With the proliferation of Internet of Things (IoT), critical infrastructures such as smart cities, industrial automation, precision healthcare, and intelligent transportation have seen a significant shift in their security landscape. As these critical infrastructures are increasingly depending on OneM2M standards for interoperability and scalable service management, they are simultaneously presenting a larger and more structured attack surface. One of the most serious and realistic threats in such scenarios is black-box adversarial attacks, in which an attacker can compromise Machine Learning (ML) and Deep Learning (DL)-based intrusion detection systems (IDS) without having prior knowledge of the ML/DL model's architecture, parameters, and training data. To mitigate such a critical challenge, we propose a multi-stage framework specific to OneM2M-based IoT networks that can effectively counter transfer-based and query-based black-box adversarial attacks. Our framework's effectiveness will be tested on a dataset of 1.25 million labeled network flows collected from Azure IoT Hub-based IoT devices over a 10-day period. We will test the robustness of our framework against three adversarial attacks: FGSM with $\varepsilon=0.05$, PGD with 40 iterations and step size 0.01, and C&W with L2 optimization and 1,000 iterations. The proposed pipeline consists of four different mechanisms: (1) adversarial training with the use of mixed clean and adversarial samples, (2) traffic sanitization to minimize malicious and/or suspicious traffic flows before processing by the Common Service Entity (CSE), (3) the use of a black-box adversarial detector based on feature transformation, and (4) the use of an ensemble-based IDS to make use of the results of the Random Forest, XGBoost, MLP, and 1D-CNN-based IDSs via majority voting. The experimental results show that the proposed Ensemble + multi-defense configuration results in 98.1% accuracy, 98.3% TPR, and 1.7% FNR. Most notably, the proposed Ensemble + multi-defense configuration results in an ASR of 11.2%, which is down by 86% compared to the 82.3% ASR achieved by the baseline IDS. Notably, the proposed framework results in limited overheads in terms of RTT (increased by 8 ms), CPU (increased by 17%), and RAM (increased by 1.6 GB).
Hamza Jamiri, A. Zyane· International Conference on...· 0 citations
The rapid expansion of Internet of Things (IoT) devices requires middleware capable of handling heterogeneous traffic while satisfying strict Quality of Service (QoS) targets. ITU-T Recommendation Y.1541 defines well-established performance thresholds for IP networks; however, baseline oneM2M deployments frequently fail to meet these targets under mixed workloads. This paper evaluates the open-source OM2M platform against ITU-T Y.1541 using eight QoS metrics spanning application and network layers, making the compliance gap explicit and quantifiable. Under the default configuration, the platform achieved only 20% overall compliance. To close this gap, an autonomic control architecture based on the Monitor-Analyze-Plan-Execute with Knowledge (MAPE-K) loop is integrated with a Random Forest (RF) classifier that predicts four discrete QoS operational states with 91.9% accuracy. The optimized configuration improves ITU-T compliance from 20% to 60%, achieving latency reductions of 53 to 71%, jitter mitigation of 93 to 97%, and transaction failure rate decreases of 36 to 64%, all measured during steady-state operation. The paper identifies the mechanisms responsible for the remaining non-compliant metrics and proposes a cross-layer roadmap for achieving full ITU-T compliance.
Jamal Et-Tousy, A. Zyane· EPJ Web of Conferences· 0 citations
The proposed IoTScal-CoM middleware employs only native oneM2M capabilities such as RTT, packet loss rate, CPU, and memory usage in order to guarantee the SLA conformity without changing the main standard specifications.
S. Abourriche, A. Zyane, A. Ghammaz· EPJ Web of Conferences· 1 citation
The rapid proliferation of IoT devices and ecosystems creates significant challenges in managing increasing data traffic and service requests while maintaining system performance [1]– [3]. In oneM2M-based IoT systems, overloaded Common Service Entities (CSEs) can become bottlenecks, leading to resource saturation, higher latency, and request loss [4]. To address these challenges, this paper proposes IoTScal-2CoM-ALO, an adaptive load orchestration framework that introduces a two-level collaboration model (2CoM) enabling distributed CSEs to cooperate within and across domains. The framework incorporates an Adaptive Load Orchestration (ALO) mechanism that continuously monitors key performance indicators, including CPU utilization, memory consumption, round-trip time (RTT), and packet loss, to detect overload conditions and dynamically redirect traffic to suitable neighboring CSEs. The proposed approach is evaluated in a simulated distributed oneM2M environment under heterogeneous traffic conditions. Experimental results demonstrate significant performance improvements compared with non-collaborative and static collaboration approaches, achieving up to 73% reduction in memory consumption, RTT peak reductions of up to 4750 ms, and success rate improvements of approximately 4.8%. These results highlight the effectiveness of IoTScal-2CoM-ALO in improving resource utilization and maintaining service continuity in scalable IoT systems.
S. Abourriche, A. Zyane, A. Ghammaz· International Conference on...· 0 citations