Browsing by Author "Neyaz, Ashar"
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Item IoT Network Attack Detection using Supervised Machine Learning(International Journal of Artificial Intelligence and Expert Systems, 2021) Krishnan, Sundar; Neyaz, Ashar; Liu, QingzhongThe use of supervised learning algorithms to detect malicious traffic can be valuable in designing intrusion detection systems and ascertaining security risks. The Internet of things (IoT) refers to the billions of physical, electronic devices around the world that are often connected over the Internet. The growth of IoT systems comes at the risk of network attacks such as denial of service (DoS) and spoofing. In this research, we perform various supervised feature selection methods and employ three classifiers on IoT network data. The classifiers predict with high accuracy if the network traffic against the IoT device was malicious or benign. We compare the feature selection methods to arrive at the best that can be used for network intrusion predictionItem NVMe-Assist: A Novel Theoretical Framework for Digital Forensics A Case Study on NVMe Storage Devices and Related Artifacts on Windows 10(2022-08-01T05:00:00.000Z) Neyaz, Ashar; Shashidhar, Narasimha K; Varol, Cihan; Rasheed, Amar AWith ever-advancing changes in technology come implications for the digital forensics community. In this document, we use the term digital forensics to denote the scientific investigatory procedure for digital crimes and attacks. Digital forensics examiners often find it challenging when new devices are used for nefarious activities. The examiners gather evidence from these devices based on supporting literature. Multiple factors contribute to a lack of research on a particular device or technology. The most common factors are that the technology is new to the market, and there has not been much time to conduct sufficient research. It is also likely that the technology is not popular enough to garner research attention. If an examiner encounters such a device, they are often required to develop impromptu solutions to investigate such a case. Sometimes, examiners have to review their examination processes on model devices that labs are necessitated to purchase to see if existing methods suffice. This ad-hoc approach adds time and additional expense before actual analysis can commence. In this research, we investigate a new storage technology called Non-Volatile Memory Express (NVMe). This technology uses Peripheral Component Interconnect (PCIe) mechanics for its working. Since this storage technology is relatively new, it lacks a substantial digital forensics foundation to draw upon to conduct a forensics investigation. Additionally, to the best of our knowledge, there is an insufficient body of work to conduct sound forensics research on such devices. To this end, our framework, NVMe-Assist puts forth a strong theoretical foundation thatempowers digital forensics examiners in conducting analysis onNVMedevices, including wear-leveling, TRIM, Prefetch files, Shellbag, and BootPerfDiagLogger.etl. Lastly, we have also worked on creating the NVMe-Assist tool using Python. This tool parses the partition tables in the boot sector and is the upgrade of the mmls tool of The Sleuth Kit command-line tools. Our tool currently supports E01, and RAW files of the physical acquisition of hard-disk drives (HDDs), solid-state drives (SSDs), NVMe SSDs, and USB flash drives as data source files. To add to that, the tool works on both the MBR (Master Boot Record) and GPT (GUID Partition Table) style partitions.Item Security, Privacy and Steganographic Analysis of FaceApp and TikTo(International Journal of Computer Science and Security, 2020) Krishnan, Sundar; Liu, Qingzhong; Neyaz, Ashar; Kumar, Avinash; Placker, JessicaSmartphone applications (Apps) can be addictive for users due to their uniqueness, ease-of-use, trendiness, and growing popularity. The addition of Artificial Intelligence (AI) into their functionality has rapidly gained popularity with smartphone users. Over the years, very few smartphone Apps have quickly gained immense popularity like FaceApp and TikTok. FaceApp boasts of using AI to transform photos of human faces using its powerful facial recognition capabilities. FaceApp has been the target of ensuing backlash against it driving the market for a number of other similar yet lesser-known clones into the top ranks of the App stores. TikTok offers video editing and sharing of short video clips whereby making them charming, funny, cringe-inducing, and addictive to the younger generation. FaceApp and TikTok have been the targets of the media, privacy watchdogs, and governments over worries of privacy, ethnicity filters, data misuse, anti-forensics, and security. In this paper, the authors forensically review FaceApp and TikTok Apps from the Android Play Store, for their data ownership, data management, privacy concerns, steganographic use, and overall security posture.