Fatima Hussain

Fatima Hussain, Rasheed Hussain, Brett Noye et al.

With the advancements in the enterprise-level business development, the demand for new applications and services is overwhelming. For the development and delivery of such applications and services, enterprise businesses rely on Application Programming Interfaces (APIs). In essence, API is a double-edged sword. On one hand, API provides ease of expanding the business through sharing value and utility, but on another hand it raises security and privacy issues. Since the applications usually use APIs to retrieve important data, therefore it is extremely important to make sure that an effective access control and security mechanism are in place , and the data does not fall into wrong hands. In this article, we discuss the current state of the enterprise API security and the role of Machine Learning (ML) in API security. We also discuss the General Data Protection Regulation (GDPR) compliance and its effect on the API security.

Fatima Hussain, Rasheed Hussain, Syed Ali Hassan et al.

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 resource-constrained, 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.