6850 IEEE INTERNET OF THINGS JOURNAL, VOL. 6, NO. 4, AUGUST 2019 How Do I Share My IoT Forensic Experience With the Broader Community? An Automated Knowledge Sharing IoT Forensic Platform Xiaolu Zhang, Kim-Kwang Raymond Choo ,Senior Member, IEEE , and Nicole Lang Beebe, Senior Member, IEEE Abstract —It is challenging for digital forensic practitioners to typical investigation process, two or more investigators located maintain skillset currency, for example knowing where and how in different cities and/or countries may be forensically examin- to extract digital artifacts relevant to investigations from newer, ing the same (type of) device at the same time [see Fig. 1(a)]. emerging devices (e.g., due to the increased variety of data storage schemas across manufacturers and constantly changing models). As the experience and background of both investigators are This paper presents a knowledge sharing platform, developed likely to vary, the outcomes of the forensic investigations may and validated using an Internet of Things dataset released in the also differ (e.g., in terms of the types and extent of artifacts DFRWS 2017–2018 forensic challenge. Specifically, we present being recovered). an automated knowledge-sharing forensic platform that auto- As the diversity of devices increases (e.g., IoT, wearable, matically suggests forensic artifact schemas, derived from case data, but does not include any sensitive data in the final (shared) embedded, and other digital devices), so does the challenge schema. Such artifact schemas are then stored in a schema pool in the forensic examination of such devices [4]. For example, and the platform presents candidate schemas for use in new cases an investigator who is unfamiliar with a particular device that based on the data presented. In this way, investigators need not (s)he has not previously examined, say a Makerbot Replicator learn the forensic profile of a new device from scratch, nor do Z18 Large 3-D Printer, may need to research the device before they have to manually anonymize and share forensic knowledge obtained during the course of an investigation. undertaking any forensic examination. The amount of time required in acquiring new knowledge could be prohibitive to Index Terms —Digital forensics, forensic knowledge as a service smaller or regional forensic investigation teams with limited (ForKaS), Internet of Things (IoT), IoT forensics, knowledge- sharing, ontology. resources. The challenge is compounded with the advent of the IoT. With an estimated 20 billion devices connected to the IoT by 2020, forensic investigators are increasingly challenged to I. I NTRODUCTION extract and analyze forensic artifacts effectively and efficiently. Automated, or semiautomated, knowledge sharing platforms S INTERNET of Things (IoT) devices are becoming that protect case sensitive data are critically needed. commonplace in our society (e.g., in environmental A In addition, due to the sensitive nature of data acquired from monitoring, smart cities, smart business/inventory and prod- forensic examination, data is seldom shared. Such challenges uct management, smart homes/smart building management, can have an impact on the timing, efficiency and consistency of health-care, security and surveillance, and battlefields such as the investigations and findings. For example, Miller et al. [5] Internet of Battlefield Things) [1]–[3], they are also a poten- noted that: tial source of evidence in criminal investigations and civil litigations [4]. Practitioners tasked with investigating new device One of the many challenges in a digital forensic investiga- types, be it for forensic or security related pur- tion is the lack of information and knowledge-sharing between poses, however, do not have the luxury of relying investigators and cases, particularly those involving contempo- on previous work and, with the variety present in rary technologies, such as newer IoT devices. For example, in a AM [additive manufacturing] device architectures, Manuscript received December 12, 2018; revised February 19, 2019 and examination of only the device may be insufficient April 6, 2019; accepted April 16, 2019. Date of publication April 22, 2019; to discover the entirety of the residual data footprint date of current version July 31, 2019. This work was supported in part left by its printing process. Additionally, acquiring by the National Science Foundation CREST under Grant HRD-1736209. (Corresponding author: Kim-Kwang Raymond Choo.) a representative sample of devices for study may be X. Zhang and N. L. Beebe are with the Department of Information Systems cost prohibitive or the exploratory analysis of a par- and Cyber Security, University of Texas at San Antonio, San Antonio, TX ticular device may be inadvisable due to operational, 78249 USA (e-mail: [email protected]; [email protected]). K.-K. R. Choo is with the Department of Information Systems and Cyber evidentiary, or resource constraints. Security, University of Texas at San Antonio, San Antonio, TX 78249 USA, and also with the Department of Electrical and Computer Engineering, Hence, we posit the importance of knowledge shar- University of Texas at San Antonio, San Antonio, TX 78249 USA (e-mail: [email protected]). ing among the forensic community—particularly technology Digital Object Identifier 10.1109/JIOT.2019.2912118 enabled automated, or semiautomated, sharing platforms that 2327-4662 c2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. ZHANG et al. : HOW DO I SHARE MY IoT FORENSIC EXPERIENCE? 6851 (a) (b) Fig. 1. Overview of the proposed ForKaS platform. (a) Traditional investigation process. (b) Proposed investigation process. strictly protect sensitive case data. Investigators need mech- can draw on prior experience and findings from the anisms for efficiently and effectively sharing knowledge international forensic community (e.g., INTERPOL and about forensic artifacts—their location, their forensic implica- EUROPOL) to benefit their own investigations. This tions, their data structures, etc.—without sharing the artifacts also minimizes the steep learning curve associated with themselves. Specifically, in this paper we present a foren- newer technologies or technologies that the investigator sic knowledge-sharing platform [see Fig. 1(b)], designed to is unfamiliar with. facilitate the sharing of knowledge/experience via a schema 3) Forensic investigators can provide other stakeholders generated from another investigation of a similar device. Such (e.g., investigation officers and prosecutors) an expe- a schema captures the knowledge of an investigator who dited, high-level overview of the case, using existing had previously examined a particular device, and contains schema. information, such as type and nature of artifacts that could be The remainder of this paper is organized as follows. The lit- acquired, to guide another investigator’s forensic examination. erature review is presented in Section II. Section III introduces Our proposed forensic knowledge as a service (ForKaS) the structure of the proposed ForKaS platform. In Section IV, platform builds on the conventional four-layer digital forensic we present two key algorithms required in the prototype imple- framework (collection, extraction, analysis, and visualization), mentation. We then present the case study in Section V and the by introducing the “abstraction” layer where the schema can be discussion in Section VI. Lastly in Section VII, we conclude generated and shared. We also implement a proof of concept this paper and discuss future work. and evaluate the prototype using the publicly available smart home dataset released for the DFRWS 2017–2018 forensic II. R ELATED WORK challenge. Specifically, we demonstrate how one can effi- To keep pace with the constant evolution of consumer tech- ciently generate a schema based on the examination of the nologies [6], [7], there has been an increased focus on digital smart home dataset (comprising a number of IoT devices) and forensic research, such as IoT forensics (see Section II-A). then share the generated schema via the platform; thereby, Since the definition of “IoT device” is relatively broad, in increasing other investigators’ efficient and effective extraction this section we will focus on a smart home scenario where of evidence on devices they may or may not have analyzed an IoT device usually refers to a device that is not conven- before. We also demonstrate how the schema can then be uti- tionally “smart,” or one that has an independent IP address lized by a forensic investigator examining a different Android (e.g., smart LED light bulb, IP camera, smart watch/TV, and device and an iOS device. a motion detector), and an Internet-connected device that can We regard the key benefits of ForKaS to include the be used to control the smart device (e.g., a smart phone). following. We will also briefly review prior attempts to share foren- 1) Allow the sharing of knowledge and experience between sic knowledge/experience, for example as forensic taxonomies forensic investigators without the need to share the sen- (see Section II-B). sitive data acquired from the forensic investigation or dataset. A. IoT Forensics 2) The inconsistency and time required of a forensic inves- IoT forensics can be broadly categorized into device tigation finding could be minimized, since investigators level forensics, network