Cyber Forensics and Investigation on Smart Devices

The Internet of Things (IoT) has unquestionably exploded into the forefront of everyone's lives, whether they realise it or not. Internet of Things (IoT) technology is now used in medical devices, transportation, and even in our homes. Devices such as these have the ability to access a great deal of personal information. Because of their diminutive size, these devices have made insufficient efforts to build security into their design. Sensors, cameras, and lights are all examples of Internet of Things (IoT) devices that can be used to automate daily tasks around the home. Smartphones and speakers can be used as remote controllers to operate these gadgets. A smart home's IoT devices collect and process data on motion, temperature, lighting control, and other variables, and they store a wider range of data from more diverse users. A wide variety of smart home devices can make extracting meaningful data difficult because of their differing data storage methods. Data from a variety of smart home devices, as well as data that can be used in digital forensics, must be collected and analysed. Google Nest Hub and Samsung Smart Things are the primary sources of forensic smart home data that will be analysed in this study. As a result, we analysed the smart home data collected using companion apps, web interfaces, and APIs to find information that was relevant to our investigation. Various types of data collected by smart homes are also discussed in the paper, and they can be used as crucial evidence in certain forensic cases. IoT devices in a smart home can be hacked, and we'll investigate how, what data can be recovered, and where it resides after it has been hacked as part of our investigation.

Digital forensics is a part of forensic science that works with the use of digital data generated, saved, and communicated by digital devices as evidence in investigations and judicial actions. It is a growing field in computing that frequently necessitates the intelligent analysis of large amounts of complex data. A form of digital forensics has existed since nearly the invention of computers, however, as digital forensic processes have matured and needs have become more prevalent, forensic capabilities have seen significant advancements in recent years. Rapid advancements in computer science and information technology enable the development of novel techniques and software for digital investigations. Initially, much of the analysis software was unique and proprietary, but over time, specialised analysis software for both the private and governmental sectors became available. Also, it appears that Artificial Intelligence (AI) is an ideal approach for dealing with many of the current problems in digital forensics. It is a well-established branch of modern computer science that may help solve computationally massive or complicated problems in a reasonable amount of time. The goal of this paper is to deliver a high-level overview of digital forensics phases, applications, merits and demerits and widely used software of the domain. The paper also discusses legitimate and legal considerations followed by the scope and role of artificial intelligence for solving complex problems of digital forensics.

The everyday rise in third-party applications across different app stores, mobile operating systems, mobile hardware, and application versions themselves has not only prompted but to a certain degree, necessitated the digital forensics community and digital forensics researchers to investigate various applications that are not inherently supported and parsed by commercial forensics tools. Apart from the capabilities associated with various forensic tools, depending on the case, many forensic investigators may come across the most unthought-of third-party applications for investigation. The only questions then would be: 1) How to parse such data? 2) Is there anything of forensic value? And 3) Some third-party application manufacturers claim that they encrypt data. However, due to the lack of time and technology, in some instances, when there is no access to or knowledge of the decryption method, where and how do find data pertinent to the investigation? Depending on the circumstances mentioned above, is it crucial to come to a firm conclusion about how and where some data resides for certain third-party applications, regardless of what the manufacturers claim. There is a plethora of third-party applications out right now that are utilized by people for a variety of purposes, whether it is for good or bad. Oftentimes, as forensics practitioners, it is our job to dig down and hunt for data that can give us some insight into what was going on in the device, related to a particular application. These applications may offer capabilities such as geolocations, communications, networkrelated artifacts, etc., that can be of value to certain cases. 

Browsers are utilized in one form or another to browse the internet since they have become an essential component of our online lives. Additionally, we may use browsers to navigate the OS's file system in addition to using them for web browsing. It has been noticed that by default, browsers save data including credit card numbers, usernames, passwords, form data, emails, and other sensitive information. Additionally, downloaded media including images, videos, executables, documents, etc. are present in browsers. A user's browsing habits and interests can be inferred from their bookmarks and browsing history. Thus, browsers keep a lot of private data about users and their browsing patterns. Due to the type and volume of data they store with them, they play a crucial role in forensics. Depending on the platform being used, there are a variety of web browsers accessible, including Safari, Chrome, Firefox, IE, and Opera. This chapter will teach us how to do forensics on various types of browsers. The following are some of the numerous places an investigator could look for evidence online like Bookmarks, Downloads, Cache, Cookies by surfing history, and many more. This chapter also discusses browser exploitation and issues involved in forensic investigation.

Mobile phones can occasionally be damaged by water, but forensics professionals can frequently still recover the evidence. The efficacy of various forensic techniques has been examined in this chapter. We use hardware and software tools to gain direct access to the phone's memory chips since a damaged phone might not be powered on and the data port might not function. These include hacking instruments, the ones that may be used to retrieve data from mobile devices. The chapter discusses strategies that apply to Android mobile devices. Additionally, the study only explored techniques for accessing data—not for decrypting it. Mobile devices can sustain water damage as a result of inadvertent exposure to water or deliberate attempts to remove forensic evidence. Traditionally, chip-off analysis has been chosen as a successful data recovery technique for damaged devices, particularly those that have been waterdamaged. In this essay, we investigate what transpires inside portable electronics when they are submerged in water. The likelihood of successfully conducting forensic data recovery on a water-damaged mobile device is high if the right steps are taken and the relevant processes are followed. This chapter discusses common water damage diagnoses as well as efficient restoration techniques.

With the advancement in healthcare technology, the industry is moving from conventional diagnosis methods to digital health platforms. These digital health platforms are useful for patients in different ways like from initial disease diagnosis to drug prescription and maintaining electronic health records. These health records contain a lot of personal information of patients that has high monetary and intelligence value, so such healthcare systems are more vulnerable and targeted by cyber thieves. Several techniques have been implemented by healthcare organizations for the early detection of such cyber threats and for securing the medical records of patients. One such method is machine learning (ML) for the detection of threats or adulterated data due to some payload ransomware. This chapter highlights different healthcare data breaches and the impact of cyber-attacks on medical data using artificial neural networks.