Tulika Bose

Auth-Lite: Lightweight M2MAuthentication reinforcing DTLS for CoAP

The resource constrained characteristic of M2M systems has made secure channel establishment for sensor data communication a challenging task. While the proposed Constrained Application Protocol (CoAP) from Internet Engineering Task Force (IETF) enables lightweight application layer for sensor devices and gateways, lightweight security protocol on DTLS-secured CoAP (CoAPs) is still not optimally achieved. CoAPs proposes different modes of security starting from pre-shared key to certificate mode. This paper investigates the performance of the payload embedded novel authentication and key management mechanism proposed by the authors, which is robust in nature, compared to pre-shared key mode CoAPs, as well as lightweight. The proposed scheme can be integrated in future with pre-shared key mode of DTLS to reinforce DTLS for mutual authentication. We prove efficacy of our proposed scheme based on comparative analysis with pre-shared key mode of CoAPs.

Lightweight security scheme for IoT applications using CoAP

Purpose – The purpose of this paper is to study lightweight security scheme for Internet of Things (IoT) applications using Constrained Application Protocol (CoAP). Resource-constrained characteristics of IoT systems have ushered in compelling requirements for lightweight application protocol and security suites. CoAP has already been established as the candidate protocol for IoT systems. However, low overhead security scheme for CoAP is still an open problem. Existing security solutions like Datagram Transport Layer Security (DTLS) is not suitable, particularly due to its expensive handshaking, public key infrastructure (PKI)-based authentication and lengthy ciphersuite agreement process. Design/methodology/approach – This paper proposes a lightweight security scheme in CoAP using Advanced Encryption Standard (AES) 128 symmetric key algorithm. The paper presents an object security (payload embedded)-based robust authentication mechanism with integrated key management. The paper introduces few unique modifications to CoAP header to optimize security operation and minimize communication cost. Findings – It is resilient to number of security attacks like replay attack, meet-in-the-middle attack and secure under chosen plaintext attack. This scheme is generic in nature, applicable for gamut of IoT applications. The paper proves efficacy of our proposed scheme for vehicle tracking application in emulated laboratory setup. Specifically, it compares with DTLS-enabled CoAP to establish the lightweight feature of our proposed solution. Research limitations/implications – This paper mainly focuses on implementing in-vehicle tracking systems as an IoT application and used CoAP as the application protocol. Practical implications – Such a lightweight security scheme would provide immense benefit in IoT systems so that resource constraint-sensing devices and nodes can be made secure. This would impact IoT eco systems to a large extent. Originality/value – Such kind of security suite that provides both robustness and lightweight feature is hitherto not known to the authors, particularly in CoAP for IoT applications.

SensIPro: Smart sensor analytics for Internet of things

Sensors play a vital role for realizing the vision of connected smart universe. In this paper, we present a novel sensor agnostic model SensIPro to perform robust unsupervised analysis of sensor data to support scalable analytics, a prime need for Internet of things (IoT). In the context of sensor analytics, outliers contain most delicate information. Analysis of anomaly or outlier is mostly dependent on the application domain as well as signal characteristics. Our proposed sensor analytics model SensIPro automates analysis of outliers based on inferring signal characteristics of diverse sensors from different IoT applications like healthcare, smart energy, smart transport. We apply relevant time-series algorithms using statistical analysis, information theoretic measure for sensor data analytics. We measure similarity/dissimilarity of the time series sensor data and correlate with detected outliers. Our algorithm does not require any prior knowledge of sensor data type and metadata. We present results and analysis based on real life heterogeneous sensor data sets. Obtained results further prove efficacy of the proposed mechanism.

Demo: IAS: Information Analytics for Sensors

Sensors are one of the primary building blocks of IoT. Owing to close proximity of physical world, sensors often collect sensitive information. Invariably, sensor data has rich information content. Here we propose a novel solution IAS: Information Analytics for Sensors to unlock massive potential of sensor data through information analytics and demonstrate an alerting mechanism based on criticality of sensor information. ECG anomaly detection for healthcare, unusual appliance operation detection from smart energy meter data, bad road condition as well as activity detection from accelerometer data are typical use-case scenarios. We use robust statistical and information theoretic approaches. Our approach is unsupervised and is completely sensor agnostic. This abstract provides overview of design and implementation of our tool IAS along with obtained results tested on publicly available datasets. Last but not the least, IAS validates that outliers contain most delicate information.

Why not keep your personal data secure yet private in IoT?: Our lightweight approach

IoT (Internet of Things) systems are resource-constrained and primarily depend on sensors for contextual, physiological and behavioral information. Sensitive nature of sensor data incurs high probability of privacy breaching risk due to intended or malicious disclosure. Uncertainty about privacy cost while sharing sensitive sensor data through Internet would mostly result in overprovisioning of security mechanisms and it is detrimental for IoT scalability. In this paper, we propose a novel method of optimizing the need for IoT security enablement, which is based on the estimated privacy risk of shareable sensor data. Particularly, our scheme serves two objectives, viz. privacy risk assessment and optimizing the secure transmission based on that assessment. The challenges are, firstly, to determine the degree of privacy, and evaluate a privacy score from the fine-grained sensor data and, secondly, to preserve the privacy content through secure transfer of the data, adapted based on the measured privacy score. We further meet this objective by introducing and adapting a lightweight scheme for secure channel establishment between the sensing device and the data collection unit/ backend application embedded within CoAP (Constrained Application Protocol), a candidate IoT application protocol and using UDP as a transport. We consider smart energy management, a killer IoT application, as the use-case where smart energy meter data contains private information about the residents. Our results with real household smart meter data demonstrate the efficacy of our scheme.

LESS: Lightweight Establishment of Secure Session: A Cross-Layer Approach Using CoAP and DTLS-PSK Channel Encryption

Secure yet lightweight protocol for communication over the Internet is a pertinent problem for constrained environments in the context of Internet of Things (IoT) / Machine to Machine (M2M) applications. This paper extends the initial approaches published in [1], [2] and presents a novel cross-layer lightweight implementation to establish a secure channel. It distributes the responsibility of communication over secure channel in between the application and transport layers. Secure session establishment is performed using a payload embedded challenge response scheme over the Constrained Application Protocol (CoAP) [3]. Record encryption mechanism of Datagram Transport Layer Security (DTLS) [4] with Pre-Shared Key (PSK) [5] is used for encrypted exchange of application layer data. The secure session credentials derived from the application layer is used for encrypted exchange over the transport layer. The solution is designed in such a way that it can easily be integrated with an existing system deploying CoAP over DTLS-PSK. The proposed method is robust under different security attacks like replay attack, DoS and chosen cipher text. The improved performance of the proposed solution is established with comparative results and analysis.

Signal Characteristics on Sensor Data Compression in IoT -An Investigation

In Internet of Things (IoT), numerous and diverse types of sensors generate a plethora of data that needs to be stored and processed with minimum loss of information. This demands efficient compression mechanisms where loss of information is minimized. Hence data generated by diverse sensors with different signal features require optimum balance between compression gain and information loss. This paper presents a unique analysis of contemporary lossy compression algorithms applied on real field sensor data with different sensor dynamics. The aim of the work is to classify the compression algorithms based on the signal characteristics of sensor data and to map them to different sensor data types to ensure efficient compression. The present work is the stepping stone for a future recommender system to choose the preferred compression techniques for the given type of sensor data.