Predicting Sequential Network Attacks Using Hidden Markov Model - MATLAB Code
Bibliographic record
Abstract
This code reproduces the results for the research paper titled Contemporary Sequential Network Attacks Prediction Using Hidden Markov Model by Timothy Chadza, Konstantinos Kyriakopoulos & Sangarapillai Lambotharan presented at the 17th International Conference on Privacy, Security and Trust (PST), IEEE, Fredericton, NB, Canada, 2019. This is published under GNU GENERAL PUBLIC LICENSE Version 3. If you use this code, please, cite the above paper. This work is on the evaluation of detection accuracy for determining all states, the current state, and the prediction of next state of an observation sequence, using the two conventional hidden Markov model training algorithms, namely, Baum Welch and Viterbi training. The training algorithms are initialised using uniform, random and count-based parameters. The experimental evaluation is conducted on the CSE-CIC-IDS2018, a modern dataset comprising seven different attack scenarios over a large network environment. The different attacks are sequentially aggregated to constitute an attack sequence. Viterbi decoding has been used to estimate the next state upon computation of the next attack manifestation. The code is run by executing the main.m file in MATLAB.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.112 | 0.042 |
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".