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Driven by the rapid escalation of its utilization, as well as ramping commercialization, Internet of Things (IoT) devices increasingly face security threats. Apart from denial of service, privacy, and safety concerns, compromised devices can be used as enablers for committing a variety of crime and e-crime. Despite ongoing research and study, there remains a significant gap in the thorough analysis of security challenges, feasible solutions, and open secure problems for IoT. To bridge this gap, we provide a comprehensive overview of the state of the art in IoT security with a critical investigation-based approach. This includes a detailed analysis of vulnerabilities in IoT-based systems and potential attacks. We present a holistic review of the security properties required to be adopted by IoT devices, applications, and services to mitigate IoT vulnerabilities and, thus, successful attacks. Moreover, we identify challenges to the design of security protocols for IoT systems in which constituent devices vary markedly in capability (such as storage, computation speed, hardware architecture, and communication interfaces). Next, we review existing research and feasible solutions for IoT security. We highlight a set of open problems not yet addressed among existing security solutions. We provide a set of new perspectives for future research on such issues including secure service discovery, on-device credential security, and network anomaly detection. We also provide directions for designing a forensic investigation framework for IoT infrastructures to inspect relevant criminal cases, execute a cyber forensic process, and determine the facts about a given incident. This framework offers a means to better capture information on successful attacks as part of a feedback mechanism to thwart future vulnerabilities and threats. This systematic holistic review will both inform on current challenges in IoT security and ideally motivate their future resolution.

Spam related cyber crimes have become a serious threat to society. Current spam research mainly aims to detect spam more effectively. We believe the prosecution of spammers is a more effective way of stopping spam emails than filtering, therefore more research is needed to help forensic investigators to collect useful evidence. This research proposes an algorithm for clustering spam domains extracted from spam emails based on the hosting IP addresses and tracing the domains over a period of time. The results reveal several facts that merit law enforcement attention: many seemingly unrelated spam campaigns are actually related; spammers have a sophisticated mechanism for combating URL blacklisting by registering many new domain names every day and flushing out old domains; the domains are hosted at different IP addresses across several networks, mostly in China where legislation is not as tight as in US; old IP addresses are replaced by new ones from time to time, but still show strong correlation among them. These facts lead to the conclusion that spam-related cyber crimes are operated by well-organized criminal syndicates that have sufficient manpower to distribute a huge volume of spam through bots, purchase a large number of domain names and hosting servers and maintain websites to sell counterfeit products online. Traditional law enforcements technology has not scaled well in cases involving millions of data elements. This paper demonstrates an effective use of data mining to respond to this challenge.