Reviewerly/exHarmony huggingface dataset:
CAM-NAS: A Fast Model for Neural Architecture Search based on Class Activation Map

Abstract Artificial intelligence has developed rapidly in recent years. However, with the increasing complexity of AI models and the realistic requirements of AI multi-platform deployment, it is difficult for experts to design a specific network model structure for a specific platform. Neural architecture search (NAS) can be used to help experts discover new network structures. However, traditional NAS algorithms need to consume a lot of computing resources and computing time. In this paper, we propose a class activation map-based neural architecture search method (CAM-NAS) by avoiding the step of training sub-models from traditional NAS algorithms, thus greatly speeding up the search efficiency. On a single NVIDIA RTX3090 graphics card, CAM-NAS takes only 0.08 seconds to evaluate a single sub-model. This is orders of magnitude faster than traditional NAS algorithms. We conducted experiments on the cifar-10 and cifar-100 datasets and achieved 95.68\% and 75.94\% accuracy, respectively. CAM-NAS is the first algorithm to combine the interpretability method with NAS, which greatly inspires further exploration of the inner principles of deep learning models in the future.
Expose Application: A Development of Learning Media For Optimising Young Learners’ English Vocabulary

The need for English language proficiency in this age of globalisation demands a variety of efforts to learn the lingua franca as soon as possible. The use of media is one of the teaching strategies that kindergarten teachers could employ to maximise the interests and desires of the students in English learning. This study aims to describe the development of process design and validation of a proposed learning media called ‘Expose’, which is empirically tested with a limited group. ‘Expose’ is the media that was developed to meet the needs of vocabulary learning at the level of early childhood education. This type of research is R&amp;D (Research and Development) using the ASSURE model. Validation of the content and media expert shows that ‘expose’ is being ‘very good’ for stimulation vocabulary for early childhood. Furthermore, empirical tests on a limited group (20 children aged 5-6 years in Lampah, Gresik) The study revealed that using the Expose application media to teach English vocabulary to kids between the ages of 5 and 6 significantly altered learning outcomes. The results of this research underscores how kindergarten teachers must be especially careful in choosing the appropriate vocabulary and learning technology for young children to learn.
Blockchain Forensics: A Modern Approach to Investigating Cybercrime in the Age of Decentralisation

Blockchain forensics (the use of scientific methods to manipulate data to create useful and informative descriptions of the manipulated data) takes data from the blockchain to interpret the flow of digital assets. Investigators use sophisticated data manipulation and visualization tools to identify the travel history of stolen assets. With these capabilities, chain hopping, round-tripping, and all attempts to blur transfer trails by cyber criminals become smoke screens with no effect. This paper establishes a framework for investigating financial crimes on the blockchain, starting with a brief explanation of blockchain forensics, types of financial crimes committed by criminals using the blockchain, and how they can be mitigated using OSINT’s (OSINT, 2022) investigation process of data collection, data preservation, data processing, and data presentation. This paper also presents a community-based approach to financial crime investigations on the blockchain that involves the public and victims themselves contributing valuable intelligence that can be used to trace and track criminals by authorities and security agencies in the global fight against cybercrime.
Case Study: Conducting a Risk Assessment for an Electrical Utility

Risk management is an important part of effective cybersecurity. This paper presents a hypothetical risk assessment as a case study for one of the largest electricity providers in the southern California region using the approach outlined in the National Institute of Standards and Technology Special Publication 800 series on cybersecurity.
Cybersecurity in Digital Transformation applications: Analysis of Past Research and Future Directions

The term digital is often used to indicate the changes occurring in today’s world, generally referred to as a cyber-physical system, driven by the rapid adoption of digital technologies, where the cyber and the physical worlds are partly overlapping. Digital transformation refers to the integration of the digital technology of the cyber world into all physical domains. These cyber-physical systems must be secure against the threat of cyberattacks. However, one of the most challenging aspects of cybersecurity is the evolving nature of cyberattack risks, which is highly integrated with digital transformation. Despite a number of published articles, there is little investigation of past literature analysis that presents digital transformation applications and cybersecurity trends. The objective of this paper is to provide an intensive examination of digital transformation applications and cybersecurity research between 2019 and 2022, to detect the most profound research areas, emphasize existing challenges and identify patterns, tendencies or regularities existing in the literature in terms of technological applications. This aims to support scholars with a comprehensive understanding of the past, present and future directions of this research trend. To achieve these objectives, a systematic literature review is utilized. The findings introduce several implications for the present state of the literature, apparent study gaps and several research questions, which can be explored in future research.
Nuclear Cyber Attacks: A Study of Sabotage and Regulation of Critical Infrastructure

As of 2021, the World Nuclear Association reports 440 Nuclear reactors are in operation worldwide in 30 countries generating capacity of 390 (GW) which is equivalent to about 10% of the world’s electricity. After Hydroelectric power, nuclear is the world's second largest source of low-carbon power. Important new nuclear technologies including the Small Modular Reactors (SMRs) are being developed globally creating more efficient and safer reactors that can be reproduced off site. &#x0D; While governments redoubled their commitments to reducing greenhouse gas emissions at the UN Climate Change Conference (COP26) in Glasgow, the recovery of economies following the harsh impacts of COVID-19 led to a surge in energy demand that surpassed the growth in production from clean sources including nuclear. The safety and security of nuclear power has received renewed attention since the Russian invasion of Ukraine presenting growing concern about the potential threat of increased malevolent cyber activity against Ukraine’s critical infrastructure. Moreover, there have been more than 20 known cyber incidents worldwide at nuclear facilities since 1990. &#x0D; To address these concerns this paper focuses on the progress of cyber security and cyber resilience in the nuclear industry globally. The 2015 cyber-attack on the Ukrainian Kyivoblenergo, a regional electricity distribution company was analyzed by multiple sources including private companies, investigators in Ukraine, and the U.S. government. The analysis revealed many opportunities to stop or prevent this attack, however, the nuclear industry continues to face serious challenges in protecting against cyber threats. This research will investigate through a comparative analysis the recent government regulations, rules and standards, for nuclear cyber security safety in the United States and internationally to determine whether these laws adequately protect energy infrastructure from cyberattacks and hold responsible parties accountable. Recent initiatives by government and the private sector to enhance the opportunities for improving cyber security in the nuclear sector will be reviewed to determine best practices for improving nuclear safety and cyber resilience.
Search and CompAre Reverse (SCAR): A Bioinformatics–Inspired Methodology for Detecting File Remnants in Digital Forensics

A storage device may contain data that an individual is legally or morally not allowed to possess. Or, a disgruntled company employee may intentionally destroy corporate files, assuming once deleted the information is lost forever. The data could take the form of a database owned by a competitor, illegal images, or videos, or trade secrets or confidential business information. Fragments of the data may very well still be present on the disk drive, for example, and forensic tools may be capable of recovering some of the confidential information. This paper introduces Search and CompAre Reverse (SCAR), inspired from tools used in the bioinformatics community. The contribution is an initial empirical investigation into the use of this bioinformatics-inspired approach to deduce the partial existence of patterns in cases where traditional digital forensics tools cannot detect the type of the file due to overwriting the file signature portion of the file.
Evaluating a Non-platform-specific OCR/NLP system to detect Online Grooming

Online Grooming is a social engineering attack in which the attacking party uses deceptive practices for sexual gratification. The targets of these attacks can vary in demographics however in most cases the target is children, with most of these attacks occurring on social media platforms. As well as the illegality of these attacks in the UK and US, children who experience these attacks are at a higher risk of self-harm or having suicidal thoughts. Due to the deployment of new social media platforms/features any implementation that is made specific to a certain feature/platform is likely to be outdated/ineffective upon release, due to the volatility of the methods/tactics used. Therefore a non-platform specific implementation has been considered within this investigation. From a preliminary analysis, it was concluded that there was an average true positive detection rate of 71% from using optical recognition and natural language processing across three different social media platforms. It is suggested that implementing this text extraction and processing method alongside a 'category-based' machine learning algorithm, a solution that can identify online grooming can be developed that considers the 'real world complexities' of this attack.
An Analysis of Crypto Scams during the Covid-19 Pandemic: 2020-2022

Blockchain and cryptocurrency adoption has increased significantly since the start of the Covid-19 pandemic. This adoption rate has overtaken the Internet adoption rate in the 90s and early 2000s, but as a result, the instances of crypto scams have also increased. The types of crypto scams reported are typically giveaway scams, rug pulls, phishing scams, impersonation scams, Ponzi schemes as well as pump and dumps. The US Federal Trade Commission (FTC) reported that in May 2021 the number of crypto scams were twelve times higher than in 2020, and the total loss increased by almost 1000%. The FTC also reported that Americans have lost more than $80 million due to cryptocurrency investment scams from October 2019 to October 2020, with victims between the ages of 20 and 39 represented 44% of the reported cases. Social Media has become the go-to place for scammers where attackers hack pre-existing profiles and ask targets’ contacts for payments in cryptocurrency. In 2020, both Joe Biden and Bill Gates’ Twitter accounts were hacked where the hacker posted tweets promising that for all payments sent to a specified address, double the amount will be returned, and this case of fraud was responsible for $100,000 in losses. A similar scheme using Elon Musk’s Twitter account resulted in losses of nearly $2 million. This paper analyses the most significant blockchain and cryptocurrency scams since the start of the Covid-19 pandemic, with the aim of raising awareness and contributing to protection against attacks. Even though the blockchain is a revolutionary technology with numerous benefits, it also poses an international crisis that cannot be ignored.
Ret-gadgets in RISC-V-based Binaries Resulting in Traps for Hijackers

The presence of instructions within executable programs is what makes the binaries executable. However, attackers leverage on the same to achieve some form of Control Flow Hijacking (CFH). Such code re-use attacks have also been found to lead to Denial of Service (DoS). An example of code re-use attack is Return Oriented Programming (ROP) which is caused by passing input crafted as chained sequences of instructions that are already existing as subroutines in the target program. The instructions are called gadgets and they would normally end with ret. The ret instructions enable the flow of hijacked execution from one set of instruction to another within the attacker’s control. There could however be exceptions depending on the structure of the chained gadgets where the chained gadget fails to run its course due to inability of specific gadgets to replace the value in the return address (ra) register. The dangers of chained gadgets are not a new idea but the possibility for an attacker’s gadget chain to fall into a trap during a ROP attack is not commonly addressed. In addition to this, recent studies have revealed that understanding the behaviours of gadgets would be useful for building information base in training machine learning (ML) models to combat ROP. This study explains the behaviour of certain ROP gadgets showing the possibility of occurrence of a loop in execution during exploitation. A sample program which accesses gadgets from the GNU C library (glibc) is used to demonstrate the findings. Gadgets identified with this possibility are poor for chaining as they do not contain instructions to load or move new values to the ra register and would produce unreliable exploits. This would result in a trap for the chained gadgets instead of arbitrary code execution, and DoS on the path of the user. This implies that the impact that a ROP chain could have on a targeted process does not only rely on the underlying system architecture but also on relies on the structure of the chained gadget. In this paper, the RISC-V architecture is the focus, new gadget finders (scripts are available) are presented, and sample of chained gadgets are analysed on a RISC-V -based binary.
Anti-American Stance in Turkey: A Twitter Case Study

The availability of social media and biased actors exacerbated Anti-American and Anti-Western views to extremes. In this paper, we report our efforts in analyzing anti-American views on Twitter. We have collected over three years of Turkish tweets related to the US, translated them into English, and analyzed these tweets using various computational social science tools. We found that Turkish tweets related to the US are significantly negative, and emotions reflect disgust and anger. Furthermore, we found that the source of the negative views stems from political actors like Trump or Biden rather than general hatred. Our results shed light on potential policy plans and interventions.
Development and Analysis of a Reconnaissance-Technique Knowledge Graph

Penetration testing involves the use of many tools and techniques. The first stage of penetration testing involves conducting reconnaissance on a target organization. In the reconnaissance phase, adversaries use tools to find network data, people data, company/organization data, and attack data to generate a risk assessment about a target to determine where initial weaknesses may be. Although a small number of tools can be used to conduct many of reconnaissance tasks, including Shodan, Nmap, Recon-ng, Maltego, Metasploit, Google and more, each tool holds an abundance of specific techniques that can be used. Furthermore, each technique uses unique syntax. For example, Nmap holds over 600 scripts that make up its Nmap Scripting Engine. Depending on the type of device targeted, Nmap scripts can scan for ports, operating systems, IP addresses, hostnames and more. As another example, Maltego operates over 150 transforms or modules that collect data on organizations, files and people. Understanding which reconnaissance tool, techniques within those tools, and the syntax for each technique represents a highly complex task. MITRE ATT&amp;CK, a widely accepted framework, models tactics and techniques within the tactics to help users make sense of adversarial behaviours. The tactic of reconnaissance is modelled in ATT&amp;CK as well as its techniques. However, the explicit links between reconnaissance techniques are not modelled. Our research focused on the development of an ontology called Recontology to model the domain of reconnaissance. Recontology was then used to form Reconnaissance-Technique Graph (RT-Graph) to model 102 reconnaissance techniques and the directional links between the techniques. We used exploratory data analysis (EDA) methods including a graph spatial-layout algorithm and several graph-statistical algorithms to examine RT-Graph. We also used EDA to find critical techniques within the graph. Patterns across the results are discussed as well as implications for real-world uses of RT-Graph.
Digital Insanity: Exploring the Flexibility of NIST Digital Identity Assurance Levels

NIST Special Publication 800-63-3 presents a new risk management concept on digital identity. It includes various harm categories to determine an appropriate assurance level for identity proofing, authentication, and federation. These three distinct approaches are highlighted to give flexibility in protecting systems. This paper explores if this is a realized flexibility by developing a tool to test assurance level and component flexibility. It also identifies appropriate MFA levels given different levels of risks and makes three recommendations to help improve the adoption of the NIST digital identity guidelines.
Social Media Manipulation Awareness through Deep Learning based Disinformation Generation

As a digital environment introduced for establishing and enhancing human communication through different social networks and channels, social media continued to develop and spread at an incredible rate making it difficult to find or imagine a concept, technology, or business that does not have or plan to have its social media representation and space. Concurrently, social media became a playground and even a battlefield where different ideas carrying out diverse validity degrees are spread for reaching their target audiences generated by clear and trustable well-known, uncertain, or even evil aimed entities. In the stride carried out for preventing, containing, and limiting the effects of social manipulation of the last two types of entities, proper/effective security awareness is critical and mandatory in the first place. On this behalf, several strategies, policies, methods, and technologies were proposed by research and practitioner communities, but such initiatives take mostly a defender perspective, and this is not enough in cyberspace where the offender is in advantage in attack. Therefore, this research aims to produce social media manipulation security awareness taking the offender stance by generating and analysing disinformation tweets using deep learning. To reach this goal, a Design Science Research methodology is followed in a Data Science approach, and the results obtained are analysed and positioned in the ongoing discourses showing the effectiveness of such approach and its role in building future social media manipulation detection solutions. This research also intends to contribute to the design of further transparent and responsible modelling and gaming solutions for building/enhancing social manipulation awareness and the definition of realistic cyber/information operations scenarios dedicated/engaging large multi-domain (non)expert audiences.
Locality-based electromagnetic leakage assessment using CNN

Deep learning side-channel analysis (SCA) attacks have recently gained in popularity. The ability of deep learning models to retrieve a key byte while minimizing the need for pre-processing steps in both cases of misaligned traces and when the leakage model is multivariate has contributed to the popularity gain. Near-field electromagnetic (EM) probes have enabled leakage capture with high spatial resolution, and the field of deep learning side-channel research looks to find models which reduce the required number of leakage traces to retrieve a key byte successfully. However, despite the many papers researching techniques to reduce the number of traces required for a successful attack, location-based research for EM SCA remains untouched. Due to the nature of EM probes and the architecture of different boards and chips, the location of the collection probe becomes important when attempting to extract the secret key within a reasonable timeframe and with a level of certainty in the result. Our contribution is a framework to determine the best location to assess localized EM leakage against a given chip platform. We use a raster scan to collect localized leakage results at 25 points in a 5x5 grid pattern on a ChipWhisperer Lite XMEGA board running an open-source implementation of the AES-128 encryption algorithm. We demonstrate the use of our framework to locate the best points for an attacker to execute a profiling attack by identifying the grid point with the most detectable leakage for a chip platform. We further execute a smaller localized 25-point raster scan in a grid over the identified location to further refine our estimate of the optimal collection location. We then demonstrate that this location can be used to execute a side-channel profiling attack against the same chip architecture and will result in a lower number of traces required to retrieve the key byte.
Secure Cloud Migration Strategy (SCMS): A Safe Journey to the Cloud

The state of cloud security is evolving. Many organizations are migrating their on-premises data centers to cloud networks at a rapid pace due to the benefits like cost-effectiveness, scalability, reliability, and flexibility. Yet, cloud environments also raise certain security concerns that may hinder their adoption. Cloud security threats may include data breaches/leaks, data loss, access management, insecure APIs, and misconfigured cloud storage. The security challenges associated with cloud computing have been widely studied in previous literature and different research groups. This paper conducted a systematic literature review and examined the research studies published between 2010 and 2023 within popular digital libraries. The paper then proposes a comprehensive Secure Cloud Migration Strategy (SCMS) that organizations can adopt to secure their cloud environment. The proposed SCMS consists of three main repeatable phases/processes, which are preparation; readiness and adoption; and testing. Among these phases, the author addresses tasks/projects from the different perspectives of the three cybersecurity teams, which are the blue team (defenders), the red team (attackers), and the yellow team (developers). This can be used by the Cloud Center of Excellence (CCoE) as a checklist that covers defending the cloud; attacking and abusing the cloud; and applying the security shift left concepts. In addition to that, the paper addresses the necessary cloud security documents/runbooks that should be developed and automated such as incident response runbook, disaster recovery planning, risk assessment methodology, and cloud security controls. Future research venues and open cloud security problems/issues were addressed throughout the paper. The ultimate goal is to support the development of a proper security system to an efficient cloud computing system to help harden organizations’ cloud infrastructures and increase the cloud security awareness level, which is significant to national security. Furthermore, practitioners and researchers can use the proposed solutions to replicate and/or extend the proposed work.