DL‐IDS: a deep learning–based intrusion detection framework for securing IoT
Bibliographic record
Abstract
Abstract The Internet of Things (IoT) is comprised of numerous devices connected through wired or wireless networks, including sensors and actuators. Recently, the number of IoT applications has increased dramatically, including smart homes, vehicular ad hoc network (VANETs), health care, smart cities, and wearables. As reported in IHS Markit (see https://technology.ihs.com ), the number of connected devices is projected to jump from approximately 27 billion in 2017 to 125 billion in 2030, an average annual increment of 12%. Security is a critical issue in today's IoT field because of the nature of the architecture, the types of devices, different methods of communication (mainly wireless), and the volume of data being transmitted over the network. Security becomes even more important as the number of devices connected to the IoT increases. To overcome the challenges of securing IoT devices, we propose a new deep learning–based intrusion detection system (DL‐IDS) to detect security threats in IoT environments. There are many IDSs in the literature, but they lack optimal features learning and data set management, which are significant issues that affect the accuracy of attack detection. Our proposed module combines the spider monkey optimization (SMO) algorithm and the stacked‐deep polynomial network (SDPN) to achieve optimal detection recognition; SMO selects the optimal features in the data sets and SDPN classifies the data as normal or anomalies. The types of anomalies detected by DL‐IDS include denial of service (DoS), user‐to‐root (U2R) attack, probe attack, and remote‐to‐local (R2L) attack. Extensive analysis indicates that the proposed DL‐IDS achieves better performance in terms of accuracy, precision, recall, and F‐score.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".