AI for Cybersecurity: Robust models for Authentication, Threat and Anomaly Detection DOI
Francesco Bergadano, Giorgio Giacinto, Olga Tushkanova

et al.

MDPI eBooks, Journal Year: 2023, Volume and Issue: unknown

Published: July 21, 2023

compare different ensemble learning methods that have been proposed in this context: Random Forests, XGBoost, CatBoost, GBM, and LightGBM.Experiments were performed on datasets, finding tree-based algorithms can achieve good performance with limited variability. Access Control [7,8]As stated above, access control be viewed as another point the anomaly detection continuum.Again, distinguishing a legitimate user from impostors automated through machine learning.The seventh paper [7] addresses context of face recognition systems (FRSs) proposes practical white box adversarial attack algorithm.The method is evaluated CASIA WebFace LFW datasets.In [8], authors used user's iris image, combined secret key, to generate public key subsequently use such data limit protected resources. Threat Intelligence [9,10]Not only do we want recognize block attacks they occur-we also need observe external overall network predict relevant events new patterns, addressing so-called threat intelligence landscape.In [9], two well-known databases (CVE MITRE) technique link correlate these sources.The tenth [10] formal ontologies monitor threats identify corresponding risks an way. ConclusionsIn conclusion, observed AI increasingly being cybersecurity, three main directions current research: (1) areas cybersecurity are addressed, CPS security intelligence; (2) more stable consistent results presented, sometimes surprising accuracy effectiveness; (3) presence AI-aware adversary recognized analyzed, producing robust reliable solutions.

Language: Английский

An Integrated Approach for Intrusion Detection in Intelligent Grid Computing Networks Using Machine Learning DOI Open Access

K. Meenakshi,

Dhandapani Raju,

Channabasamma Arandi

et al.

Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications, Journal Year: 2024, Volume and Issue: 15(4), P. 313 - 324

Published: Dec. 12, 2024

Intelligent Grid (IG) systems improve the usability of old energy networks, but they can still be hacked in many ways. Intruders get into system through these holes, risking IG networks' safety and privacy. An Intrusion Detection System (IDS) keeps services safe secure an setting. With help Machine Learning (ML) techniques characteristics, this work shows IDS for platforms. The categorization algorithm comprises a Convolutional Neural Network (CNN) Gated Recurrent Unit (GRU). research uses Precision, Detecting Rate (IDR), False Alarming Ratio (FAR) to rate how well suggested approach works. It turns out that Random Forest (RF) (NN) algorithms did outperform others. study found KDD-99 records had Alarm 7.29%, NSL-KDD FAR 7.31%. 88.68% time, both methods find things, 90.87% confirm are correct.

Language: Английский

Citations

0
Special Issue “AI for Cybersecurity: Robust Models for Authentication, Threat and Anomaly Detection” DOI Creative Commons
Francesco Bergadano, Giorgio Giacinto

Algorithms, Journal Year: 2023, Volume and Issue: 16(7), P. 327 - 327

Published: July 7, 2023

Cybersecurity models include provisions for legitimate user and agent authentication, as well algorithms detecting external threats, such intruders malicious software [...]

Language: Английский

Citations

0
AI for Cybersecurity: Robust models for Authentication, Threat and Anomaly Detection DOI
Francesco Bergadano, Giorgio Giacinto, Olga Tushkanova

et al.

MDPI eBooks, Journal Year: 2023, Volume and Issue: unknown

Published: July 21, 2023

compare different ensemble learning methods that have been proposed in this context: Random Forests, XGBoost, CatBoost, GBM, and LightGBM.Experiments were performed on datasets, finding tree-based algorithms can achieve good performance with limited variability. Access Control [7,8]As stated above, access control be viewed as another point the anomaly detection continuum.Again, distinguishing a legitimate user from impostors automated through machine learning.The seventh paper [7] addresses context of face recognition systems (FRSs) proposes practical white box adversarial attack algorithm.The method is evaluated CASIA WebFace LFW datasets.In [8], authors used user's iris image, combined secret key, to generate public key subsequently use such data limit protected resources. Threat Intelligence [9,10]Not only do we want recognize block attacks they occur-we also need observe external overall network predict relevant events new patterns, addressing so-called threat intelligence landscape.In [9], two well-known databases (CVE MITRE) technique link correlate these sources.The tenth [10] formal ontologies monitor threats identify corresponding risks an way. ConclusionsIn conclusion, observed AI increasingly being cybersecurity, three main directions current research: (1) areas cybersecurity are addressed, CPS security intelligence; (2) more stable consistent results presented, sometimes surprising accuracy effectiveness; (3) presence AI-aware adversary recognized analyzed, producing robust reliable solutions.

Language: Английский

Citations

0