Title
Machine Learning Cyberattack and Defense Strategies
Abstract
Cybersecurity is an increasingly important challenge for computer systems. In this work, cyberattacks were modeled using an extension of the well-known Petri net formalism. That formalism, designated Petri nets with players, strategies, and costs, models the states of the cyberattack and events during the attack as markings and transition firings in the net respectively. The formalism models the attacker and defender as competing players who may observe the marking of a subset of the net and based on the observed marking act by changing the stochastic firing rates of a subset of the transitions in order to achieve their competing goals. Rate changes by the players incur a cost. Using the formalism, nets were constructed to model specific cyberattack patterns (cross-site scripting and spear phishing) documented in the Common Attack Pattern Enumeration and Classification database. The models were validated by a panel of cybersecurity experts in a structured face validation process.
Year
DOI
Venue
2020
10.1016/j.cose.2020.101738
Computers & Security
Keywords
DocType
Volume
Cybersecurity,Modeling,Petri Net,Machine Learning,CAPEC,Reinforcement Learning
Journal
92
ISSN
Citations 
PageRank 
0167-4048
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
John A. Bland100.34
Mikel D. Petty26812.54
Tymaine S. Whitaker300.34
Katia P. Maxwell400.34
Walter Alan Cantrell500.34