study on Machine Learning and DoS Attacks AI Robustness and Bypassing Detection Methods

   Kandidat-uppsats, Högskolan i Halmstad/Akademin för informationsteknologi

   Författare :Molin Matti; Böhme Fredrik; [2023]
   Nyckelord :AI; Cybersecurity; Machine learning; DoS; Poisoning; AI; Cybersäkerhet; Maskininlärning; DoS; Poisoning;

   Sammanfattning : Cybercrime has increased for several years; both in volume andsophistication. When the capabilities of threat actors increase, techniques andtactics within cybersecurity also need to evolve. AI and machine learninghave potential to prevent and mitigate attacks. LÄS MER

4. 3. Anomalous Behavior Detection in Aircraft based Automatic Dependent Surveillance–Broadcast (ADS-B) system using Deep Graph Convolution and Generative model (GA-GAN)

   Magister-uppsats, Linköpings universitet/Databas och informationsteknik

   Författare :Jayesh Kenaudekar; [2022]
   Nyckelord :Intrusion detection aircraft aviation security adsb protocol AI deep learning machine learning graph generative model surveillance broadcast;

   Sammanfattning : The Automatic Dependent Surveillance-Broadcast (ADS-B) is a key component of the Next Generation Air Transportation System (Next Gen) that manages the increasingly congested airspace and operation. From Jan 2020, the U.S. Federal Aviation Administration (FAA) mandated the use of (ADS-B) as a key component of Next Gen project. LÄS MER

5. 4. The Resilience of Deep Learning Intrusion Detection Systems for Automotive Networks : The effect of adversarial samples and transferability on Deep Learning Intrusion Detection Systems for Controller Area Networks

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Ivo Zenden; [2022]
   Nyckelord :Vehicle Security; Deep Learning; Controller Area Network; Intrusion Detection System; Adversarial Samples; Fordonssäkerhet; Deep Learning; Controller Area Network; Intrusion Detection System; kontradiktoriska prover;

   Sammanfattning : This thesis will cover the topic of cyber security in vehicles. Current vehicles contain many computers which communicate over a controller area network. This network has many vulnerabilities which can be leveraged by attackers. To combat these attackers, intrusion detection systems have been implemented. LÄS MER

7. 5. Adversarial Attacks in Federated Learning

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Matteo Demartis; [2022]
   Nyckelord :;

   Sammanfattning : Maskininlärning kräver olika utbildningsdatauppsättningar för att fungera bra. Att dela datauppsättningar är ofta en juridisk fråga och integritetsfråga mellan länder/företag. LÄS MER