Inter-Slice Defender: An Anomaly Detection Solution for Distributed Slice Mobility Attacks
Bibliographic record
Abstract
With the evolution of Fifth Generation (5G) technology, network slicing has become a key enabler, providing flexibility and efficiency in segmenting the network to enhance service delivery. This technology enables User Equipment (UEs) to simultaneously connect or switch between Network Slices (NSs) to get access to multiple services with guaranteed quality of service. Nonetheless, switching between NSs, also known as Inter-Slice-Switching (ISS), can be maliciously exploited by attackers to cause a Distributed Slice Mobility (DSM) attack. DSM attack is a distributed denial of service attack that can disrupt both NSs and the 5G control plane. In this work, we develop Inter-slice defender, a novel Long ShortTerm Memory (LSTM)-Autoencoder-based anomaly detection solution, tailored to detect DSM attacks. Inter-slice Defender leverages Third Generation Partnership (3GPP) Key Performance Indicators (KPIs) and Performance Measurement (PM) counters to detect two variations of the DSM attack that we devise. Our experimental results are based on DSM attacks simulations performed on a 5G testbed employing the opensource Free5GC testbed and UERANSIM simulator. They show that Inter-slice defender achieves an average F1-score of 98.75%, demonstrating its robustness in detecting these sophisticated attacks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".