The Internet of Things devices generates a huge amount of sensitive data. Machine learning is the standard processing paradigm for intelligently handling the huge amount of data. Unfortunately, the IoT devices have limited resources to handle the performance of big data feature learning with machine learning techniques. IoT devices often compromise the privacy of users and make them vulnerable to numerous cyber-attacks. In this paper, we propose an efficient privacy-preserving authentication protocol based on blockchain technology and the secret computational model of physically unclonable function (denoted by PUF model). The proposed protocol guarantees the users privacy with a decentralized smart contract blockchain with the PUF model. In practice, the proposed protocol guarantees that IoT devices and the miner are authenticated in a faster authentication process compared to current blockchain techniques. In addition, Blockchain and PUF combine to ensure data provenance and data transparency in IoT networks. Blockchain-based smart contracts provide decentralized digital ledgers that are able to withstand data tampering attacks. This ensures the security and privacy of outsourced big data in IoT environments. We also investigated the privacy implications of using IoT devices with various security analysis, and avenues for research to extenuate the privacy concerns in IoT environments.

Devices constituting the Internet of Things (IoT) have become widely used, therein generating a large amount of sensitive data. The communication of these data across IoT devices and over the public Internet makes them susceptible to several cyber attacks. In this paper, we propose an efficient blockchain approach based on the secret computational model of a physically unclonable function (PUF). The proposed framework aims to guarantee authentication of the devices and the miner with a faster verification process compared to existing blockchain techniques. Furthermore, the combination of the blockchain and PUF allows us to propose an efficient framework that guarantees data provenance and data integrity in IoT networks. The proposed framework employs PUFs, which provide unique hardware fingerprints for establishing data provenance. Smart contracts based on the blockchain provide a decentralized digital ledger that is able to resist data tampering attacks.

In this paper, we propose an energy-efficient surveillance framework for real-time video processing. The proposed framework guarantees the confidentiality of important frames and transfers them for a real-time decision. First, we extract the keyframes from the surveillance video using a lightweight summarization technique based on a fast histogram-clustering approach. Then, we employ an enhanced discrete cosine transform (DCT) compression technique to reduce the size of the extracted key frames. Finally, the cryptosystem encrypts these keyframes using a lightweight image encryption scheme based on discrete fractional random transform (DFRT) and Chen chaotic system. The proposed framework is fast and ensures real-time processing. Furthermore, this framework has the ability to reduce the transmission cost, and storage required during transmitting the video surveillance.

Nowadays, smartphone applications are the most widespread in our daily lives. These applications raised several security concerns such as authentication, key agreement, and mutual authentication. Accordingly, the researchers have been presented several user authentication schemes based on the identity-based cryptography (IBC) and certificateless cryptography (CLC). Smartphones considered as limited resources devices, thus, it needs lightweight protocols. However, the existing schemes are suffering from high computational costs especially the one that depends on CLC. In this paper, a lightweight certificateless user authentication scheme based on the elliptic curve cryptography (ECC) is introduced. The proposed scheme has the lowest computation costs comparing with the existing certificateless user’s authentication protocols. Furthermore, The proposed scheme is secure under the computational Diffie-Hellman (CDH) Problem and the elliptic curve discrete logarithm problem (ECDLP). Indeed, the proposed scheme is suitable to use in the mobile client-server environment and the Internet of things (IoT) applications.

Ration card plays a vital role for the household details such as to get gas connection, family member details, it acts as address proof etc. In this paper, we have proposed a smart ration card system using Radio Frequency Identification (RFID) Technique and loT to prevent the malpractices and corruption in the current ration distribution system. In this system conventional ration card will be replaced by a unique RFID tag. This RFID tag will be verified at the fair price shop for the authentication of the user. The user's identity will be verified by microcontroller which is connected to an Amazon Web Services (AWS) database. For added security One Time Password (OTP) is also sent to user's registered mobile number which needs to be entered in the system. If user is found to be authentic then monthly quota of the ration available for the user is displayed. After successful transaction the database will be updated stating the ration content delivered to the user. This system will require very less human efforts for operation and is also very secure. By implementing this system government can keep track of all the delivered ration content very easily.

The emerging greenhouse technology in agriculture based on Internet of Things (IoT) used for remote monitoring and automation has been rapidly developed. But it still has major concern about security and privacy, due to the large scale of disseminating nature of its network. To overcome these security challenges, we use blockchain which allows the creation of a distributed digital ledger of transactions that is shared among the nodes on IoT network. The main aim of this paper is to provide lightweight blockchain based architecture for smart greenhouse farms to provide security and privacy. Here, IoT devices in greenhouses which act as a blockchain managed centrally to optimize energy consumption have the benefit of private immutable ledgers. In addition, we present a security framework that blends the blockchain technology with IoT devices to provide a secure communication platform in Smart Greenhouse farming.

Network infrastructures are in jeopardy of suffering nowadays since a number of attacks have been developed and grown up enormously. In order to get rid of such security threats, a defense mechanism is much sought-after. This paper proposes an improved model of intrusion detection by using two-level classifier ensemble. The proposed model is made up of a PSO-based feature selection technique and a two-level classifier ensemble which employs two ensemble learners, i.e. boosting and random subspace model (RSM). The experiment conducted on NSL-KDD dataset reveals that the proposed model outperforms previous detection models significantly in terms of accuracy and false alarm rate (FPR).