Machine Learning: Models, Challenges, and Research Directions

Abstract

Machine learning techniques have emerged as a transformative force, revolutionizing various application domains, particularly cybersecurity. The development of optimal machine learning applications requires the integration of multiple processes, such as data pre-processing, model selection, and parameter optimization. While existing surveys have shed light on these techniques, they have mainly focused on specific application domains. A notable gap that exists in current studies is the lack of a comprehensive overview of machine learning architecture and its essential phases in the cybersecurity field. To address this gap, this survey provides a holistic review of current studies in machine learning, covering techniques applicable to any domain. Models are classified into four categories: supervised, semi-supervised, unsupervised, and reinforcement learning. Each of these categories and their models are described. In addition, the survey discusses the current progress related to data pre-processing and hyperparameter tuning techniques. Moreover, this survey identifies and reviews the research gaps and key challenges that the cybersecurity field faces. By analyzing these gaps, we propose some promising research directions for the future. Ultimately, this survey aims to serve as a valuable resource for researchers interested in learning about machine learning, providing them with insights to foster innovation and progress across diverse application domains.

A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks

“…where, p is a dimensional column vector and c > 0 is a regularization parameter. For X is the original input (i.e., image) affiliated with the specified label l, X is the adversarial sample affiliated with the specified label t (i.e., f (X ) = t = f (X) = l, where f (X + t) is the loss function defined by below Expression (11):…”

“…To detect and mitigate cyberattacks, Intrusion Detection Systems (IDSs) [4], Malware Detection Systems (MDSs) [5], and Device Identification Systems (DISs) [6] are often employed to monitor IoT network traffic and detect malicious activities [7][8][9]. ML [10,11] techniques, including Deep Learning (DL) [12,13], have shown promise in enhancing the effectiveness of these systems, by leveraging the ability of ML algorithms to learn from data and identify patterns that indicate anomalous behavior.…”