ترغب بنشر مسار تعليمي؟ اضغط هنا

Memory Forensic Analysis of MQTT Devices

105   0   0.0 ( 0 )
 نشر من قبل Palak Rajdev
 تاريخ النشر 2019
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

Internet of Things is revolutionizing the current era with its vast usage in number of fields such as medicine, automation, home security, smart cities, etc. As these IoT devices uses are increasing, the threat to its security and to its application protocols are also increasing. Traffic passing over these protocol if intercepted, could reveal sensitive information and result in taking control of the entire IoT network. Scope of this paper is limited to MQTT protocol. MQTT (MQ Telemetry Transport) is a light weight protocol used for communication between IoT devices. There are multiple brokers as well as clients available for publishing and subscribing to services. For security purpose, it is essential to secure the traffic, broker and end client application. This paper demonstrates extraction of sensitive data from the devices which are running broker and client application.


قيم البحث

اقرأ أيضاً

Network Forensics (NFs) is a branch of digital forensics which used to detect and capture potential digital crimes over computer networked environments crime. Network Forensic Tools (NFTs) and Network Forensic Processes (NFPs) have abilities to exami ne networks, collect all normal and abnormal traffic/data, help in network incident analysis, and assist in creating an appropriate incident detection and reaction and also create a forensic hypothesis that can be used in a court of law. Also, it assists in examining the internal incidents and exploitation of assets, attack goals, executes threat evaluation, also by evaluating network performance. According to existing literature, there exist quite a number of NFTs and NTPs that are used for identification, collection, reconstruction, and analysing the chain of incidents that happen on networks. However, they were vary and differ in their roles and functionalities. The main objective of this paper, therefore, is to assess and see the distinction that exist between Network Forensic Tools (NFTs) and Network Forensic Processes (NFPs). Precisely, this paper focuses on comparing among four famous NFTs: Xplico, OmniPeek, NetDetector, and NetIetercept. The outputs of this paper show that the Xplico tool has abilities to identify, collect, reconstruct, and analyse the chain of incidents that happen on networks than other NF tools.
Given the ubiquity of memory in commodity electronic devices, fingerprinting memory is a compelling proposition, especially for low-end Internet of Things (IoT) devices where cryptographic modules are often unavailable. However, the use of fingerprin ts in security functions is challenged by the inexact reproductions of fingerprints from the same device at different time instances due to various noise sources causing, small, but unpredictable variations in fingerprint measurements. Our study formulates a novel and textit{pragmatic} approach to achieve the elusive goal of affording highly reliable fingerprints from device memories. We investigate the transformation of raw fingerprints into a noise-tolerant space where the generation of fingerprints from memory biometrics is intrinsically highly reliable. Further, we derive formal performance bounds to support practitioners to adopt our methods for practical applications. Subsequently, we demonstrate the expressive power of our formalization by using it to investigate the practicability of extracting noise-tolerant fingerprints from commodity devices. We have employed a set of 38 memory chips including SRAM (69,206,016 cells), Flash (3,902,976 cells) and EEPROM (32,768 cells) ubiquitously embedded in low-end commodity devices from 6 different manufacturers for extensive experimental validations. Our results demonstrate that noise-tolerant fingerprints -- achieving a key failure rate less than $10^{-6}$ -- can always be efficiently afforded from tested memories with a solely fingerprint snap-shot enrollment. Further, we employ a low-cost wearable Bluetooth inertial sensor and demonstrate a practical, end-to-end implementation of a remote attestation security function built upon a root key from noise-tolerant SRAM fingerprints generated on demand and at run-time.
Internet of Things (IoT) devices have expanded the horizon of digital forensic investigations by providing a rich set of new evidence sources. IoT devices includes health implants, sports wearables, smart burglary alarms, smart thermostats, smart ele ctrical appliances, and many more. Digital evidence from these IoT devices is often extracted from third party sources, e.g., paired smartphone applications or the devices back-end cloud services. However vital digital evidence can still reside solely on the IoT device itself. The specifics of the IoT devices hardware is a black-box in many cases due to the lack of proven, established techniques to inspect IoT devices. This paper presents a novel methodology to inspect the internal software activities of IoT devices through their electromagnetic radiation emissions during live device investigation. When a running IoT device is identified at a crime scene, forensically important software activities can be revealed through an electromagnetic side-channel analysis (EM-SCA) attack. By using two representative IoT hardware platforms, this work demonstrates that cryptographic algorithms running on high-end IoT devices can be detected with over 82% accuracy, while minor software code differences in low-end IoT devices could be detected over 90% accuracy using a neural network-based classifier. Furthermore, it was experimentally demonstrated that malicious modification of the stock firmware of an IoT device can be detected through machine learning-assisted EM-SCA techniques. These techniques provide a new investigative vector for digital forensic investigators to inspect IoT devices.
The unprecedented ease and ability to manipulate video content has led to a rapid spread of manipulated media. The availability of video editing tools greatly increased in recent years, allowing one to easily generate photo-realistic alterations. Suc h manipulations can leave traces in the metadata embedded in video files. This metadata information can be used to determine video manipulations, brand of video recording device, the type of video editing tool, and other important evidence. In this paper, we focus on the metadata contained in the popular MP4 video wrapper/container. We describe our method for metadata extractor that uses the MP4s tree structure. Our approach for analyzing the video metadata produces a more compact representation. We will describe how we construct features from the metadata and then use dimensionality reduction and nearest neighbor classification for forensic analysis of a video file. Our approach allows one to visually inspect the distribution of metadata features and make decisions. The experimental results confirm that the performance of our approach surpasses other methods.
73 - Xiaoyu Du , Mark Scanlon 2019
The ever increasing volume of data in digital forensic investigation is one of the most discussed challenges in the field. Usually, most of the file artefacts on seized devices are not pertinent to the investigation. Manually retrieving suspicious fi les relevant to the investigation is akin to finding a needle in a haystack. In this paper, a methodology for the automatic prioritisation of suspicious file artefacts (i.e., file artefacts that are pertinent to the investigation) is proposed to reduce the manual analysis effort required. This methodology is designed to work in a human-in-the-loop fashion. In other words, it predicts/recommends that an artefact is likely to be suspicious rather than giving the final analysis result. A supervised machine learning approach is employed, which leverages the recorded results of previously processed cases. The process of features extraction, dataset generation, training and evaluation are presented in this paper. In addition, a toolkit for data extraction from disk images is outlined, which enables this method to be integrated with the conventional investigation process and work in an automated fashion.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا