Advances in data collection, storage, and processing in e-Health systems have recently increased the importance and popularity of data mining in the health care field. However, the high sensitivity of the handled and shared data, brings a high risk of information disclosure and exposure. It is therefore important to hide sensitive relationships by modifying the shared data. This major information security threat has, therefore, mandated the requirement of hiding/securing sensitive relationships of shared data. As a large number of data mining activities that attempt to identify interesting patterns from databases depend on locating frequent item sets, further investigation of frequent item sets requires privacy-preserving techniques. To solve many difficult combinatorial problems, such as data distribution problem, exact and heuristic algorithms have been used. Exact algorithms are studied and considered optimal for such problems, however they suffer scalability bottleneck, as they are limited to medium-sized instances only. Heuristic algorithms, on the other hand, are scalable, however, they perform poor on security and privacy preservation. This paper proposes a novel heuristic approach based on Formal Concept Analysis (FCA) for enhancing security and privacy preservation of sensitive e-Health information using itemset hiding techniques. Our approach, named FACHS (FCA Hiding Sensitive-itemsets) uses constraints to minimise side effects and asymmetry between the original database and the clean database (minimal distortion on the database). Moreover, our approach does not require frequent itemset extraction before the masking process. This gives the proposed approach an advantage in terms of total availability. We tested our FCAHS heuristic on various reference datasets. Extensive experimental results showed the effectiveness of the proposed masking approach and the time efficiency of itemset extraction, making it very promising for e-Health sensitive data security and privacy.

Internet traffic is a mixture of unicast and multicast flows. Integrated schedulers capable of dealing with both traffic types have been designed mainly for input queued (IQ) buffer-less crossbar switches. Combined input and crossbar queued (CICQ) switches, on the other hand, are known to have better performance than their buffer-less predecessors due to their potential in simplifying the scheduling and improving the switching performance. The design of integrated schedulers in CICQ switches has thus far been neglected. In this paper, we propose a novel CICQ architecture that supports both unicast and multicast traffic along with its appropriate scheduling. In particular, we propose an integrated round robin based scheduler that efficiently services both unicast and multicast traffic simultaneously. Our scheme, named multicast and unicast round robin scheduling (MURS), has been shown to outperform all existing schemes while keeping simple hardware requirements. Simulation results suggested that we can trade the size of the internal buffers for the number of input multicast queues

The Internet of Things (IoT) is a growing technology that remotely connects multiple devices (ranging across many fields and applications) over the Internet. The scalability of an IoT network mandates a reliable transport infrastructure. Traditional transport control protocol (TCP) control protocol is unsuitable for such domain, mainly due to energy and power consumption reasons. A lighter version of TCP, light weight IP (lwIP) provides a promising solution for current and projected future scalable IoT infrastructures. However, the original lwIP is just a simple mapping of the protocol, without insight into the IoT specific requirements. This paper examines the lwIP congestion control mechanism and addresses its shortcomings. In particular, a detailed examination is devoted to the various metrics such as retransmission time‐outs and its back‐off epochs, the congestion window behaviour and progress in the absence (and presence) of congestion. In particular, we propose a set of novel algorithms to address both the IoT constraints nature (light‐weight) as well as keeping up with scalability in IoT network size and performance. A detailed simulation study has been conducted to endorse the viability of our proposed set of algorithms for next‐generation IoT networks.

The rapid deployment of Internet of Things (IoT) devices has increased exposure to a diverse array of evolving cyberattacks, motivating the need for accurate and interpretable intrusion detection systems (IDS). In this work, we develop an explainable hybrid Convolutional Neural Network–Extreme Gradient Boosting (CNN–XGBoost) framework for multi-class IoT attack classification using the CIC IoT-DIAD 2024 dataset. Network-traffic records are preprocessed and standardized using a scalable, chunk-wise workflow, after which a compact top-k subset of features is selected via Random Forest importance ranking. To reduce selection bias, a leakage-prone feature-ranking strategy is compared with a leakage-aware strategy in which features are ranked using only the training data within each split. Subsequently, a one-dimensional Convolutional Neural Network (CNN) learns a 128-dimensional representation from the selected predictors, and XGBoost performs the final multi-class classification. Under the leakage-aware protocol, the proposed model achieves 0.9324 accuracy with 0.5910 macro-F1. Results indicate that leakage-aware selection provides a more defensible estimate of generalization while maintaining competitive detection performance. Finally, SHapley Additive exPlanations (SHAP) is used to interpret the model’s decisions in the learned latent space. The analysis shows that only a small number of embedding dimensions contribute most of the decision evidence, which can aid analyst triage, although the explanations remain indirect with respect to the original traffic features.

The growing trends of data centers over last decades including social networking, cloud-based applications, services, and storage technologies enabled many advances to take place in the networking area. Recent changes imply continuous demand for bandwidth to manage the large amount of packetized traffic. Cluster switches and routers make the switching fabric in a data center network (DCN) environment. They are employed to provide interconnectivity between elements of the same DC and inter DCs. To handle the constantly variable loads, switches need deliver outstanding throughput along with resiliency and scalability for DCN requirements. Conventional DCN switches adopt crossbars or/and blocks of memories in multistage interconnection architectures (commonly 2-tiers or 3-tiers). However, current multistage switches, with their space-memory variants, are either too complex to implement, have poor performance, or not cost effective.We pro- pose a novel and highly scalable multistage switch design based on networks-on-chip (NoC) fabrics for DCNs. In particular, we de- scribe a novel three-stage Clos-network packet-switch fabric with a round robin (RR) packets dispatching scheme where each central stage module is based on a unidirectional NoC (UDN), instead of the conventional single-hop crossbar fabric. The design, referred to as Clos-UDN, overcomes all the shortcomings of conventional multistage architectures. In particular, the Clos-UDN switch: (i) Obviates the need for a complex and costly input modules, by means of few, yet simple, input FIFO queues. (ii) Avoids the need for a complex and synchronized scheduling process over a high number of input-output modules and/or port pairs. (iii) Provides speedup, load balancing, and path-diversity thanks to a dynamic dispatching scheme as well as the NoC based fabric nature. Extensive simulations are conducted to compare the Clos-UDN switch to some multistage switches. Results show that the Clos-UDN outperforms conventional designs under different input traffics, making it highly appealing for ultra-high capacity DC networks.