Computer Sciences Commons^™

Digital Forensics For Mobility As A Service Platform: Analysis Of Uber Application On Iphone And Cloud, Nina Matulis, Umit Karabiyik

Annual ADFSL Conference on Digital Forensics, Security and Law

Uber is a ride-hailing smartphone application (app) that allows users to order a ride in a highly efficient manner. The Uber app provides Mobility as a Service and allows users to easily order a ride in a private car with just a few clicks. Uber stores large amounts of data on both the mobile device the app is being used on, and in the cloud. Examples of this data include geolocation data, date/time, origin/destination addresses, departure/arrival times, and distance. Uber geolocation data has been previously researched to investigate the privacy of the Uber app; however, there is minimal research relating …

Smart Home Forensics: Identifying Ddos Attack Patterns On Iot Devices, Samuel Ho, Hope Greeson, Umit Karabiyik

Annual ADFSL Conference on Digital Forensics, Security and Law

Smart homes are becoming more common as more people integrate IoT devices into their home environment. As such, these devices have access to personal data on their homeowners’ networks. One of the advantages of IoT devices is that they are compact. However, this limits the incorporation of security measures in their hardware. Misconfigured IoT devices are commonly the target of malicious attacks. Additionally, distributed denial-of-service attacks are becoming more common due to applications and software that provides users with easy-to-use user interfaces. Since one vulnerable device is all an attacker needs to launch an attack on a network, in regards …

Microsoft Defender Will Be Defended: Memoryranger Prevents Blinding Windows Av, Denis Pogonin, Igor Korkin, Phd

Annual ADFSL Conference on Digital Forensics, Security and Law

Windows OS is facing a huge rise in kernel attacks. An overview of popular techniques that result in loading kernel drivers will be presented. One of the key targets of modern threats is disabling and blinding Microsoft Defender, a default Windows AV. The analysis of recent driver-based attacks will be given, the challenge is to block them. The survey of user- and kernel-level attacks on Microsoft Defender will be given. One of the recently published attackers’ techniques abuses Mandatory Integrity Control (MIC) and Security Reference Monitor (SRM) by modifying Integrity Level and Debug Privileges for the Microsoft Defender via syscalls. …

Detection Of Overlapping Passive Manipulation Techniques In Image Forensics, Gianna S. Lint, Umit Karabiyik

Annual ADFSL Conference on Digital Forensics, Security and Law

With a growing number of images uploaded daily to social media sites, it is essential to understand if an image can be used to trace its origin. Forensic investigations are focusing on analyzing images that are uploaded to social media sites resulting in an emphasis on building and validating tools. There has been a strong focus on understanding active manipulation or tampering techniques and building tools for analysis. However, research on manipulation is often studied in a vacuum, involving only one technique at a time. Additionally, less focus has been placed on passive manipulation, which can occur by simply uploading …

Deeppose: Detecting Gps Spoofing Attack Via Deep Recurrent Neural Network, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

The Global Positioning System (GPS) has become a foundation for most location-based services and navigation systems, such as autonomous vehicles, drones, ships, and wearable devices. However, it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools. Pervasive tools, such as Fake GPS, Lockito, and software-defined radio, enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being detected. Furthermore, it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion sensors. To this …

"Mystify": A Proactive Moving-Target Defense For A Resilient Sdn Controller In Software Defined Cps, Mohamed Azab, Mohamed Samir, Effat Samir

Electrical & Computer Engineering Faculty Publications

The recent devastating mission Cyber–Physical System (CPS) attacks, failures, and the desperate need to scale and to dynamically adapt to changes, revolutionized traditional CPS to what we name as Software Defined CPS (SD-CPS). SD-CPS embraces the concept of Software Defined (SD) everything where CPS infrastructure is more elastic, dynamically adaptable and online-programmable. However, in SD-CPS, the threat became more immanent, as the long-been physically-protected assets are now programmatically accessible to cyber attackers. In SD-CPSs, a network failure hinders the entire functionality of the system. In this paper, we present MystifY, a spatiotemporal runtime diversification for Moving-Target Defense (MTD) to secure …

A Channel State Information Based Virtual Mac Spoofing Detector, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

Physical layer security has attracted lots of attention with the expansion of wireless devices to the edge networks in recent years. Due to limited authentication mechanisms, MAC spoofing attack, also known as the identity attack, threatens wireless systems. In this paper, we study a new type of MAC spoofing attack, the virtual MAC spoofing attack, in a tight environment with strong spatial similarities, which can create multiple counterfeits entities powered by the virtualization technologies to interrupt regular services. We develop a system to effectively detect such virtual MAC spoofing attacks via the deep learning method as a countermeasure. …

Zero Trust Architecture, Ahmed Abdullah Syed

All Student Theses and Dissertations

Cyber threats are becoming increasingly sophisticated, and IT organizations need to continue to modernize their approaches to cybersecurity in light of today's dynamic cyber threat environment. Cybersecurity professionals must adopt a zero-trust security model and the mindset needed to engineer and operate a system specifically designed to operate according to zero trust principles in order to ensure the security of sensitive data, systems, and services as enterprise networks become increasingly dispersed and complex.

Lessons Learnt Conducting Capture The Flag Cybersecurity Competition During Covid-19, Kee Hock Tan, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

This innovative practice full paper describes our experiences conducting cybersecurity capture the flag (CTF) competition for cybersecurity enthusiast participants (inclusive of both tertiary students and working professionals) local and abroad during the COVID-19 pandemic. Learning and appreciation of cybersecurity concepts for our participants with little to no technical background can be challenging. Gamification methods such as capture the flag competition style is a popular form of cybersecurity education to help participants overcome this challenge and identify talents. Participants get to apply theoretical concepts in a controlled environment, solve hands-on tasks in an informal, game-like setting and gain hands-on active learning …

Authenticated Data Redaction With Accountability And Transparency, Jinhua Ma, Xinyi Huang, Yi Mu, Robert H. Deng

Research Collection School Of Computing and Information Systems

A common practice in data redaction is removing sensitive information prior to data publication or release. In data-driven applications, one must be convinced that the redacted data is still trustworthy. Meanwhile, the data redactor must be held accountable for (malicious) redaction, which could change/hide the meaning of the original data. Motivated by these concerns, we present a novel solution for authenticated data redaction based on a new Redactable Signature Scheme with Implicit Accountability (RSS - IA). In the event of a dispute, not only the original data signer but also the redactor can generate an evidence tag to unequivocally identify …

Novel Secure Outsourcing Of Modular Inversion For Arbitrary And Variable Modulus, Chengliang Tian, Jia Yu, Hanlin Zhang, Haiyang Xue, Cong Wang, Kui Ren

Research Collection School Of Computing and Information Systems

In cryptography and algorithmic number theory, modular inversion is viewed as one of the most common and time-consuming operations. It is hard to be directly accomplished on resource-constrained clients (e.g., mobile devices and IC cards) since modular inversion involves a great amount of operations on large numbers in practice. To address the above problem, this paper proposes a novel unimodular matrix transformation technique to realize secure outsourcing of modular inversion. This technique makes our algorithm achieve several amazing properties. First, to the best of our knowledge, it is the first secure outsourcing computation algorithm that supports arbitrary and variable modulus, …

On Implementing And Testing The Rsa Algorithm, Kien Trung Le

Senior Independent Study Theses

In this work, we give a comprehensive introduction to the RSA cryptosystem, implement it in Java, and compare it empirically to three other RSA implementations. We start by giving an overview of the field of cryptography, from its primitives to the composite constructs used in the field. Then, the paper presents a basic version of the RSA algorithm. With this information in mind, we discuss several problems with this basic conception of RSA, including its speed and some potential attacks that have been attempted. Then, we discuss possible improvements that can make RSA runs faster and more secure. On the …

More Amazon Than Mafia: Analysing A Ddos Stresser Service As Organised Cybercrime, Roberto Musotto, David S. Wall

Research outputs 2014 to 2021

© 2020, The Author(s). The internet mafia trope has shaped our knowledge about organised crime groups online, yet the evidence is largely speculative and the logic often flawed. This paper adds to current knowledge by exploring the development, operation and demise of an online criminal group as a case study. In this article we analyse a DDoS (Distributed Denial of Service) stresser (also known as booter) which sells its services online to enable offenders to launch attacks. Using Social Network Analysis to explore the service operations and payment systems, our findings show a central business model that is similar to …

A Low-Cost Machine Learning Based Network Intrusion Detection System With Data Privacy Preservation, Jyoti Fakirah, Lauhim Mahfuz Zishan, Roshni Mooruth, Michael L. Johnstone, Wencheng Yang

Research outputs 2022 to 2026

Network intrusion is a well-studied area of cyber security. Current machine learning-based network intrusion detection systems (NIDSs) monitor network data and the patterns within those data but at the cost of presenting significant issues in terms of privacy violations which may threaten end-user privacy. Therefore, to mitigate risk and preserve a balance between security and privacy, it is imperative to protect user privacy with respect to intrusion data. Moreover, cost is a driver of a machine learning-based NIDS because such systems are increasingly being deployed on resource-limited edge devices. To solve these issues, in this paper we propose a NIDS …

Camouflaged Poisoning Attack On Graph Neural Networks, Chao Jiang, Yi He, Richard Chapman, Hongyi Wu

Computer Science Faculty Publications

Graph neural networks (GNNs) have enabled the automation of many web applications that entail node classification on graphs, such as scam detection in social media and event prediction in service networks. Nevertheless, recent studies revealed that the GNNs are vulnerable to adversarial attacks, where feeding GNNs with poisoned data at training time can lead them to yield catastrophically devastative test accuracy. This finding heats up the frontier of attacks and defenses against GNNs. However, the prior studies mainly posit that the adversaries can enjoy free access to manipulate the original graph, while obtaining such access could be too costly in …

Ready Raider One: Exploring The Misuse Of Cloud Gaming Services, Guannan Liu, Daiping Liu, Shuai Hao, Xing Gao, Kun Sun, Haining Wang

Computer Science Faculty Publications

Cloud gaming has become an emerging computing paradigm in recent years, allowing computer games to offload complex graphics and logic computation to the cloud. To deliver a smooth and high-quality gaming experience, cloud gaming services have invested abundant computing resources in the cloud, including adequate CPUs, top-tier GPUs, and high-bandwidth Internet connections. Unfortunately, the abundant computing resources offered by cloud gaming are vulnerable to misuse and exploitation for malicious purposes. In this paper, we present an in-depth study on security vulnerabilities in cloud gaming services. Specifically, we reveal that adversaries can purposely inject malicious programs/URLs into the cloud gaming services …

Anatomy Of An Internet Hijack And Interception Attack: A Global And Educational Perspective, Ben A. Scott, Michael N. Johnstone, Patryk Szewczyk

Research outputs 2022 to 2026

The Internet’s underlying vulnerable protocol infrastructure is a rich target for cyber crime, cyber espionage and cyber warfare operations. The stability and security of the Internet infrastructure are important to the function of global matters of state, critical infrastructure, global e-commerce and election systems. There are global approaches to tackle Internet security challenges that include governance, law, educational and technical perspectives. This paper reviews a number of approaches to these challenges, the increasingly surgical attacks that target the underlying vulnerable protocol infrastructure of the Internet, and the extant cyber security education curricula; we find the majority of predominant cyber security …

Cyber Security Curriculum In Western Australian Primary And Secondary Schools: Interim Report: Curriculum Mapping, Nicola Johnson, Ahmed Ibrahim, Leslie Sikos, Cheryl Glowrey

Research outputs 2022 to 2026

Cyber-crime poses a significant threat to Australians—think of, for example, how scams take advantage of vulnerable people and systems. There is a need to educate people from an early age to protect them from cyberthreats.

Consistent with the increasing prevalence of cyberthreats to individuals and organisations in Australia, the national Australian curriculum has been updated (version 9.0) to include specific content for cyber security for primary and secondary students up to Year 10. Endorsed by Education Ministers in April 2022, the Western Australian School Curriculum and Standards Authority (SCSA) completed a detailed audit of the endorsed Australian Curriculum version 9.0 …

A Review On Security Issues And Solutions Of The Internet Of Drones, Wencheng Yang, Song Wang, Xuefei Yin, Xu Wang, Jiankun Hu

Research outputs 2022 to 2026

The Internet of Drones (IoD) has attracted increasing attention in recent years because of its portability and automation, and is being deployed in a wide range of fields (e.g., military, rescue and entertainment). Nevertheless, as a result of the inherently open nature of radio transmission paths in the IoD, data collected, generated or handled by drones is plagued by many security concerns. Since security and privacy are among the foremost challenges for the IoD, in this paper we conduct a comprehensive review on security issues and solutions for IoD security, discussing IoD-related security requirements and identifying the latest advancement in …

Edge-Iiotset: A New Comprehensive Realistic Cyber Security Dataset Of Iot And Iiot Applications For Centralized And Federated Learning, Mohamed A. Ferrag, Othmane Friha, Djallel Hamouda, Leandros Maglaras, Helge Janicke

Research outputs 2022 to 2026

In this paper, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the dataset has been generated using a purpose-built IoT/IIoT testbed with a large representative set of devices, sensors, protocols and cloud/edge configurations. The IoT data are generated from various IoT devices (more than 10 types) such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame …

Asymmetrical Trusted Technology Networks In Developing Economies: A Case Study On Critical Infrastructure In Bhutan, Pratima Pradhan, Bal Subba, Thinley Jamtsho, Ganga Ram Ghimiray, David M. Cook

Research outputs 2022 to 2026

Developing Nations are subject to amplified challenges in terms of the integration of technology, and the exposure to non-domestic opportunism from larger neighboring economies. These challenges are recognizable as asymmetrical differences between what is seen as the normative list of critical infrastructures, and the specialisms that can dominate an emerging economy with early maturity technology networks. This paper discusses the case of Bhutan and demonstrates the need for strengthened approaches to trusted networks to ensure the reliability and continuity of the Nation's critical infrastructures. The paper also links the importance of trusted information sharing networks as part of an overarching …

Multimedia Security And Privacy Protection In The Internet Of Things: Research Developments And Challenges, Wencheng Yang, Song Wang, Jiankun Hu, Nickson M. Karie

Research outputs 2022 to 2026

With the rapid growth of the internet of things (IoT), huge amounts of multimedia data are being generated from and/or exchanged through various IoT devices, systems and applications. The security and privacy of multimedia data have, however, emerged as key challenges that have the potential to impact the successful deployment of IoT devices in some data-sensitive applications. In this paper, we conduct a comprehensive survey on multimedia data security and privacy protection in the IoT. First, we classify multimedia data into different types and security levels according to application areas. Then, we analyse and discuss the existing multimedia data protection …

A Practical Fog-Based Privacy-Preserving Online Car-Hailing Service System, Jianfei Sun, Guowen Xu, Tianwei Zhang, Mamoun Alazab, Robert H. Deng

Research Collection School Of Computing and Information Systems

Aiming for minimizing passengers’ waiting time and vehicles’ vacancy rate, online car-hailing service systems with fog computing has been deployed in various scenarios. In this paper, we focus on addressing the security and privacy issues in such a promising system by customizing a new cryptographic primitive to provide the following security guarantees: (1) private, finegrained and bilateral order matching between passengers and drivers; (2) authenticity verification of passengers’ orders in the form of ciphertext, and (3) temporal assurance of passengers’ ciphertext orders. To the best of our knowledge, no previous system has been designed to meet all three requirements. Existing …

Lightweight And Expressive Fine-Grained Access Control For Healthcare Internet-Of-Things, Shengmin Xu, Yingjiu Li, Robert H. Deng, Yinghui Zhang, Xiangyang Luo, Ximeng Liu

Research Collection School Of Computing and Information Systems

Healthcare Internet-of-Things (IoT) is an emerging paradigm that enables embedded devices to monitor patients vital signals and allows these data to be aggregated and outsourced to the cloud. The cloud enables authorized users to store and share data to enjoy on-demand services. Nevertheless, it also causes many security concerns because of the untrusted network environment, dishonest cloud service providers and resource-limited devices. To preserve patients' privacy, existing solutions usually apply cryptographic tools to offer access controls. However, fine-grained access control among authorized users is still a challenge, especially for lightweight and resource-limited end-devices. In this paper, we propose a novel …

Secure Cloud Data Deduplication With Efficient Re-Encryption, Haoran Yuan, Xiaofeng Chen, Jin Li, Tao Jiang, Jianfeng Wang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Data deduplication technique has been widely adopted by commercial cloud storage providers, which is both important and necessary in coping with the explosive growth of data. To further protect the security of users' sensitive data in the outsourced storage mode, many secure data deduplication schemes have been designed and applied in various scenarios. Among these schemes, secure and efficient re-encryption for encrypted data deduplication attracted the attention of many scholars, and many solutions have been designed to support dynamic ownership management. In this paper, we focus on the re-encryption deduplication storage system and show that the recently designed lightweight rekeying-aware …

Defining Smart Contract Defects On Ethereum, Jiachi Chen, Xin Xia, David Lo, John Grundy, Xiapu Luo, Ting Chen

Research Collection School Of Computing and Information Systems

Smart contracts are programs running on a blockchain. They are immutable to change, and hence can not be patched for bugs once deployed. Thus it is critical to ensure they are bug-free and well-designed before deployment. A Contract defect is an error, flaw or fault in a smart contract that causes it to produce an incorrect or unexpected result, or to behave in unintended ways. The detection of contract defects is a method to avoid potential bugs and improve the design of existing code. Since smart contracts contain numerous distinctive features, such as the gas system. decentralized, it is important …

Privacy Preserving For Multiple Computer Vision Tasks, Amala Varghese Wilson

Master's Projects

Privacy-preserving visual recognition is an important area of research that is gaining momentum in the field of computer vision. In a production environment, it is critical to have neural network models learn continually from user data. However, sharing raw user data with a server is less desirable from a regulatory, security and privacy perspective. Federated learning addresses the problem of privacy- preserving visual recognition. More specifically, we closely examine and dissect a framework known as Dual User Adaptation (DUA) presented by Lange et al. at CVPR 2020, due to its novel idea of bringing about user-adaptation on both the server-side …

Identifying Bots On Twitter With Benford’S Law, Sanmesh Bhosale

Master's Projects

Over time Online Social Networks (OSNs) have grown exponentially in terms of active users and have now become an influential factor in the formation of public opinions. Due to this, the use of bots and botnets for spreading misinformation on OSNs has become a widespread concern. The biggest example of this was during the 2016 American Presidential Elections, where Russian bots on Twitter pumped out fake news to influence the election results.

Identifying bots and botnets on Twitter is not just based on visual analysis and can require complex statistical methods to score a profile based on multiple features and …

Extending The Quality Of Secure Service Model To Multi-Hop Networks, Paul M. Simon, Scott R. Graham

Faculty Publications

Rarely are communications networks point-to-point. In most cases, transceiver relay stations exist between transmitter and receiver end-points. These relay stations, while essential for controlling cost and adding flexibility to network architectures, reduce the overall security of the respective network. In an effort to quantify that reduction, we extend the Quality of Secure Service (QoSS) model to these complex networks, specifically multi-hop networks. In this approach, the quantification of security is based upon probabilities that adversarial listeners and disruptors gain access to or manipulate transmitted data on one or more of these multi-hop channels. Message fragmentation and duplication across available channels …

Analyzing And Detecting Android Malware And Deepfake, Md Shohel Rana

Dissertations

Rapid advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) over the past several decades have produced a variety of technologies and tools that, among numerous cybersecurity issues, have enticed cybercriminals and hackers to design malware for the Android operating systems and/or manipulate multimedia. For example, high-quality and realistic fake videos, images, or audios have been created to spread misinformation and propaganda, foment political discord and hate, or even harass and blackmail people; these manipulated, high-quality and realistic videos became known recently as Deepfake. There has been much work done in recent years on malware analysis and …