Anomaly detection has gained considerable attention in the past couple of years. Emerging technologies, such as the Internet of Things (IoT), are known to be among the most critical sources of data streams that produce massive amounts of data continuously from numerous applications. Examining these collected data to detect suspicious events can reduce functional threats and avoid unseen issues that cause downtime in the applications. Due to the dynamic nature of the data stream characteristics, many unresolved problems persist. In the existing literature, methods have been designed and developed to evaluate certain anomalous behaviors in IoT data stream sources. However, there is a lack of comprehensive studies that discuss all the aspects of IoT data processing. Thus, this paper attempts to fill this gap by providing a complete image of various state-of-the-art techniques on the major problems and core challenges in IoT data. The nature of data, anomaly types, learning mode, window model, datasets, and evaluation criteria are also presented. Research challenges related to data evolving, feature-evolving, windowing, ensemble approaches, nature of input data, data complexity and noise, parameters selection, data visualizations, heterogeneity of data, accuracy, and large-scale and high-dimensional data are investigated. Finally, the challenges that require substantial research efforts and future directions are summarized.

Internet of Things (IoT) is a network of “things,” connected via Internet, to collect and exchange data. These “things” can be sensors, actuators, smartphones, wearables, computers, or any object that is interconnected to provide specific services. Similarly, wireless sensor network (WSN), as a part of IoT, forwards the gathered data after sensing any event. The scalability and heterogeneity of IoT offer limited protection and is prone to diverse attacks, including WSN-inherited attacks. Moreover, IPv6 routing protocol for low power and lossy networks (RPL), a de facto routing protocol for IoT networks, also suffers from certain vulnerabilities based on its features and functionalities. Researchers have proposed various mitigation mechanisms for secure networks and routing in IoT. Recently, trust-based approaches have gained tremendous interest from the research community to embed security in IoT networks and routing protocols. In the existing literature, several trust models have been introduced according to the security needs of the IoT system, such as SecTrust, DCTM-IoT, CTRUST, etc. In this research, security issues and requirements of IoT networks and RPL routing protocol are studied with respect to various attacks, such as Blackhole, Spoofing, Rank, etc. Additionally, various mitigation methods and significance of trust models in IoT for secure routing are analyzed. Further, trust metrics in IoT environments, including the open issues and research challenges, as well as the implication of trust as a security paradigm in IoT networks and routing protocols are discussed.

In IoT networks, the de facto Routing Protocol for Low Power and Lossy Networks (RPL) is vulnerable to various attacks. Routing attacks in RPL-based IoT are becoming critical with the increase in the number of IoT applications and devices globally. To address routing attacks in RPL-based IoT, several security solutions have been proposed in literature, such as machine learning techniques, intrusion detection systems, and trust-based approaches. Studies show that trust-based security for IoT is feasible due to its simple integration and resource-constrained nature of smart devices. Existing trust-based solutions have insufficient consideration of nodes’ mobility and are not evaluated for dynamic scenarios to satisfy the requirements of smart applications. This research work addresses the Rank and Blackhole attacks in RPL considering the static as well as mobile nodes in IoT. The proposed Security, Mobility, and Trust-based model (SMTrust) relies on carefully chosen trust factors and metrics, including mobility-based metrics. The evaluation of the proposed model through simulation experiments shows that SMTrust performs better than the existing trust-based methods for securing RPL. The improvisation in terms of topology stability is 46%, reduction in packet loss rate is 45%, and 35% increase in throughput, with only 2.3% increase in average power consumption.

On Instagram, the number of followers is a common success indicator. Hence, followers selling services become a huge part of the market. Influencers become bombarded with fake followers and this causes a business owner to pay more than they should for a brand endorsement. Identifying fake followers becomes important to determine the authenticity of an influencer. This research aims to identify fake users' behavior, and proposes supervised machine learning models to classify authentic and fake users. The dataset contains fake users bought from various sources, and authentic users. There are 17 features used, based on these sources: 6 metadata, 3 media info, 2 engagement, 2 media tags, 4 media similarity. Five machine learning algorithms will be tested. Three different approaches of classification are proposed, i.e. classification to 2-classes and 4-classes, and classification with metadata. Random forest algorithm produces the highest accuracy for the 2-classes (authentic, fake) and 4-classes (authentic, active fake user, inactive fake user, spammer) classification, with accuracy up to 91.76%. The result also shows that the five metadata variables, i.e. number of posts, followers, biography length, following, and link availability are the biggest predictors for the users class. Additionally, descriptive statistics results reveal noticeable differences between fake and authentic users.

The widespread use of drones across various industries is leading to significant transformations. However, the resulting concerns about data security and privacy are quite significant. This section offers a thorough exploration of these important issues, providing insights into the challenges they pose and potential ways to address them. Starting with an overview of the increasing utility of drones, this chapter highlights the importance of strong protocols for data security and privacy. By examining the complexities of data collection and storage, it reveals the different types of data that drones gather, delves into storage techniques, and reveals vulnerabilities, setting the stage for effective countermeasures. At the core of this discussion are cybersecurity risks, which range from cyberattacks on drone systems to unauthorized access and tampering of data. To sum up, this chapter serves as a comprehensive guide to understanding, addressing, and mitigating concerns related to data security and privacy in drone operations.

In the Internet of Things (IoT), the de facto Routing Protocol for Low Power and Lossy Networks (RPL) is susceptible to several disruptive attacks based on its functionalities and features. Among various RPL security solutions, a trust-based security is easy to adapt for resource-constrained IoT environments. In the existing trust-based security for RPL routing attacks, nodes’ mobility is not considered or limited to only the sender nodes. Similarly, these trust-based protocols are not evaluated for mobile IoT environments, particularly regarding RPL attacks. Hence, a trust and mobility-based secure routing protocol is proposed, termed as SMTrust, by critically analysing the trust metrics involving the mobility-based metrics in IoT. SMTrust intends to provide security against RPL Rank and Blackhole attacks. The proposed protocol is evaluated in three different scenarios, including static and mobile nodes in an IoT network. SMTrust is compared with the default RPL objective function, Minimum Rank with Hysteresis Objective Function (MRHOF), SecTrust, DCTM, and MRTS. The evaluation results indicate that the proposed protocol outperforms with respect to packet loss rate, throughput, and topology stability. Moreover, SMTrust is validated using routing protocol requirements analysis to ensure that it fulfils the consistency, optimality, and loop-freeness.