This paper evaluates an ultra-compact five-feature polling scheme (F1–F5) designed to preserve statistical symmetry between control-plane monitoring and telemetry overhead within a dynamic Mininet–Ryu testbed and reveals that tree-based ensembles consistently outperform deep learning approaches.
Abstract
Software-Defined Networking (SDN) provides fine-grained control over network architectures, yet integrating intrusion detection systems (IDSs) into the control plane frequently introduces prohibitive computational overhead. This issue is compounded by the fact that existing machine learning models, typically trained on static benchmark datasets, often degrade under real-time polling conditions and unpredictable traffic bursts. To bridge this gap, this paper evaluates an ultra-compact five-feature polling scheme (F1–F5) designed to preserve statistical symmetry between control-plane monitoring and telemetry overhead within a dynamic Mininet–Ryu testbed. The experimental framework incorporates 15% background noise, and a 10% stealth attack overlaps across a 120 s dynamic trace. Four distinct classifiers—Random Forest (RF), Decision Tree (DT), Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM)—were evaluated across frame-by-frame snapshot and windowed prediction tasks. Empirical findings reveal that tree-based ensembles consistently outperform deep learning approaches, with RF attaining an overall accuracy of 97.57% and DT achieving 96.74%, compared to 90.77% for MLP and 90.73% for LSTM. Analysis of the time-series logs demonstrates that RF’s orthogonal decision boundaries successfully isolate transient, high-intensity threats such as WebAttack and PortScan vectors without needing memory-intensive recurrent architectures. Ultimately, pairing minimal feature extraction with lightweight tree ensembles offers an optimal balance between low control-plane latency and high detection efficacy.
Controller Area Network (CAN) is the dominant in-vehicle bus, yet its broadcast design and absent authentication leave it exposed to injection and spoofing attacks. Existing deep-learning intrusion detection systems achieve strong accuracy but depend on ML inference frameworks incompatible with the resource budgets of production Electronic Control Units (ECUs). We present a systems-oriented pipeline that trains four neural architectures—Transformer, CNN, LSTM, and ANN—on a five-class labeled CAN dataset and distills each into a framework-free C++ decision tree via the Trustee protocol. We observe and address a systematic false-alarm collapse on Normal-class traffic: standard Trustee produces around 30% FPR-N because Normal frames constitute only 5.4% of training data. A class-balanced Trustee variant, which oversamples Normal by factor α=5, reduces FPR-N to 13.8±1.4% (three Trustee runs) while preserving 91.1±0.6% accuracy and 0.918±0.006 macro-F1 at 2.1% CPU on a Raspberry Pi 4, versus 22.6% for TF-Lite int8 and 59.4% for TensorFlow on the same board. We additionally report α and depth ablations, three-seed teacher statistics, and per-class recall for all models.Code: https://github.com/Amirmasoud-Pourmiri/Lightweight-Neural-Network-Distillation-for-Real-Time-CAN-Bus-Intrusion-Detection-in-Vehicles
Amirmasoud Pourmiri, Ali Eslami, Sergio A. Salinas Monroy· International Conference on...· 0 citations
The recent trend of Software-Defined Networking (SDN) has posed significant cybersecurity challenges as a result of its centralized control architecture, dynamic traffic behavior, and high programmability. Although these attributes improve network flexibility and management, they also increase vulnerability to Distributed Denial-of-Service (DDoS) attacks that can overwhelm network resources and disrupt services. Traditional signature- and rule-based detection methods may struggle with evolving traffic patterns and generate excessive false alarms. Machine learning offers a more promising solution that can learn the complex traffic patterns and separate malicious traffic from normal traffic. Most machine learning models, however, are black-box models that provide only superficial insight into the model predictions. Explainable Artificial Intelligence (XAI) addresses this limitation by identifying influential traffic features and providing interpretable evidence for detection decisions. This research develops an explainable machine learning-based framework for accurate, transparent, and reliable DDoS attack detection in an SDN environment. Several machine learning models are assessed, and XAI techniques are applied to explain the results of the predictions at global and instance levels. Gradient Boosting, Logistic Regression, AdaBoost, and Gaussian Naive Bayes were evaluated on 104,345 network-flow records using a 70:30 training–testing split. Gradient Boosting achieved the strongest performance, with 99.88% training accuracy, 99.87% testing accuracy, a testing F1-score of 99.84%, and a 0.20% miss rate. SHAP identified the most influential traffic features, while LIME linked individual predictions to feature-specific contributions. The proposed framework therefore combines reliable DDoS detection with transparent, analyst-oriented decision support for SDN security monitoring.
J. Malik, N. Naz, Muhammad Saleem et al.· Italian National Conference...· 0 citations
A closed-loop framework that detects and blocks attacks in software-defined networks without operator involvement is presented, evaluating its performance against this stringent temporal constraint rather than relying exclusively on detection accuracy.
Distributed Denial-of-Service (DDoS) attacks remain one of the most disruptive threats to network infrastructure, yet many machine learning (ML)-based detection studies report only offline benchmark performance without verifying whether that performance holds under real network conditions. This study evaluates two explainable ML classifiers, XGBoost and Random Forest, for DDoS detection and examines whether their near-perfect offline accuracy translates into reliable physical-network operation. The study combines offline benchmarking on the CIC-DDoS2019 dataset (293,485 flows) with physical-network validation using a working Intrusion Detection System (IDS) prototype under a controlled SYN-flood attack. Session-disjoint stratified sampling prevented flow-level leakage across attack sessions, while SHapley Additive exPlanations (SHAP) interpreted global and local feature importance. Offline, both classifiers achieved near-perfect performance (accuracy 99.99% for XGBoost, 99.98% for Random Forest; F1 = 0.9999; ROC-AUC up to 1.0000), with no statistically significant difference between them (McNemar's exact test, p = 0.2188), though XGBoost achieved approximately 3.69 times higher inference throughput (1,819,816 flows/s). SHAP identified Min Packet Length, Fwd Packet Length Min, Inbound, Protocol, and Init_Win_bytes_forward as the most influential features. In physical deployment, however, the IDS prototype flagged 6.26% of captured flows (6,935 of 110,762) as ATTACK during the SYN-flood test, and a separate 397-flow ambient-benign subset yielded a 6.80% false positive rate (95% Wilson CI: 4.72–9.71%), with short-duration SSDP/UPnP-style UDP control traffic accounting for 70% of observed false positives. This gap shows that near-perfect offline accuracy does not guarantee low false positives in real deployment, indicating that offline benchmarks alone are insufficient for validating IDS readiness.
Muhammad Azzam Anshori, R. Amri· Journal of Computer Science...· 0 citations
A sniffer-free detection pipeline that operates exclusively on the integer counters each mote already maintains for normal operation of RPL, indicating that detection in constrained IoT networks is bounded by what the mote reports, not by classifier sophistication.
E. Pacheco, C. Pedroso· IEEE Open Journal of the Com...· 0 citations
These findings demonstrate that stacking ensemble learning is a practical and computationally efficient alternative to complex deep learning architectures for SDN intrusion detection, with strong potential for scalable and real-time cybersecurity deployment in modern network environments.
Ubakaghinwa Paul Chigbu, Abdulrashid Abdulrauf, Ishaq Isa et al.· Scientific Journal of Comput...· 0 citations
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