A Computationally Intelligent Approach to the Detection of Wormhole Attacks in Wireless Sensor Networks
Bibliographic record
Abstract
A wormhole attack is one of the most critical and challenging security threats for wireless sensor networks because of its nature and ability to perform concealed malicious activities.This paper proposes an innovative wormhole detection scheme to detect wormhole attacks using computational intelligence and an artificial neural network (ANN).Most wormhole detection schemes reported in the literature assume the sensors are uniformly distributed in a network, and, furthermore, they use statistical and topological information and special hardware for their detection.However, these schemes may perform poorly in non-uniformly distributed networks, and, moreover, they may fail to defend against "out of band" and "in band" wormhole attacks.The aim of the proposed research is to develop a detection scheme that is able to detect all kinds of wormhole attacks in both uniformly and non-uniformly distributed sensor networks.Furthermore, the proposed research does not require any special hardware and causes no significant network overhead throughout the network.Most importantly, the probable location of the malicious nodes can be identified by the proposed ANN based detection scheme.We evaluate the efficacy of the proposed detection scheme in terms of detection accuracy, false positive rate, and false negative rate.The performance of the proposed algorithm is also compared with other machine learning techniques (i.e.SVM and regularized nonlinear logistic regression (LR) based detection models).The simulation results show that proposed ANN based algorithm outperforms the SVM or LR based detection schemes in terms of detection accuracy, false positive rate, and false negative rates.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".