Light-weight Security and Data Provenance for Multi-hop Internet of Things
ABSTRACT
Due to limited resources and scalability, security protocols for Internet of Things (IoT) need to be light-weighted. The cryptographic solutions are not feasible to apply on small and low-energy devices of IoT because of their energy and space limitations. In this paper, a light-weight protocol to secure the data and achieving data provenance is presented for multi-hop IoT network. The Received Signal Strength Indicator (RSSI) of communicating IoT nodes are used to generate the link fingerprints. The link fingerprints are matched at the server to compute the correlation coefficient. Higher the value of correlation coefficient, higher the percentage of secured data transfer. Lower value gives the detection of adversarial node in between a specific link. Data provenance has also been achieved by comparison of packet header with all the available link fingerprints at the server. The time complexity is computed at the node and server level, which is O(1). The energy dissipation is calculated for IoT nodes and overall network. The results show that the energy consumption of the system presented in this paper is 52 mJ to 53 mJ for each IoT node and 313.626 mJ for the entire network. RSSI values are taken in real time from MICAz motes and simulations are performed on MATLAB for adversarial node detection, data provenance and timecomplexity. Experimental results show that up to 97% correlation is achieved when no adversarial node is present in the IoT network.
EXISTING SYSTEM:
Internet of Things (IoT) comprises a complex network of smart devices, which frequently exchange data through the Internet [1]. IoT has become the necessity for the future communication. It is estimated that 50 billion smart devices will be connected through IoT ]. The information of a patient to a medical staff, automobile’s performance and statistics, home automation, transportation domain, smart grids and smart meters will be based on IoT. The data acquired from sensors or IoT nodes is propagated to Internet cloud where it is received by the concerned body. The acquired data needs to be accurate and should have the information about its origin. As the number of nodes are large in number, small in size and mostly accessible, the measures should be taken to make sure that the data is secured and efficiently received at the receiving end. Data security and provenance act as backbone in order to implement IoT network because the IoT nodes are not physically protected. The data can easily be forged or tampered if proper security primitivesare not taken. Security primitives include detection of certain attacks, masking channel state, intrusion detection, location distinction and data provenance. Provenance is to find the origin of the data. A single change in data might cause big problems e.g., in terms of medical health report generated by an IoT node sent to a doctor, meter reading sent to the company for billing according to the consumption and change in transportation system information . Therefore, the traditional cryptographic techniques are not the viable solution in IoT because of the energy limitations of the IoT nodes . Less space acquiring and energy efficient security primitives with less computational complexities are key building blocks for enabling end-to-end content protection, user authentication, and consumer confidentiality in the IoT world . To ensure the trust of users, the IoT-based network should be secured enough.
The security mechanism involved should be light-weighted because of the low energy requirements for IoT nodes. The mutual authentication between IoT nodes with the server should also be secured and authentic. Accurate and secure data provenance in the IoT are used for improving the level of trust. The data provenance is useful for determining and describing the derivation history of data starting from the original resource. The records can be used to protect intellectual property and its relevance from the perspective of regulatory mechanisms. However, the data provenance integrity is a big question. The data provenance can be forged or tampered by an unauthorized party if the provenance is not properly protected by implementing inefficient security protocols. In order to establish the trust of IoT, a solution to security should be designed which is light-weight and highly secured. Most of the security algorithms and cryptography techniques used today contain high computational complexities with high energy consumption.
PROPOSED SYSTEM:
The solution proposed in this paper incorporates lightweight security algorithms for secured IoT-based information exchange without using extra hardware. Adversarial node is detected effectively by correlating the link fingerprints generated by the adjacent IoT nodes. The correlation coefficient is computed at the server. Data provenance is also achieved using the same link fingerprints generated to find the intrusion detection in the IoT network. Hence, fingerprints are used to authenticate the integrity of data and in the detection of intrusion. The proposed solution has less time complexity compared to other state-of-the-art available solutions. The energy calculations are presented as well showing very desirable results when compared to the previously work done.
CONCLUSION :
The fingerprints generated between any two connected IoT nodes are highly correlated. Introducing an adversarial node gives very low correlation coefficient. It means that the detection of any adversarial node in an IoT network can be done for low power nodes. The data forensics can also be applied by looking at the header of the last received data. The origin of data is computed by extracting the header. The server is considered as highly protected because it contains the keys associated with all the IoT nodes. We get the lightweight solution for the security and data provenance in IoT environment. The energy calculations show that less energy is consumed by applying the link fingerprint generation protocol, sending the packet to the server and to the adjacent IoTnode. Time complexity of the system remains the same no matter how lengthy the code becomes.