Explainable and Optimally Configured Artificial Neural Networks for Attack Detection in Smart Homes

by   Shaleeza Sohail, et al.

In recent years cybersecurity has become a major concern in adaptation of smart applications. Specially, in smart homes where a large number of IoT devices are used having a secure and trusted mechanisms can provide peace of mind for users. Accurate detection of cyber attacks is crucial, however precise identification of the type of attacks plays a huge role if devising the countermeasure for protecting the system. Artificial Neural Networks (ANN) have provided promising results for detecting any security attacks for smart applications. However, due to complex nature of the model used for this technique it is not easy for normal users to trust ANN based security solutions. Also, selection of right hyperparameters for ANN architecture plays a crucial role in the accurate detection of security attacks, especially when it come to identifying the subcategories of attacks. In this paper, we propose a model that considers both the issues of explainability of ANN model and the hyperparameter selection for this approach to be easily trusted and adapted by users of smart home applications. Also, our approach considers a subset of the dataset for optimal selection of hyperparamters to reduce the overhead of the process of ANN architecture design. Distinctively this paper focuses on configuration, performance and evaluation of ANN architecture for identification of five categorical attacks and nine subcategorical attacks. Using a very recent IoT dataset our approach showed high performance for intrusion detection with 99.9 and Subcategory level classification of attacks.


page 7

page 8

page 16

page 20

page 27


Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System

The Internet of things (IoT) is still in its infancy and has attracted m...

Multi-Layer Perceptron Artificial Neural Network Based IoT Botnet Traffic Classification

Internet of Things (IoT) is becoming an integral part of our homes today...

NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains

In the recent years, Distributed Denial of Service (DDoS) attacks on Int...

Fusion of ANN and SVM Classifiers for Network Attack Detection

With the progressive increase of network application and electronic devi...

KeyDetect –Detection of anomalies and user based on Keystroke Dynamics

Cyber attacks has always been of a great concern. Websites and services ...

A cognitive based Intrusion detection system

Intrusion detection is one of the primary mechanisms to provide computer...

A Reference Model for IoT Embodied Agents Controlled by Neural Networks

Embodied agents is a term used to denote intelligent agents, which are a...

Please sign up or login with your details

Forgot password? Click here to reset