Distributed Denial of Service (DDoS) of Things are becoming increasingly severe, characterized by unprecedented traffic volumes and rates. Predicting these attacks before they escalate is critical for reducing costs. However, the complex and multidimensional nature of DDoS attacks demands adaptive defense strategies, which are lacking in current approaches. Existing methods often depend on labeled datasets, offline processing or context-specific models, resulting in low prediction accuracy. Further, they tend to rely on homogeneous data sources, limiting their adaptability to the variability of real-world network environments. This paper introduces DELIBERATE, a novel technique for DDoS attack prediction that utilizes unsupervised online AutoML and ordinal pattern transformation through multimodal correlation analysis. DELIBERATE adapts itself to changes in network traffic by leveraging the correlation of heterogeneous data and ordinal patterns transformation, a noisetolerant method suitable for IoT environments. It predicts DDoS attacks up to 47 minutes in advance. The method outperforms traditional approaches that depend on labeled data, extensive training, and complex neural networks.

Predicting Distributed Denial of Service (DDoS) attacks is crucial given the large volume of generated attack traffic, particularly that generated by infected Internet of Things (IoT) devices. Attackers conceal their actions to delay detection as much as possible, increasing their damage when effectively launched. Hence, predicting signals of the attack plays a vital role in anticipating DDoS attacks and enhancing service protection. This work presents SEE, an unsupervised feature engineering approach to assist in predicting DDoS attacks. SEE evaluations encompass four experiments employing multiple datasets (CTU- 13, CIC-DDoS2019, and IoT-23) and DDoS attacks. The approach predicts a DDoS attack 30 minutes before it effectively starts, reaching up to 100% accuracy.

Molecular Communications (MC) networks comprise multiple devices performing coordinated complex tasks, such as detecting types of cancer and smart drug delivery. Signaling-based MC uses molecules as information carriers between signaling cells. In this context, synchronization is jointly paramount and challenging since the system must overcome the limitation of molecular propagation to make sure computationally deprived bio-devices can communicate. On top of that, a multi-user increases this system challenges as possible co-channel interference causes errors or failures. Bio-devices present severe computational and communication limitations, being this last one essentially unidirectional. This paper presents the first synchronization protocol between signaling cells for multi-user MC. Results have shown the convergence time concerning different network sizes from 12 to 60 nodes.

This paper presents a multi-carrier molecular communication model for astrocyte tissues. The model considers molecular diversity and analyses channel path loss and capacity for molecular communication based on the concentration of Inositol Triphosphate (IP3) and calcium (Ca2+) molecules. Without loss of generality, we investigate the spatiotemporal concentration of these molecules and how both intracellular and intercellular signaling dictates signal propagation inside astrocytes tissues. Astrocytes are the most abundant glial cell type in the adult brain and play essential roles in brain function, such as modulating neuronal excitation, inhibition, and synaptic transmission. Results show that using combined these two molecules reduces path loss, improves data propagation, and can be an alternative for data encoding and transmission. The multi-carrier molecular communication using IP3and Ca2+has overall superior performance, showing the potential benefits of molecular diversity.