2nd International Conference on Advancements in Computing [ICAC] 2020

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    Secure Communication Using Steganography in IoT Environment
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-11-10) Amjath, M.I.M.; Senthooran, V.
    IoT is an emerging technology in modern world of communication. As the usage of IoT devices is increasing in day to day life, the secure data communication in IoT environment is the major challenge. Especially, small sized Single-Board Computers (SBCs) or Microcontrollers devices are widely used to transfer data with another in IoT. Due to the less processing power and storage capabilities, the data acquired from these devices must be transferred very securely in order to avoid some ethical issues. There are many cryptography approaches are applied to transfer data between IoT devices, but there are obvious chances to suspect encrypted messages by eavesdroppers. To add more secure data transfer, steganography mechanism is used to avoid the chances of suspicion as another layer of security. Based on the capabilities of IoT devices, low complexity images are used to hide the data with different hiding algorithms. In this research study, the secret data is encoded through QR code and embedded in low complexity cover images by applying image to image hiding fashion. The encoded image is sent to the receiving device via the network. The receiving device extracts the QR code from image using secret key then decoded the original data. The performance measure of the system is evaluated by the image quality parameters mainly Peak Signal to Noise Ratio (PSNR), Normalized Coefficient (NC) and Security with maintaining the quality of contemporary IoT system. Thus, the proposed method hides the precious information within an image using the properties of QR code and sending it without any suspicion to attacker and competes with the existing methods in terms of providing more secure communication between Microcontroller devices in IoT environment.
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    Enhanced Symmetric Cryptography for IoT using Novel Random Secret Key Approach
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Sittampalam, G.; Ratnarajah, N.
    The deployment of IoT devices in different domains enables technical innovations and value-added services to users but also creates multiple requirements in terms of effective communication and security. IoT devices are constrained by less computing resources and limited battery power. Generally, the TLS/SSL protocol is used to provide communication security on IoT and the protocol utilizes important encryption algorithms like RSA, Elliptic Curve Cryptography, and AES. However, these conventional encryption algorithms are computationally and economically expensive to implement in IoT devices. Lightweight Cryptography (LWC) algorithms were introduced recently for IoT and the aim of the algorithms is to provide the same level security with a minimal amount of computing resources and power. This paper proposes a novel Random Secret Key (RSK) technique to provide an additional security layer for symmetric LWC algorithms for IoT applications. In RSK, IoT devices do not transmit keys over the network; they share a random matrix, calculate their own RSK, encrypt, and transmit the cipher text. When a random matrix lifetime expires new matrix published and RSK resets. Regular change in the RSK makes the IoT networks resistant to brute-force/dictionary attacks. The RSK added one more simple and effective secure layer to strengthen the security of the original secret key and is successfully implemented in a smart greenhouse environment. The outcomes of the experiments prove that the RSK provides enhanced and efficient protection for symmetric LWC algorithms in any IoT systems, consume a minimum amount of resources and more resistant to key-based attacks.
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    An Integrated Framework for Predicting Health Based on Sensor Data Using Machine Learning
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Jayaweera, K.N.; Kallora, K.M.C.; Subasinghe, N.A.C.K.; Rupasinghe, L.; Liyanapathirana, C.
    According to recent studies, the majority of the world's population shows a lack of concern in their health. As a consequence, the non-communicable disease rate has increased dramatically. Amongst these diseases, heart diseases have caused the most catastrophic situations. Apart from the busy lifestyle, studies also show that stress is another factor that causes these diseases. Therefore, the focus of our research is to provide a user-friendly health monitoring system that causes minimum disturbance to its users. However, many studies have focused on predicting health; very few have focused on its usability. The objective of our research is to predict the possibility of cardiac arrests and the presence of stress in real-time using a wearable device prototype. The system uses biometric signals obtained from the photoplethysmogram sensor embedded in the wearable device to perform real-time predictions. We trained three models using random forest, k-nearest neighbor, and logistic regression classification algorithms to predict sudden cardiac arrests with accuracies 99.93%, 99.10%, and 94.47%, respectively. Further, we trained three additional models to predict stress using the same algorithms with accuracies 99.87%, 96.83%, and 65.00%, respectively. Thus, the results of this study show that an integrated framework, capable of predicting different health-related conditions, through sensor data collected from wearable sensors, is feasible.