Proper security solutions in the cyber world are crucial for enforcing network security by providing real-time network protection against network vulnerabilities and data exploitation. An effective intrusion detection strategy is capable of taking a holistic approach for protecting critical systems against unauthorized access or attack. In this paper, we describe a machine learning (ML) based comprehensive security solution for network intrusion detection using ensemble supervised ML framework and ensemble feature selection methods. In addition, we provide a comparative analysis of several ML models and feature selection methods. The goal of this research is to design a generic detection mechanism and achieve higher accuracy with minimal false positive rates (FPR). NSL-KDD, UNSW-NB15, and CICIDS2017 datasets are used in the experiment, and results show that our detection model can identify 99.3% of intrusions successfully with the lowest 0.5% of false alarms, which depicts better performance metrics compared to existing solutions.

The distributed denial of service (DDoS) attack is one of the most pernicious threats in cyberspace. Catastrophic failures over the past two decades have resulted in catastrophic and costly disruption of services across all sectors and critical infrastructure. Machine-learning-based approaches have shown promise in developing intrusion detection systems (IDSs) for detecting cyber-attacks, such as DDoS. Herein, we present a solution to detect DDoS attacks through an ensemble-based machine learning approach that combines supervised and unsupervised machine learning ensemble frameworks. This combination produces higher performance in detecting known DDoS attacks using supervised ensemble and for zero-day DDoS attacks using an unsupervised ensemble. The unsupervised ensemble, which employs novelty and outlier detection, is effective in identifying prior unseen attacks. The ensemble framework is tested using three well-known benchmark datasets, NSL-KDD, UNSW-NB15, and CICIDS2017. The results show that ensemble classifiers significantly outperform single-classifier-based approaches. Our model with combined supervised and unsupervised ensemble models correctly detects up to 99.1% of the DDoS attacks, with a negligible rate of false alarms.

Machine Learning (ML) systems are currently used widely. While the requirements elicitation and management processes are well understood and standardized for software systems, they have not been adopted for ML systems. These processes pose additional complexity due to the multitude of stakeholders, their understanding of ML concepts, and their varied requirement orientations. Communication among the stakeholders is a must to generate an appropriate requirements document to guide the development of the system. Another aspect of ML systems is their black box mode of operation. It is essential to know the reasoning behind the predictions of these systems, especially for critical applications. These explainability requirements also must be taken into consideration. This paper reviews state of the art in these two areas to form a basis for developing a framework for stakeholder interaction to facilitate the requirement management of ML systems.

Complex machine learning (ML) algorithms consistently outperform traditional ML models, yielding significantly improved results. Furthermore, these complex system uses large amounts of data with many features to train, increasing their predictive power but reducing our ability to explain them. Thus, complex ML models primarily operate like black boxes. Explaining their inference processes and predictions poses a challenge due to the necessity to account for millions of interacting weights in a complicated way. Still, we need to clarify their inner workings and procedures, as understanding a model can improve transparency and promote trustworthiness. Therefore, ‘Explainability’ assumes pivotal importance, as it enhances accuracy and broadens the scope of applicability for complex ML methods within critical domains. Thus, in this review paper, we have investigated the complete ML system development lifecycle, compiled recommendations to cover explainability gaps from diverse literature sources, listed different methods of interpretability and explainability, and improved our explainability-augmented ML system development life cycle for all stakeholders to enhance trustworthiness.