IoT Device Identification Based on Network Communication Analysis Using Deep Learning

by   Jaidip Kotak, et al.

Attack vectors for adversaries have increased in organizations because of the growing use of less secure IoT devices. The risk of attacks on an organization's network has also increased due to the bring your own device (BYOD) policy which permits employees to bring IoT devices onto the premises and attach them to the organization's network. To tackle this threat and protect their networks, organizations generally implement security policies in which only white listed IoT devices are allowed on the organization's network. To monitor compliance with such policies, it has become essential to distinguish IoT devices permitted within an organization's network from non white listed (unknown) IoT devices. In this research, deep learning is applied to network communication for the automated identification of IoT devices permitted on the network. In contrast to existing methods, the proposed approach does not require complex feature engineering of the network communication, because the 'communication behavior' of IoT devices is represented as small images which are generated from the device's network communication payload. The proposed approach is applicable for any IoT device, regardless of the protocol used for communication. As our approach relies on the network communication payload, it is also applicable for the IoT devices behind a network address translation (NAT) enabled router. In this study, we trained various classifiers on a publicly accessible dataset to identify IoT devices in different scenarios, including the identification of known and unknown IoT devices, achieving over 99


IoT Device Identification Using Deep Learning

The growing use of IoT devices in organizations has increased the number...

DLWIoT: Deep Learning-based Watermarking for Authorized IoT Onboarding

The onboarding of IoT devices by authorized users constitutes both a cha...

Detection of Unauthorized IoT Devices Using Machine Learning Techniques

Security experts have demonstrated numerous risks imposed by Internet of...

Thesis Deployment Optimization of IoT Devices through Attack Graph Analysis

The Internet of things (IoT) has become an integral part of our life at ...

Automated Identification of Vulnerable Devices in Networks using Traffic Data and Deep Learning

Many IoT devices are vulnerable to attacks due to flawed security design...

SECCS: SECure Context Saving for IoT Devices

Energy consumption of IoT devices is a very important issue. For this re...

Verifying and Monitoring IoTs Network Behavior using MUD Profiles

IoT devices are increasingly being implicated in cyber-attacks, raising ...

Please sign up or login with your details

Forgot password? Click here to reset