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Are Accuracy and Robustness Correlated?
, 2016 Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors.
Boult, Terrance E. +2 more
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NUST Journal of Engineering Sciences, 2022 The availability of information and its integrity and confidentiality are important factors in information and communication of the system security. The DDoS attack generally means Distributed denial of services generates many enormous packets to slow ...
Zahid Iqbal +3 more
doaj +1 more source
A Systematic Review of Adversarial Machine Learning Attacks, Defensive Controls, and Technologies
IEEE AccessAdversarial machine learning (AML) attacks have become a major concern for organizations in recent years, as AI has become the industry’s focal point and GenAI applications have grown in popularity around the world.
Jasmita Malik, Raja Muthalagu, P. Pawar
semanticscholar +1 more source
Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains
, 2017 While modern day web applications aim to create impact at the civilization level, they have become vulnerable to adversarial activity, where the next cyber-attack can take any shape and can originate from anywhere. The increasing scale and sophistication
Kantardzic, Mehmed, Sethi, Tegjyot Singh
core +1 more source
Adversarial Machine Learning: A Comparative Study on Contemporary Intrusion Detection Datasets
International Conference on Information Systems Security and Privacy, 2021 Studies have shown the vulnerability of machine learning algorithms against adversarial samples in image classification problems in deep neural networks. However, there is a need for performing comprehensive studies of adversarial machine learning in the
Yulexis Pacheco, Weiqing Sun
semanticscholar +1 more source
Investigation of the impact effectiveness of adversarial data leakage attacks on the machine learning models [PDF]
ITM Web of ConferencesMachine learning solutions have been successfully applied in many aspects, so it is now important to ensure the security of the machine learning models themselves and develop appropriate solutions and approaches.
Parfenov Denis +3 more
doaj +1 more source
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward [PDF]
IEEE Communications Surveys and Tutorials, 2019 Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services.
A. Qayyum +3 more
semanticscholar +1 more source
Breaking Machine Learning Models with Adversarial Attacks and its Variants
Proceedings of the International Florida Artificial Intelligence Research Society ConferenceMachine learning models can be by adversarial attacks, subtle, imperceptible perturbations to inputs that cause the model to produce erroneous outputs.
Pavan Reddy
doaj +1 more source
Future InternetDuring the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems.
Muhammad Imran +2 more
doaj +1 more source
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
Journal of Cybersecurity and Privacy, 2021 As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought.
Pavlos Papadopoulos +5 more
doaj +1 more source