Alireza Jolfaei

TL;DR: A multi-stage intrusion detection framework to identify intrusions from ITSs and produce low rate of false alarms and is capable to detect zero-day (concealed) outbreaks from IoVs networks.

TL;DR: A novel spectrum sharing technique is proposed using 5G enabled bidirectional cognitive deep learning nodes (BCDLN) along with dynamic spectrum sharing long short-term memory (DSLSTM), and expressions are derived for the spectrum allocated to multiple sources to obtain their spectrum targets as a variant of the participation node spectrum sharing ratio (PNSSR).

TL;DR: This chapter proposes a novel deep learning framework to detect malicious domains generated by malicious Domain Generation Algorithms (DGA), and provides an early detection mechanism for the identification of Domain-Flux botnets propagating in a network.

TL;DR: In this paper, an end-to-end multi-objective neural evolutionary algorithm based on decomposition and dominance (MONEADD) was proposed for combinatorial optimization problems.

TL;DR: This paper proposes a combination of machine learning techniques to mitigate the relay attacks on Passive Keyless Entry and Start (PKES) systems and uses a Long Short-Term Memory recurrent neural network for driver identification based on the real-world driving data.