Rajarshi Roy Chowdhury

Rajarshi Roy Chowdhury, Azam Che Idris, Pg Emeroylariffion Abas

The rapidly increasing number of internet of things (IoT) and non-IoT devices has imposed new security challenges to network administrators. Accurate device identification in the increasingly complex network structures is necessary. In this paper, a device fingerprinting (DFP) method has been proposed for device identification, based on digital footprints, which devices use for communication over a network. A subset of nine features have been selected from the network and transport layers of a single transmission control protocol/internet protocol packet based on attribute evaluators in Weka, to generate device-specific signatures. The method has been evaluated on two online datasets, and an experimental dataset, using different supervised machine learning (ML) algorithms. Results have shown that the method is able to distinguish device type with up to 100% precision using the random forest (RF) classifier, and classify individual devices with up to 95.7% precision. These results demonstrate the applicability of the proposed DFP method for device identification, in order to provide a more secure and robust network.

Rajarshi Roy Chowdhury, Azam Che Idris, Pg Emeroylariffion Abas

The usage of technologically advanced devices has seen a boom in many domains, including education, automation, and healthcare; with most of the services requiring Internet connectivity. To secure a network, device identification plays key role. In this paper, a device fingerprinting (DFP) model, which is able to distinguish between Internet of Things (IoT) and non-IoT devices, as well as uniquely identify individual devices, has been proposed. Four statistical features have been extracted from the consecutive five device-originated packets, to generate individual device fingerprints. The method has been evaluated using the Random Forest (RF) classifier and different datasets. Experimental results have shown that the proposed method achieves up to 99.8% accuracy in distinguishing between IoT and non-IoT devices and over 97.6% in classifying individual devices. These signify that the proposed method is useful in assisting operators in making their networks more secure and robust to security breaches and unauthorized access.

Rajarshi Roy Chowdhury, Debashish Roy, Pg Emeroylariffion Abas

Internet of Things (IoT) is one of the technological advancements of the twenty-first century which can improve living standards. However, it also imposes new types of security challenges, including device authentication, traffic types classification, and malicious traffic identification, in the network domain. Traditionally, internet protocol (IP) and media access control (MAC) addresses are utilized for identifying network-connected devices in a network, whilst these addressing schemes are prone to be compromised, including spoofing attacks and MAC randomization. Therefore, device identification using only explicit identifiers is a challenging task. Accurate device identification plays a key role in securing a network. In this paper, a supervised machine learning-based device fingerprinting (DFP) model has been proposed for identifying network-connected IoT devices using only communication traffic characteristics (or implicit identifiers). A single transmission control protocol/internet protocol (TCP/IP) packet header features have been utilized for generating unique fingerprints, with the fingerprints represented as a vector of 22 features. Experimental results have shown that the proposed DFP method achieves over 98% in classifying individual IoT devices using the UNSW dataset with 22 smart-home IoT devices. This signifies that the proposed approach is invaluable to network operators in making their networks more secure.

Rajarshi Roy Chowdhury, Sandhya Aneja, Nagender Aneja et al.

Device identification is the process of identifying a device on Internet without using its assigned network or other credentials. The sharp rise of usage in Internet of Things (IoT) devices has imposed new challenges in device identification due to a wide variety of devices, protocols and control interfaces. In a network, conventional IoT devices identify each other by utilizing IP or MAC addresses, which are prone to spoofing. Moreover, IoT devices are low power devices with minimal embedded security solution. To mitigate the issue in IoT devices, fingerprint (DFP) for device identification can be used. DFP identifies a device by using implicit identifiers, such as network traffic (or packets), radio signal, which a device used for its communication over the network. These identifiers are closely related to the device hardware and software features. In this paper, we exploit TCP/IP packet header features to create a device fingerprint utilizing device originated network packets. We present a set of three metrics which separate some features from a packet which contribute actively for device identification. To evaluate our approach, we used publicly accessible two datasets. We observed the accuracy of device genre classification 99.37% and 83.35% of accuracy in the identification of an individual device from IoT Sentinel dataset. However, using UNSW dataset device type identification accuracy reached up to 97.78%.