Malware Resistant Data Protection in Hyper-connected Networks: A survey

Data protection is the process of securing sensitive information from being corrupted, compromised, or lost. A hyperconnected network, on the other hand, is a computer networking trend in which communication occurs over a network. However, what about malware. Malware is malicious software meant to penetrate private data, threaten a computer system, or gain unauthorised network access without the users consent. Due to the increasing applications of computers and dependency on electronically saved private data, malware attacks on sensitive information have become a dangerous issue for individuals and organizations across the world. Hence, malware defense is critical for keeping our computer systems and data protected. Many recent survey articles have focused on either malware detection systems or single attacking strategies variously. To the best of our knowledge, no survey paper demonstrates malware attack patterns and defense strategies combinedly. Through this survey, this paper aims to address this issue by merging diverse malicious attack patterns and machine learning (ML) based detection models for modern and sophisticated malware. In doing so, we focus on the taxonomy of malware attack patterns based on four fundamental dimensions the primary goal of the attack, method of attack, targeted exposure and execution process, and types of malware that perform each attack. Detailed information on malware analysis approaches is also investigated. In addition, existing malware detection techniques employing feature extraction and ML algorithms are discussed extensively. Finally, it discusses research difficulties and unsolved problems, including future research directions.Comment: 30 pages, 9 figures, 7 tables, no where submitted ye

Techniques for the reverse engineering of banking malware

Malware attacks are a significant and frequently reported problem, adversely affecting the productivity of organisations and governments worldwide. The well-documented consequences of malware attacks include financial loss, data loss, reputation damage, infrastructure damage, theft of intellectual property, compromise of commercial negotiations, and national security risks. Mitiga-tion activities involve a significant amount of manual analysis. Therefore, there is a need for automated techniques for malware analysis to identify malicious behaviours. Research into automated techniques for malware analysis covers a wide range of activities. This thesis consists of a series of studies: an anal-ysis of banking malware families and their common behaviours, an emulated command and control environment for dynamic malware analysis, a technique to identify similar malware functions, and a technique for the detection of ransomware. An analysis of the nature of banking malware, its major malware families, behaviours, variants, and inter-relationships are provided in this thesis. In doing this, this research takes a broad view of malware analysis, starting with the implementation of the malicious behaviours through to detailed analysis using machine learning. The broad approach taken in this thesis differs from some other studies that approach malware research in a more abstract sense. A disadvantage of approaching malware research without domain knowledge, is that important methodology questions may not be considered. Large datasets of historical malware samples are available for countermea-sures research. However, due to the age of these samples, the original malware infrastructure is no longer available, often restricting malware operations to initialisation functions only. To address this absence, an emulated command and control environment is provided. This emulated environment provides full control of the malware, enabling the capabilities of the original in-the-wild operation, while enabling feature extraction for research purposes. A major focus of this thesis has been the development of a machine learn-ing function similarity method with a novel feature encoding that increases feature strength. This research develops techniques to demonstrate that the machine learning model trained on similarity features from one program can find similar functions in another, unrelated program. This finding can lead to the development of generic similar function classifiers that can be packaged and distributed in reverse engineering tools such as IDA Pro and Ghidra. Further, this research examines the use of API call features for the identi-fication of ransomware and shows that a failure to consider malware analysis domain knowledge can lead to weaknesses in experimental design. In this case, we show that existing research has difficulty in discriminating between ransomware and benign cryptographic software. This thesis by publication, has developed techniques to advance the disci-pline of malware reverse engineering, in order to minimize harm due to cyber-attacks on critical infrastructure, government institutions, and industry.Doctor of Philosoph

Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

Infrastructure as Code for Cybersecurity Training

An organization\u27s infrastructure rests upon the premise that cybersecurity professionals have specific knowledge in administrating and protecting it against outside threats. Without this expertise, sensitive information could be leaked to malicious actors and cause damage to critical systems. These attacks tend to become increasingly specialized, meaning cybersecurity professionals must ensure proficiency in specific areas. Naturally, recommendations include creating advanced practical training scenarios considering realistic situations to help trainees gain detailed knowledge. However, the caveats of high-cost infrastructure and difficulties in the deployment process of this kind of system, primarily due to the manual process of pre-configuring software needed for the training and relying on a set of static Virtual Machines, may take much work to circumvent. In order to facilitate this process, our work addresses the use of Infrastructure as Code (IaC) and DevOps to automate the deployment of cyber ranges. An approach closely related to virtualization and containerization as the code\u27s underlying infrastructure helps lay down this burden. Notably, placing emphasis on using IaC tools like Ansible eases the process of configuration management and provisioning of a network. Therefore, we start by focusing on understanding what the State of the Art perspectives lack and showcasing the benefits of this new working outlook. Lastly, we explore several up-to-date vulnerabilities that are constantly messing with the lives of individuals and organizations, most related to Privilege Escalation, Remote Code Execution attacks, and Incident Forensics, allowing the improvement of skills concerning Red team and Blue team scenarios. The analysis of the attacks and exploitation of such vulnerabilities are carried out safely due to a sandbox environment. The expected results revolve around using IaC to deploy a set of purposely-designed cyber ranges with specific challenges. The main objective is to guarantee a complexity of scenarios similar to what we can observe in enterprise-level networks. Thus, this entails having a set of playbooks that can be run in a machine or laboratory, assuring the final state of the network is consistent. We expect this deployment strategy to be cost-effective, allowing the trainee to get deep insight into a wide range of situations. Nowadays, DevOps solutions work as a silver bullet against the issues derived from old-case-driven approaches for setting up scenarios. In short, one of the key takeaways of this work is contributing to better prepare specialists in ensuring that the principles of the National Institute of Standards and Technology (NIST) Cybersecurity Framework hold, namely: prevent, detect, mitigate, and recover

Studying a Virtual Testbed for Unverified Data

It is difficult to fully know the effects a piece of software will have on your computer, particularly when the software is distributed by an unknown source. The research in this paper focuses on malware detection, virtualization, and sandbox/honeypot techniques with the goal of improving the security of installing useful, but unverifiable, software. With a combination of these techniques, it should be possible to install software in an environment where it cannot harm a machine, but can be tested to determine its safety. Testing for malware, performance, network connectivity, memory usage, and interoperability can be accomplished without allowing the program to access the base operating system of a machine. After the full effects of the software are understood and it is determined to be safe, it could then be run from, and given access to, the base operating system. This thesis investigates the feasibility of creating a system to verify the security of unknown software while ensuring it will have no negative impact on the host machine

Secure portable execution and storage environments: A capability to improve security for remote working

Remote working is a practice that provides economic benefits to both the employing organisation and the individual. However, evidence suggests that organisations implementing remote working have limited appreciation of the security risks, particularly those impacting upon the confidentiality and integrity of information and also on the integrity and availability of the remote worker’s computing environment. Other research suggests that an organisation that does appreciate these risks may veto remote working, resulting in a loss of economic benefits. With the implementation of high speed broadband, remote working is forecast to grow and therefore it is appropriate that improved approaches to managing security risks are researched. This research explores the use of secure portable execution and storage environments (secure PESEs) to improve information security for the remote work categories of telework, and mobile and deployed working. This thesis with publication makes an original contribution to improving remote work information security through the development of a body of knowledge (consisting of design models and design instantiations) and the assertion of a nascent design theory. The research was conducted using design science research (DSR), a paradigm where the research philosophies are grounded in design and construction. Following an assessment of both the remote work information security issues and threats, and preparation of a set of functional requirements, a secure PESE concept was defined. The concept is represented by a set of attributes that encompass the security properties of preserving the confidentiality, integrity and availability of the computing environment and data. A computing environment that conforms to the concept is considered to be a secure PESE, the implementation of which consists of a highly portable device utilising secure storage and an up-loadable (on to a PC) secure execution environment. The secure storage and execution environment combine to address the information security risks in the remote work location. A research gap was identified as no existing ‘secure PESE like’ device fully conformed to the concept, enabling a research problem and objectives to be defined. Novel secure storage and execution environments were developed and used to construct a secure PESE suitable for commercial remote work and a high assurance secure PESE suitable for security critical remote work. The commercial secure PESE was trialled with an existing telework team looking to improve security and the high assurance secure PESE was trialled within an organisation that had previously vetoed remote working due to the sensitivity of the data it processed. An evaluation of the research findings found that the objectives had been satisfied. Using DSR evaluation frameworks it was determined that the body of knowledge had improved an area of study with sufficient evidence generated to assert a nascent design theory for secure PESEs. The thesis highlights the limitations of the research while opportunities for future work are also identified. This thesis presents ten published papers coupled with additional doctoral research (that was not published) which postulates the research argument that ‘secure PESEs can be used to manage information security risks within the remote work environment’

CoFHEE: A Co-processor for Fully Homomorphic Encryption Execution

The migration of computation to the cloud has raised privacy concerns as sensitive data becomes vulnerable to attacks since they need to be decrypted for processing. Fully Homomorphic Encryption (FHE) mitigates this issue as it enables meaningful computations to be performed directly on encrypted data. Nevertheless, FHE is orders of magnitude slower than unencrypted computation, which hinders its practicality and adoption. Therefore, improving FHE performance is essential for its real world deployment. In this paper, we present a year-long effort to design, implement, fabricate, and post-silicon validate a hardware accelerator for Fully Homomorphic Encryption dubbed CoFHEE. With a design area of $12mm^2$, CoFHEE aims to improve performance of ciphertext multiplications, the most demanding arithmetic FHE operation, by accelerating several primitive operations on polynomials, such as polynomial additions and subtractions, Hadamard product, and Number Theoretic Transform. CoFHEE supports polynomial degrees of up to $n = 2^{14}$ with a maximum coefficient sizes of 128 bits, while it is capable of performing ciphertext multiplications entirely on chip for $n \leq 2^{13}$. CoFHEE is fabricated in 55nm CMOS technology and achieves 250 MHz with our custom-built low-power digital PLL design. In addition, our chip includes two communication interfaces to the host machine: UART and SPI. This manuscript presents all steps and design techniques in the ASIC development process, ranging from RTL design to fabrication and validation. We evaluate our chip with performance and power experiments and compare it against state-of-the-art software implementations and other ASIC designs. Developed RTL files are available in an open-source repository

Zero Trust Architecture: Framework and Case Study

The world and business are connected and a business does not exist today that does not have potentially thousands of connections to the Internet in addition to the thousands of connections to other various parts of its own infrastructure. That is the nature of the digital world we live in and there is no chance the number of those interconnections will reduce in the future. Protecting from the “outside” world with a perimeter solution might have been enough to reduce risk to an acceptable level in an organization 20 years ago, but today’s threats are sophisticated, persistent, abundant, and can come from pretty much anywhere: a hostile nation-state, a hacker in their parents’ basement, a disgruntled system admin, an unsuspecting accountant victim, and countless others. The methodologies and principles of perimeter-based protections of yesteryear must be constricted down to the resource level and constantly evaluated for risk in order to be effective today and into the future…and that constriction comes in the form of zero trust. We must no longer solely focus on protecting the perimeter of the network but take that same mentality and make that perimeter protection applicable to every user, resource, service, and asset within operation in our enterprise and our cloud services