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The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems. 

Phishing attack countermeasures have previously relied on technical solutions or user training. As phishing attacks continue to impact users resulting in adverse consequences, mitigation efforts may be strengthened through an understanding of how user characteristics predict phishing susceptibility. Several studies have identified factors of interest that may contribute to susceptibility. Others have begun to build predictive models to better understand the relationships among factors in addition to their prediction power, although these studies have only used a handful of predictors. As a step toward creating a holistic model to predict phishing susceptibility, it was first necessary to catalog all known predictors that have been identified in the literature. We identified 32 predictors related to personality traits, demographics, educational background, cybersecurity experience and beliefs, platform experience, email behaviors, and work commitment style. 

Objective To understand how aspects of vishing calls (phishing phone calls) influence perceived visher honesty. Background Little is understood about how targeted individuals behave during vishing attacks. According to truth-default theory, people assume others are being honest until something triggers their suspicion. We investigated whether that was true during vishing attacks. Methods Twenty-four participants read written descriptions of eight real-world vishing calls. Half included highly sensitive requests; the remainder included seemingly innocuous requests. Participants rated visher honesty at multiple points during conversations. Results Participants initially perceived vishers to be honest. Honesty ratings decreased before requests occurred. Honesty ratings decreased further in response to highly sensitive requests, but not seemingly innocuous requests. Honesty ratings recovered somewhat, but only after highly sensitive requests. Conclusions The present results revealed five important insights: (1) people begin vishing conversations in the truth-default state, (2) certain aspects of vishing conversations serve as triggers, (3) other aspects of vishing conversations do not serve as triggers, (4) in certain situations, people’s perceptions of visher honesty improve, and, more generally, (5) truth-default theory may be a useful tool for understanding how targeted individuals behave during vishing attacks. Application Those developing systems that help users deal with suspected vishing attacks or penetration testing plans should consider (1) targeted individuals’ truth-bias, (2) the influence of visher demeanor on the likelihood of deception detection, (3) the influence of fabricated situations surrounding vishing requests on the likelihood of deception detection, and (4) targeted individuals’ lack of concern about seemingly innocuous requests.