Information Technology (IT) is recognized as an independent and unique research field. However, there has been ambiguity and difficulty in identifying and differentiating IT research from other close variations. Given this context, this paper aimed to explore the roots of the Information Technology (IT) research domain by conducting a large-scale text mining analysis of 50,780 abstracts from awarded NSF CISE grants from 1985 to 2024. We categorized the awards based on their program content, labeling human-centric programs as IT research programs and infrastructure-centric programs as other research programs based on the IT definitions in the literature. This novel approach helped us identify the core concepts of IT research and compare the similarities and differences between IT research and other research areas. The results showed that IT differentiates itself from other close variations by focusing more on the needs of users, organizations, and societies.

With the emergence of remote code execution (RCE) vulnerabilities in ubiquitous libraries and advanced social engineering techniques, threat actors have started conducting widespread fileless cryptojacking attacks. These attacks have become effective with stealthy techniques based on PowerShell-based exploitation in Windows OS environments. Even if attacks are detected and malicious scripts removed, processes may remain operational on victim endpoints, creating a significant challenge for detection mechanisms. In this paper, we conducted an experimental study with a collected dataset on detecting PowerShell-based fileless cryptojacking scripts. The results showed that Abstract Syntax Tree (AST)-based fine-tuned CodeBERT achieved a high recall rate, proving the importance of the use of AST integration and fine-tuned pre-trained models for programming language.

Fileless malware predominantly relies on PowerShell scripts, leveraging the native capabilities of Windows systems to execute stealthy attacks that leave no traces on the victim's system. The effectiveness of the fileless method lies in its ability to remain operational on victim endpoints through memory execution, even if the attacks are detected, and the original malicious scripts are removed. Threat actors have increasingly utilized this technique, particularly since 2017, to conduct cryptojacking attacks. With the emergence of new Remote Code Execution (RCE) vulnerabilities in ubiquitous libraries, widespread cryptocurrency mining attacks have become prevalent, often employing fileless techniques. This paper provides a comprehensive analysis of PowerShell scripts of fileless cryptojacking, dissecting the common malicious patterns based on the MITRE ATT&CK framework.

Information flow tracking was proposed more than 40 years ago to address the limitations of access control mechanisms to guarantee the confidentiality and integrity of information flowing within a system, but has not yet been widely applied in practice for security solutions. Here, we survey and systematize literature on dynamic information flow tracking (DIFT) to discover challenges and opportunities to make it practical and effective for security solutions. We focus on common knowledge in the literature and lingering research gaps from two dimensions— (i) the layer of abstraction where DIFT is implemented (software, software/hardware, or hardware) and (ii) the security goal (confidentiality and/or integrity). We observe that two major limitations hinder the practical application of DIFT for on-the-fly security applications: (i) high implementation overhead and (ii) incomplete information flow tracking (low accuracy). We posit, after review of the literature, that addressing these major impedances via hardware parallelism can potentially unleash DIFT’s great potential for systems security, as it can allow security policies to be implemented in a built-in and standardized fashion. Furthermore, we provide recommendations for the next generation of practical and efficient DIFT systems with an eye towards hardware-supported implementations.

Drug overdose is a pressing public health concern in the United States, resulting in a significant number of fatalities each year. In this study, we employ the Journey to Crime (JTC) framework borrowed from the field of environmental criminology to examine the association between drug sales locations and overdose death locations. In this research, our objective is to elucidate the trajectory of overdose victims to overdose locations, aiming to enhance the distribution of overdose services and interventions. To the best of our knowledge, no previous studies have applied the JTC framework to investigate drug overdose deaths. By scrutinizing data obtained from the Hamilton County, OH Coroners, and the Cincinnati Police Department, we endeavor to explore the plausible correlation between overdose deaths and drug sales locations. Our findings underscore the necessity of implementing a comprehensive strategy to curtail overdose deaths. This strategy should encompass various facets, including targeted efforts to reduce the accessibility of illicit drugs, the enhancement of responses to overdose incidents through a collaborative multidisciplinary approach, and the availability of data to inform evidence-based strategies and facilitate outcome evaluation. By shedding light on the relationship between drug sales locations and overdose death locations through the utilization of the JTC framework, this study contributes valuable insights to the field of drug overdose prevention. It emphasizes the significance of adopting multifaceted approaches to address this public health crisis effectively. Ultimately, our research aims to inform the development of evidence-based interventions and policies that can mitigate the occurrence and impact of drug overdoses in our communities.

Fileless malware and cryptojacking attacks have appeared independently as the new alarming threats in 2017. After 2020, fileless attacks have been devastating for victim organizations with low-observable characteristics. Also, the amount of unauthorized cryptocurrency mining has increased after 2019. Adversaries have started to merge these two different cyberattacks to gain more invisibility and profit under "Fileless Cryptojacking." This paper aims to provide a literature review in academic papers and industry reports for this new threat. Additionally, we present a new threat hunting-oriented DFIR approach with the best practices derived from field experience as well as the literature. Last, this paper reviews the fundamentals of the fileless threat that can also help ransomware researchers examine similar patterns.

The rapid progress experienced in the Internet of Things (IoT) space is one that has introduced new and unique challenges for cybersecurity and IoT-Forensics. One of these problems is how digital forensics and incident response (DFIR) are handled in IoT. Since enormous users use IoT platforms to accomplish their day to day task, massive amounts of data streams are transferred with limited hardware resources; conducting DFIR needs a new approach to mitigate digital evidence and incident response challenges owing to the facts that there are no unified standard or classified principles for IoT forensics. Today's IoT DFIR relies on self-defined best practices and experiences. Given these challenges, IoT-related incidents need a more structured approach in identifying problems of DFIR. In this paper, we examined the major DFIR challenges in IoT by exploring the different phases involved in a DFIR when responding to IoT-related incidents. This study aims to provide researchers and practitioners a road-map that will help improve the standards of IoT security and DFIR.

In recent years, widespread internet usage, increasing numbers of IoT devices, and vast data on social media have increased cyber attack vectors. Parallel to this trend, the demand for the cyber security workforce has augmented; however, employers could not fill these positions due to the shortage in the field. This anomaly, in turn, has placed companies in a vulnerable situation by being exposed to an increased level of cyber security threats/incidents. Relevant authorities underscore the importance of a comprehensive cybersecurity workforce framework to overcome this problem. Given this context, this study offers a CyberSec Labs framework to improve the skills of potential cybersecurity actors. The framework extensively benefits from hands-on exercises like cloud computing and networking, OSINT, data-driven cybersecurity approach, vulnerability and penetration test analysis, and volatility analysis. The overall goal of this open-source material is to prepare students for their future cybersecurity roles, which contributes to overcoming the shortage of skilled human capital in the cybersecurity field.

This paper presents a framework for the academic discipline of information technology. The framework defines four independent elements to the discipline: People, Information, Technology, and the Solutions or Needs that connect them. In IT practice, the information technology discipline aim is the selection, creation, integration, application, security, and administration of solutions that use technology to empower people through information. In IT research, the discipline aim is the investigation, discovery, and dissemination of needs that connect people, information, and technology. The proposed framework presents a theory for the discipline of information technology to inform its research and practice. It builds on past efforts to define the information technology discipline and provides a foundation for academic institutions and the industry to proactively address the need for talent and innovation to sustain the economic engine of our society.

Cryptojacking is the exploitation of victims' computer resources to mine for cryptocurrency using malicious scripts. It had become popular after 2017 when attackers started to exploit legal mining scripts, especially Coinhive scripts. Coinhive was actually a legal mining service that provided scripts and servers for in-browser mining activities. Nevertheless, over 10 million web users had been victims every month before the Coinhive shutdown that happened in Mar 2019. This paper explores the new era of the cryptojacking world after Coinhive discontinued its service. We aimed to see whether and how attackers continue cryptojacking, generate new malicious scripts, and developed new methods. We used a capable cryptojacking detector named CMTracker that proposed by Hong et al. in 2018. We automatically and manually examined 2770 websites that had been detected by CMTracker before the Coinhive shutdown. The results revealed that 99% of sites no longer continue cryptojacking. 1% of websites still run 8 unique mining scripts. By tracking these mining scripts, we detected 632 unique cryptojacking websites. Moreover, open-source investigations (OSINT) demonstrated that attackers still use the same methods. Therefore, we listed the typical patterns of cryptojacking. We concluded that cryptojacking is not dead after the Coinhive shutdown. It is still alive, but not as attractive as it used to be.

Today, maps are important components of public safety. Collecting geographic information data enables any public safety agency to smartly allocate their limited resources to those places where the public safety needs are the greatest. However, the current address geocoding applications are generally static, requiring specifically assigned personnel to geocode the addresses using a geographic information system (GIS) software. Real-time geocoding services like Google Maps are an alternative to these static systems by offering real-time geocoding capabilities; however, these closed systems usually come with a price that many agencies cannot afford. Given this context, we developed a public safety geocoding system using available open-source data and systems to enable any public safety agency to continuously enjoy the power of geographic information systems for their smart decision-making process. The proposed model is not limited to public safety and can be used for any purpose where people are used to using Google Maps Geocoding API or other commercial geocoding services.

The present study proposes a novel automated model, called Vulnerability Index for Population at Risk (VIPAR) scores, to identify rare populations for their future shooting victimizations. Likewise, the focused deterrence approach identifies vulnerable individuals and offers certain treatments (e.g., outreach services) to prevent violence in communities. Our rule-based engine model is the first AI-based model for victim prediction purposes. The model merit is the usage of criminology studies to construct the rule-based engine to predict victims. This paper aims to compare the list of focused deterrence strategy with the VIPAR score list regarding their predictive power for the future shooting victimizations. Drawing on the criminological studies, this study uses age, past criminal history, and peer influence as the main predictors of future violence. Network graph analysis is employed to measure the influence of peers on the outcome variable. The proposed model also uses logistic regression analysis to verify the variable selections in the model. Following the analytical process, the current research creates an automated model (VIPAR scores) to predict vulnerable populations for their future shooting involvements. Our empirical results show that VIPAR scores predict 25.8% of future shooting victims and 32.2% of future shooting suspects, whereas the focused deterrence list predicts 13% of future shooting victims and 9.4% of future shooting suspects. The proposed model outperforms the intelligence list of focused deterrence policies in predicting the future fatal and non-fatal shootings. Furthermore, this paper discusses the concerns about the presumption of innocence right.

Previous studies have offered a variety of explanations on the relationship between democracy and the internet. However, most of these studies concentrate on regions, specific states or authoritarian regimes. No study has investigated the influence of the internet in partly free countries defined by the Freedom House. Moreover, very little is known about the effects of online censorship on the development, stagnation, or decline of democracy. Drawing upon the International Telecommunication Union, Freedom House, and World Bank databases and using machine learning methods, this study sheds new light on the effects of the internet on democratization in partly free countries. The findings suggest that internet penetration and online censorship both have a negative impact on democracy scores and the internet's effect on democracy scores is conditioned by online censorship. Moreover, results from random forest suggest that online censorship is the most important variable followed by governance index and education on democracy scores. The comparison of the various machine learning models reveals that the best predicting model is the 175-tree random forest model which has 92% accuracy. Also, this study might help "IT professionals" to see their important role not only in the technical fields but also in society in terms of democratization and how close IT is to social sciences.

Many organizations migrate their on-premise infrastructure to the cloud for its known advantages such as low cost and flexibility. However, a dilemma appears that despite the increased usage of cloud computing among small and midsize businesses, the reported number of cyber attacks does not reduce and even astronomically increases. Recent studies explain the source of this dilemma as having scarce familiarity with cloud computing. Scholars basically stress that cloud computing offers better malware and DDoS attack protection; however, limited knowledge of cloud computing infrastructure and the complex nature of cloud systems contribute to the high number of cyber attacks. In this context, this study attempts to add one more step to the traditional incident response plan to adapt it to cloud computing. In addition, the current study introduces certain solutions to gather dispersed data from complex cloud computing infrastructure which past studies cited as the main challenges of effective cloud incident response.

The availability of sophisticated technologies and methods of perpetrating criminogenic activities in the cyberspace is a pertinent societal problem. Darknet is an encrypted network technology that uses the internet infrastructure and can only be accessed using special network configuration and software tools to access its contents which are not indexed by search engines. Over the years darknets traditionally are used for criminogenic activities and famously acclaimed to promote cybercrime, procurements of illegal drugs, arms deals, and cryptocurrency markets. In countries with oppressive regimes, censorship of digital communications, and strict policies prompted journalists and freedom fighters to seek freedom using darknet technologies anonymously while others simply exploit it for illegal activities. Recently, MIT's Lincoln Laboratory of Artificial Intelligence augmented a tool that can be used to expose illegal activities behind the darknet. We studied relevant literature reviews to help researchers to better understand the darknet technologies, identify future areas of research on the darknet and ultimately to optimize how data-driven insights can be utilized to support governmental agencies in unraveling the depths of darkent technologies. This paper focuses on the use of internet for crimes, deanonymization of TOR-services, darknet a new digital street for illicit drugs, research questions and hypothesis to guide researchers in further studies. Finally, in this study, we propose a model to examine and investigate anonymous online illicit markets.

Over the past three years, especially following Wanna-Cry malware, ransomware has become one of the biggest concerns for private businesses, state, and local government agencies. According to Homeland Security statistics, 1.5 million ransomware attacks have occurred per year since 2016. Cybercriminals often use creative methods to inject their malware into the target machines and use sophisticated cryptographic techniques to hold hostage victims' files and programs unless a certain amount of equivalent Bitcoin is paid. The return to the cybercriminals is so high (estimated $1 billion in 2019) without any cost because of the advanced anonymity provided by cryptocurrencies, especially Bitcoin [1]. Given this context, this study first discusses the current state of ransomware, detection, and prevention systems. Second, we propose a global ransomware center to better manage our concerted efforts against cybercriminals. The policy implications of the proposed study are discussed in the conclusion section.