Ruling the Unruly: Designing Effective, Low-Noise Network Intrusion Detection Rules for Security Operations Centers
January 16, 2025 Β· Declared Dead Β· π ACM Asia Conference on Computer and Communications Security
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Koen T. W. Teuwen, Tom Mulders, Emmanuele Zambon, Luca Allodi
arXiv ID
2501.09808
Category
cs.CR: Cryptography & Security
Cross-listed
cs.NI
Citations
3
Venue
ACM Asia Conference on Computer and Communications Security
Last Checked
5 months ago
Abstract
Many Security Operations Centers (SOCs) today still heavily rely on signature-based Network Intrusion Detection Systems (NIDS) such as Suricata. The specificity of intrusion detection rules and the coverage provided by rulesets are common concerns within the professional community surrounding SOCs, which impact the effectiveness of automated alert post-processing approaches. We postulate a better understanding of factors influencing the quality of rules can help address current SOC issues. In this paper, we characterize the rules in use at a collaborating commercial (managed) SOC serving customers in sectors including education and IT management. During this process, we discover six relevant design principles, which we consolidate through interviews with experienced rule designers at the SOC.We then validate our design principles by quantitatively assessing their effect on rule specificity. We find that several of these design considerations significantly impact unnecessary workload caused by rules. For instance, rules that leverage proxies for detection, and rules that do not employ alert throttling or do not distinguish (un)successful malicious actions, cause significantly more workload for SOC analysts. Moreover, rules that match a generalized characteristic to detect malicious behavior, which is believed to increase coverage, also significantly increase workload, suggesting a tradeoff must be struck between rule specificity and coverage. We show that these design principles can be applied successfully at a SOC to reduce workload whilst maintaining coverage despite the prevalence of violations of the principles.
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