Jamel Nebhen | Searcharxiv

arXiv2022

An Executable Formal Model of the VHDL in Isabelle/HOL

In the hardware design process, hardware components are usually described in a hardware description language. Most of the hardware description languages, such as Verilog and VHDL, do not have mathematical foundation and hence are not fit for formal reasoning about the design. To enable formal reasoning in one of the most commonly used description language VHDL, we define a formal model of the VHDL language in Isabelle/HOL. Our model targets the functional part of VHDL designs used in industry, specifically the design of the LEON3 processor's integer unit. We cover a wide range of features in the VHDL language that are usually not modelled in the literature and define a novel operational semantics for it. Furthermore, our model can be exported to OCaml code for execution, turning the formal model into a VHDL simulator. We have tested our simulator against simple designs used in the literature, as well as the div32 module in the LEON3 design. The Isabelle/HOL code is publicly available: https://zhehou.github.io/apps/VHDLModel.zip

cs.CL↗

arXiv2021

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique.

cs.NI↗