While the seamless interconnection of IoT devices with the physical realm is envisioned to bring a plethora of critical improvements on many aspects and in diverse domains, it will undoubtedly pave the way for attackers that will target and exploit such devices, threatening the integrity of their data and the reliability of critical infrastructure. The aim of this thesis is to generate cyber threat intelligence related to Internet-scale inference and evaluation of malicious activities generated by compromised IoT devices to facilitate prompt detection, mitigation and prevention of IoT exploitation. In this context, we initially provide a unique taxonomy, which sheds the light on IoT vulnerabilities from five di↵erent perspectives. Subsequently, we address the task of inference and characterization of IoT maliciousness by leveraging active and passive measurements. To support large-scale empirical data analytics in the context of IoT, we made available corresponding raw data through an authenticated platform. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Healthcare organizations, realizing the potential of the Internet of Things (IoT) technology, are rapidly adopting the technology to bring signi cant improvements in the quality and e ectiveness of the service. However, these smart and interconnected devices can act as a potential \back door" into a hospital's IT network, giving attack- ers access to sensitive information. As a result, cyber-attacks on medical IoT devices have been increasing since the last few years. It is a growing concern for all the stakeholders involved, as the impact of such attacks is not just monetary or privacy loss, but the lives of many patients are also at risk. Considering the various kinds of IoT devices one may nd connected to a hospital's network, traditional host-centric security solutions (e.g. antivirus, software patches) are at odds with realistic IoT infrastructure (e.g. constrained hardware, lack of proper built-in security measures). There is a need for security solutions which consider the challenges of IoT devices like heterogeneity of technology and protocols used, limited resources in terms of battery and computation power, etc. Accordingly, the goals of this thesis have been: (1) to provide an in-depth understanding of vulnerabilities of medical IoT devices; (2) to in- troduce a novel approach which uses a microservices-based framework as an adaptive and agile security solution to address the issue. The thesis focuses on OS Fingerprint- ing attacks because of its signi cance for attackers to understand a target's network. In this thesis, we developed three microservices, each one designed to serve a speci c functionality. Each of these microservices has a small footprint with RAM usage of approximately 50 MB. We also suggest how microservices can be used in a real-life scenario as a software-based security solution to secure a hospital's network consisting of di erent IoT devices. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Ubiquitous computing is already weaving itself around us and it is connecting everything to the network of networks. This interconnection of objects to the internet is new computing paradigm called the Internet of Things (IoT) networks. Many capacity and non-capacity constrained devices, such as sensors are connecting to the Internet. These devices interact with each other through the network and provide a new experience to its users. In order to make full use of this ubiquitous paradigm, security on IoT is important. There are problems with privacy concerns regarding certain algorithms that are on IoT, particularly in the area that relates to their avalanche effect means that a small change in the plaintext or key should create a significant change in the ciphertext. The higher the significant change, the higher the security if that algorithm. If the avalanche effect of an algorithm is less than 50% then that algorithm is weak and can create security undesirability in any network. In this, case IoT. In this study, we propose to do the following: (1) Search and select existing block cryptographic algorithms (maximum of ten) used for authentication and encryption from different devices used on IoT. (2) Analyse the avalanche effect of select cryptographic algorithms and determine if they give efficient authentication on IoT. (3) Improve their avalanche effect by designing a mathematical model that improves their robustness against attacks. This is done through the usage of the initial vector XORed with plaintext and final vector XORed with cipher tect. (4) Test the new mathematical model for any enhancement on the avalanche effect of each algorithm as stated in the preceding sentences. (5) Propose future work on how to enhance security on IoT. Results show that when using the proposed method with variation of key, the avalanche effect significantly improved for seven out of ten algorithms. This means that we have managed to improve 70% of algorithms tested. Therefore indicating a substantial success rate for the proposed method as far as the avalanche effect is concerned. We propose that the seven algorithms be replaced by our improved versions in each of their implementation on IoT whenever the plaintext is varied. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

<p dir="ltr">With the ability to connect to networks and send and receive data, Internet of Things (IoT) devices involve associated security risks and threats, for a given environment. These threats are even more of a concern in a Smart Home network, where there is a lack of a dedicated security IT team, unlike a corporate environment. While efficient user interface(UI) and ease of use is at the front and center of IoT devices within Smart Home which enables its wider adoption, often security and privacy have been an afterthought and haven’t kept pace when needed. Therefore, a unsafe possibility exists where malicious actors could exploit vulnerable devices in a domestic home environment.</p><p dir="ltr">This thesis involves a detailed study of the cybersecurity for a Smart Home and also examines the various types of cyberthreats encountered, such as DDoS, Man-In-Middle, Ransomware, etc. that IoT devices face. Given, IoT devices are commonplace in most home automation scenarios, its crucially important to detect intrusions and unauthorized access. Privacy issues are also involved making this an even more pertinent topic. Towards this, various state of the art industry standard tools, such as Nmap, Nessus, Metasploit, etc. were used to gather data on a Smart Home environment to analyze their impacts to detect security vulnerabilities and risks to a Smart Home. Results from the research indicated various vulnerabilities, such as open ports, password vulnerabilities, SSL certificate anomalies and others that exist in many cases, and how precautions when taken in timely manner can help alleviate and bring down those risks.</p><p dir="ltr">Also, an IoT monitoring dashboard was developed based on open-source tools, which helps visualize threats and emphasize the importance of monitoring. The IoT dashboard showed how to raise alerts and alarms based on specific threat conditions or events. In addition, currently available cybersecurity regulations, standards, and guidelines were also examined that can help safeguard against threats to commonly used IoT devices in a Smart Home. It is hoped that the research carried out in this dissertation can help maintain safe and secure Smart Homes and provide direction for future work in the area of Smart Home Cybersecurity.</p>