Results 51 to 60 of about 1,185,392 (332)

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

Open Access Research Journal of Science and Technology
The abstract is "The rapid evolution of cyber threats necessitates innovative defenses, particularly in the domains of risk assessment and fraud detection.
Idoko Peter, Onuh Matthew Ijiga, Idoko Peter Idoko, Godslove Isenyo, Ebiega, F. I. Olajide, Timilehin Isaiah Olatunde, Chukwunonso Ukaegbu   +7 more
semanticscholar   +1 more source

Adversarial support vector machine learning [PDF]

Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012
Many learning tasks such as spam filtering and credit card fraud detection face an active adversary that tries to avoid detection. For learning problems that deal with an active adversary, it is important to model the adversary's attack strategy and develop robust learning models to mitigate the attack. These are the two objectives of this paper.
Yan Zhou, Murat Kantarcioglu, Bhavani Thuraisingham, Bowei Xi   +3 more
openaire   +1 more source

Denial of Service (DoS) Defences against Adversarial Attacks in IoT Smart Home Networks using Machine Learning Methods

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, Azhar Imran, Amanullah Yasin , Adnan Alvi   +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, Muhammad Usama, Junaid Qadir, Ala I. Al-Fuqaha   +3 more
semanticscholar   +1 more source

ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems [PDF]

ACM Asia Conference on Computer and Communications Security, 2020
Recent research demonstrated that the superficially well-trained machine learning (ML) models are highly vulnerable to adversarial examples. As ML techniques are becoming a popular solution for cyber-physical systems (CPSs) applications in research ...
Jiangnan Li, Jin Young Lee, Yingyuan Yang, Jinyuan Sun, K. Tomsovic   +4 more
semanticscholar   +1 more source

A Systematic Review of Adversarial Machine Learning Attacks, Defensive Controls, and Technologies

IEEE Access
Adversarial 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

Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning

Algorithms, 2021
Cyber security is used to protect and safeguard computers and various networks from ill-intended digital threats and attacks. It is getting more difficult in the information age due to the explosion of data and technology.
S. Patil, V. Vijayakumar, D. Walimbe, Siddharth Gulechha, Sushant Shenoy, Aditya Raina, K. Kotecha   +6 more
semanticscholar   +1 more source

Adversarial attacks on medical machine learning

Science, 2019
Emerging vulnerabilities demand new ...
Finlayson, Samuel G., Bowers, John D., Ito, Joichi, Zittrain, Jonathan L., Beam, Andrew L., Kohane, Isaac S.   +5 more
openaire   +4 more sources

Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics

Advanced Materials, EarlyView.
An active learning framework is introduced for the accelerated discovery of high‐entropy chalcogenides with superior thermoelectric performance. Only 80 targeted syntheses, selected from 16206 possible combinations, led to three high‐performance compositions, demonstrating the remarkable efficiency of data‐driven guidance in experimental materials ...
Hanhwi Jang, Wooseok Lee, Hwa‐Jung Kim, Sohyang Cha, Hosun Shin, Won Bo Lee, Min‐Wook Oh, Yeon Sik Jung, YongJoo Kim   +8 more
wiley   +1 more source

Adversarial Controls for Scientific Machine Learning

ACS Chemical Biology, 2018
New machine learning methods to analyze raw chemical and biological data are now widely accessible as open-source toolkits. This positions researchers to leverage powerful, predictive models in their own domains. We caution, however, that the application of machine learning to experimental research merits careful consideration.
Kangway V. Chuang, Michael J. Keiser
openaire   +4 more sources