The demand for the digital monitoring of environmental ecosystems is high and growing rapidly as a means of protecting the public and managing the environment. However, before data, algorithms, and models can be mobilized at scale, there are considerable concerns associated with privacy and security that can negatively affect the adoption of technology within this domain. In this paper, we propose the advancement of electronic environmental monitoring through the capability provided by the blockchain. The blockchain’s use of a distributed ledger as its underlying infrastructure is an attractive approach to counter these privacy and security issues, although its performance and ability to manage sensor data must be assessed. We focus on a new distributed ledger technology for the IoT, called IOTA, that is based on a directed acyclic graph. IOTA overcomes the current limitations of the blockchain and offers a data communication protocol called masked authenticated messaging for secure data sharing among Internet of Things (IoT) devices. We show how the application layer employing the data communication protocol, MAM, can support the secure transmission, storage, and retrieval of encrypted environmental sensor data by using an immutable distributed ledger such as that shown in IOTA. Finally, we evaluate, compare, and analyze the performance of the MAM protocol against a non-protocol approach.

In this paper, we present the design, implementation, and evaluation of a secure sensing data processing and logging system. The system is inspired and enabled by the blockchain technology. In this system, a public blockchain is used as immutable datastore to log the most critical data needed to secure the system. Furthermore, several innovative blockchain-inspired mechanisms have been incorporated into the system to provide additional security for the system’s operations. The first priority in securing sensing data processing and logging is admission control, i.e., only legitimate sensing data are accepted for processing and logging. This is achieved via a sensor identification and authentication mechanism. The second priority is to ensure that the logged data remain intact overtime. This is achieved by storing a small amount of data condensed from the raw sensing data on a public blockchain. A Merkel-tree based mechanism is devised to link the raw sensing data stored off-chain to the condensed data placed on public blockchain. This mechanism passes the data immutability property of a public blockchain to the raw sensing data stored off-chain. Third, the raw sensing data stored off-chain are secured with a self-protection mechanism where the raw sensing data are grouped into chained blocks with a moderate amount of proof-of-work. This scheme prevents an adversary from making arbitrary changes to the logged data within a short period of time. Fourth, mechanisms are developed to facilitate the search of the condensed data placed on the public blockchain and the verification of the raw sensing data using the condensed data placed on the public blockchain. The system is implemented in Python except the graphical user interface, which is developed using C#. The functionality and feasibility of the system have been evaluated locally and with two public blockchain systems, one is the IOTA Shimmer test network, and the other is Ethereum.

With the advent of technology, sophisticated malware presents a significant threat to computer security. In this work, we propose anomaly detection techniques that learn three different behaviors of windows system-call sequences. We apply Long-Short-Term-Memory (LSTM) for temporal behavior, Cosine Similarity for frequency distribution behavior, and Jaccard Similarity for commonality behavior. The proposed framework monitors the processes in a hypervisor-based environment to detect compromised virtual machines. System call sequences of normal processes and malware-infected processes were extracted with memory forensic techniques. Our proposed anomaly detection techniques were able to learn the above three behavior of the system call sequences with 99% accuracy.

Wireless Sensor Networks (WSNs) are crucial in various fields including Health Care Monitoring, Battlefield Surveillance, and Smart Agriculture. However, WSNs are susceptible to malicious attacks due to the massive quantity of sensors within them. Hence, there is a demand for a trust evaluation framework within WSNs to function as a secure system, to identify and isolate malicious or faulty sensor nodes. This information can be leveraged by neighboring nodes, to prevent collaboration in tasks like data aggregation and forwarding. While numerous trust frameworks have been suggested in the literature to assess trust scores and examine the reliability of sensors through direct and indirect communications, implementing these trust evaluation criteria is challenging due to the intricate nature of the trust evaluation process and the limited availability of datasets. This research conducts a novel comparative analysis of three trust management models: “Lightweight Trust Management based on Bayesian and Entropy (LTMBE)”, “Beta-based Trust and Reputation Evaluation System (BTRES)”, and “Lightweight and Dependable Trust System (LDTS)”. To assess the practicality of these trust management models, we compare and examine their performance in multiple scenarios. Additionally, we assess and compare how well the trust management approaches perform in response to two significant cyber-attacks. Based on the experimental comparative analysis, it can be inferred that the LTMBE model is optimal for WSN applications emphasizing high energy efficiency, while the BTRES model is most suitable for WSN applications prioritizing critical security measures. The conducted empirical comparative analysis can act as a benchmark for upcoming research on trust evaluation frameworks for WSNs.

In this paper, we present a secure datastore based on an Ethereum smart contract. Our research is guided by three research questions. First, we will explore to what extend a smart-contract-based datastore should resemble a traditional database system. Second, we will investigate how to store the data in a smart-contract-based datastore for maximum flexibility while minimizing the gas consumption. Third, we seek answers regarding whether or not a smart-contract-based datastore should incorporate complex processing such as data encryption and data analytic algorithms. The proposed smart-contract-based datastore aims to strike a good balance between several constraints: (1) smart contracts are publicly visible, which may create a confidentiality concern for the data stored in the datastore; (2) unlike traditional database systems, the Ethereum smart contract programming language (i.e., Solidity) offers very limited data structures for data management; (3) all operations that mutate the blockchain state would incur financial costs and the developers for smart contracts must make sure sufficient gas is provisioned for every smart contract call, and ideally, the gas consumption should be minimized. Our investigation shows that although it is essential for a smart-contract-based datastore to offer some basic data query functionality, it is impractical to offer query flexibility that resembles that of a traditional database system. Furthermore, we propose that data should be structured as tag-value pairs, where the tag serves as a non-unique key that describes the nature of the value. We also conclude that complex processing should not be allowed in the smart contract due to the financial burden and security concerns. The tag-based secure datastore designed this way also defines its applicative perimeter, i.e., only applications that align with our strategy would find the proposed datastore a good fit. Those that would rather incur higher financial cost for more data query flexibility and/or less user burden on data pre- and post-processing would find the proposed database too restrictive.