Launch Intel
Gr

Graph-Based ML for Detecting Anomalies in SELinux Policies

Hacker News

Graph-Based ML for Detecting Anomalies in SELinux Policies

We're working on a way to tackle the complexity of SELinux policies by using graph-based machine learning to automate policy analysis and anomaly detection. SELinux enforces security through strict access controls, but the intricacy of its policies makes it hard to manage and troubleshoot effectively. Our approach represents policies as graphs and uses node embedding (like Node2Vec) to train models for identifying policy violations more efficiently. SELinux has become widely adopted for security on Linux systems, but misconfigured policies can lead to unauthorized actions and security issues. Our method aims to make it easier for admins to detect and adjust misconfigurations, potentially reducing vulnerability risks. Early results are promising, and we’re excited about the potential to make SELinux management more intuitive. IEEE ICIR (2024) - KJ

Visit productView on Hacker NewsNov 14, 2024
Share card

Actual performance

1points
1comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting
56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive, efficient · Missing: plus, platform, reviews
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: mac, model, models · Missing: agents, macos, agent
34%34% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

ON
ONNX-Based ML Deployments52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ONNX-Based ML Deployments

Hacker News2
Ch
Changewatch – see what’s changed between versions of policies43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Changewatch – see what’s changed between versions of policies

Hacker News3
Ba
Basic ML Platform based on Pulumi, DVC, MLFlow and more50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Basic ML Platform based on Pulumi, DVC, MLFlow and more

Hacker News3
Gr
Graph-Based Dungeon Generator81%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Graph-Based Dungeon Generator

Hacker News2
Eg
Egsis: Exploratory Graph-Based Semi-Supervised Image Segmentation36%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Egsis: Exploratory Graph-Based Semi-Supervised Image Segmentation

Hacker News2
El
EliasDB a graph based database in Go66%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

EliasDB a graph based database in Go

Hacker News6
Gr
GraphQL+-, a Variant of GraphQL for Graph DBs76%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

GraphQL+-, a Variant of GraphQL for Graph DBs

Hacker News7
El
Election Lie Graph32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Election Lie Graph

Hacker News2
We
Weighted # of Lies from Each Candidate (Graph)51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Weighted # of Lies from Each Candidate (Graph)

Hacker News3
Mo
MongoDB + GraphViz = mongo-graph57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

MongoDB + GraphViz = mongo-graph

Hacker News14
Browse all productsAnalyze your launch