Nowadays, Learning Management Systems (LMS) have become increasingly popular, particularly due to the COVID-19 pandemic, offering enhanced effectiveness and efficiency. Online exams have emerged as a critical feature within LMS, serving as a means to evaluate students’ performance and assess their understanding of the course material. These exams play a crucial role in determining students’ eligibility for progression to the next grade or level of study. It is imperative that online exam results meet the standards of reliability and transparency. Any lack of reliability, such as the vulnerability to hacking, can have detrimental effects on students’ overall grades. Traditional online exam systems typically store data in centralized locations like MySQL databases, leaving them susceptible to unauthorized access by malicious individuals who may alter students’ exam results. This paper aims to propose a blockchain-based framework that facilitates the secure and peer-to-peer conduct and evaluation of academic exams. The framework employs hashing techniques to ensure the integrity of the data and utilizes proof of stake mechanisms to enhance security. Blockchain technology has proven to be effective in safeguarding data integrity by virtue of its decentralized data storage approach and the use of cryptographic hashing for every block within the chain. This paper demonstrates how online exams can be developed using blockchain technology, with each question asked and answered being directly stored on the blockchain. To achieve this, we have developed a module that integrates with the Moodle learning management system. Through a comparative analysis of the default centralized storage approach in Moodle, our module modifies the exam results’ storage method, ensuring secure and tamper-proof data storage on the blockchain network. By leveraging the blockchain network, the data associated with exam results is reliably secured, ensuring its integrity, and making it immune to manipulation. Our results indicate that the data stored through the blockchain achieved complete accuracy, with no discrepancies observed when compared to the standard approach employed by the Moodle LMS for storing results. The blockchain network provides a reliable and immutable platform that prevents unauthorized alterations or manipulations of student data. In conclusion, our blockchain-based framework offers a robust solution for enhancing the security and reliability of online exam results. By leveraging the decentralized and tamper-proof nature of blockchain technology, we can ensure the integrity and transparency of student data, ultimately providing a more trustworthy and accurate assessment of their academic performance.

One of the significant aspects of our digital world is that data are literally everywhere, and it is increasing. On the other hand, the number of cyberattacks aiming to seize this data and use it illegally is increasing at an exponential rate, and this is the challenge. Therefore, intrusion detection systems (IDS) have attracted considerable interest from researchers and industries. In this regard, machine learning (ML) techniques are playing a pivotal role as they put the responsibility of analyzing enormous amounts of data, finding patterns, classifying intrusions, and solving issues on computers instead of humans. This paper implements two separate classification layers of ML-based algorithms with the recently published NF-UQ-NIDS-v2 dataset, preprocessing two volumes of sample records (100 k and 10 million), utilizing MinMaxScaler, LabelEncoder, selecting superlative features by recursive feature elimination, normalizing the data, and optimizing hyper-parameters for classical algorithms and neural networks. With a small dataset volume, the results of the classical algorithms layer show high detection accuracy rates for support vector (98.26%), decision tree (98.78%), random forest (99.07%), K-nearest neighbors (98.16%), CatBoost (99.04%), and gradient boosting (98.80%). In addition, the layer of neural network algorithms has proven to be a very powerful technology when using deep learning, particularly due to its unique ability to effectively handle enormous amounts of data and detect hidden correlations and patterns; it showed high detection results, which were (98.87%) for long short-term memory and (98.56%) for convolutional neural networks.