Extending Attack Graphs to Represent Cyber-Attacks in Communication Protocols and Modern IT Networks
June 24, 2019 Β· Declared Dead Β· π IEEE Transactions on Dependable and Secure Computing
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Orly Stan, Ron Bitton, Michal Ezrets, Moran Dadon, Masaki Inokuchi, Yoshinobu Ohta, Yoshiyuki Yamada, Tomohiko Yagyu, Yuval Elovici, Asaf Shabtai
arXiv ID
1906.09786
Category
cs.CR: Cryptography & Security
Citations
44
Venue
IEEE Transactions on Dependable and Secure Computing
Last Checked
6 months ago
Abstract
An attack graph is a method used to enumerate the possible paths that an attacker can execute in the organization network. MulVAL is a known open-source framework used to automatically generate attack graphs. MulVAL's default modeling has two main shortcomings. First, it lacks the representation of network protocol vulnerabilities, and thus it cannot be used to model common network attacks such as ARP poisoning, DNS spoofing, and SYN flooding. Second, it does not support advanced types of communication such as wireless and bus communication, and thus it cannot be used to model cyber-attacks on networks that include IoT devices or industrial components. In this paper, we present an extended network security model for MulVAL that: (1) considers the physical network topology, (2) supports short-range communication protocols (e.g., Bluetooth), (3) models vulnerabilities in the design of network protocols, and (4) models specific industrial communication architectures. Using the proposed extensions, we were able to model multiple attack techniques including: spoofing, man-in-the-middle, and denial of service, as well as attacks on advanced types of communication. We demonstrate the proposed model on a testbed implementing a simplified network architecture comprised of both IT and industrial components.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted