Machine Learning in IoT Security: Current Solutions and Future Challenges

Abstract

The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resourceconstrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML-and DL-based IoT security.

Development of use-specific high-performance cyber-nanomaterial optical detectors by effective choice of machine learning algorithms

“…Then, the test case T is assigned to the group with largest vote or population. In order to find the best k for our system we tried different values for k in the range k = [1,20], and picked k = 7 which performed the best. As mentioned before, kNN is a non-parametric classification algorithm so, no parameters are being learned in kNN.…”

“…In recent decades, the emergence of machine learning (ML) has demonstrated a great potential for enhancing statistical analysis in the field of material science. Nowadays, ML provides popular tools for obtaining information from internet of things (IoT) networks [17][18][19][20][21] such as charge-coupled devices (CCDs), 22,23 complementary metal-oxide-semiconductor (CMOS) detectors, [24][25][26] or regular Silicon-based spectrometers, which are examples of sophisticated networks of optical detectors. 27,28 In physics, on one hand, people employ machine learning to analyze, predict, or interpret physical quantities; on the other hand, underlying physical principle has also been employed to facilitate designing effective machine learning tools.…”

Knowledge Engineering on Internet of Things through Reinforcement Learning

“…In this section, we discuss different areas where RL techniques have been applied with the ability of the machines to practice and learning is recognized as algorithms. Within the security phenomena and its associated challenges including attacks [8], confidentiality and integrity, physical access within the IoTs analysis on the standard and natural policy gradients on actor-critics [9], huge or big data processing in learning [10], user simulation techniques for RL example dialogue management strategies [11], robotic systems during learning, node discovery within IoTs scenarios [12], content-aware computing with close focus on the learning and data screening analytics [13].…”

Towards an Agent-Based Architecture using Deep Reinforcement Learning for Intelligent Internet of Things Applications.pdf

“…These data can be interpreted and analyzed using machine learning (ML) algorithm, which makes good decisions to monitor devices' reactions to the physical world. ML is considered to be one of the most suitable computational paradigms to provide embedded intelligence in the IoT devices [7]. The integration process of IoT and AI, therefore, plays a crucial role in the technology and makes the IoT became intelligent and autonomous by seeing system capabilities grow, including increasing operational efficiency.…”