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AI for Cybersecurity: Robust models for Authentication, Threat and Anomaly Detection

Cybersecurity models include provisions for legitimate user and agent authentication, as well as algorithms for detecting external threats, such as intruders and malicious software. In particular, we can define a continuum of cybersecurity measures ranging from user identification to risk-based and...

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Bibliographic Details
Main Author: Bergadano, Francesco
Other Authors: Giacinto, Giorgio
Format: eBook
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
Subjects:
Biometric Cryptosystem
Machine Learning
False Negative Rate
Performance Comparison
Risk Identification
Mitre Att&ck Matrix
N/a
Bert-based Multi-labeling
Deep Belief Network (dbn)
Cooperative Intelligent Transportation Systems (citss)
Attack Detection
Deep Learning
Iris Identification
Non-targeted Attack
Mechanical Engineering And Materials / Bicssc
Vulnerability
Neural Networks
History Of Engineering And Technology / Bicssc
Tree-based Ensemble
Adaptive Model
Nids
Technology: General Issues / Bicssc
Vehicular Ad-hoc Networks (vanet)
Datasets
Cyber Threats
Artificial Neural Network
Internet Of Things
Ids
Targeted Attack
Adversarial Examples
Network Traffic
Evaluation Metrics
Smart Grid
Techniques Classification
Cyber-physical System
Malware Detection
Face Recognition
Mask Matrix
Formal Ontology
Statistical Significance Test
Anomaly Detection
Cybersecurity
Portable Executable Malware
Intrusion Detection
Error-correcting Codes
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary:Cybersecurity models include provisions for legitimate user and agent authentication, as well as algorithms for detecting external threats, such as intruders and malicious software. In particular, we can define a continuum of cybersecurity measures ranging from user identification to risk-based and multilevel authentication, complex application and network monitoring, and anomaly detection. We refer to this as the "anomaly detection continuum". Machine learning and other artificial intelligence technologies can provide powerful tools for addressing such issues, but the robustness of the obtained models is often ignored or underestimated. On the one hand, AI-based algorithms can be replicated by malicious opponents, and attacks can be devised so that they will not be detected (evasion attacks). On the other hand, data and system contexts can be modified by attackers to influence the countermeasures obtained from machine learning and render them ineffective (active data poisoning). This Special Issue presents ten papers that can be grouped under five main topics: (1) Cyber-Physical Systems (CPSs), (2) Intrusion Detection, (3) Malware Analysis, (4) Access Control, and (5) Threat intelligence.AI is increasingly being used in cybersecurity, with three main directions of current research: (1) new areas of cybersecurity are being addressed, such as CPS security and threat intelligence; (2) more stable and consistent results are being presented, sometimes with surprising accuracy and effectiveness; and (3) the presence of an AI-aware adversary is recognized and analyzed, producing more robust solutions.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (208 p.)
ISBN:9783036582658
9783036582641
books978-3-0365-8265-8

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