AI-Based Multi Cyber Threat Detection

Every element of daily life in the modern world is now reliant on the internet. The usage of the internet is increasing daily. The amount of time spent on the Internet has increased globally. Consequently, there is a growing risk of cyber threats and cybercrimes. We examine the difficulties in applying machine learning to cyber security, such as the risk of adversarial attacks, data quality, and model interpretability. We highlight new trends, like the combination of deep learning and AI-driven automation in threat detection systems, by examining case studies and existing research. The results highlight the significance of continuous machine learning research and innovation to improve cyber threat detection skills. The detection of cybercrimes and the fight against cyber threats have led to the development of numerous machine learning approaches. The aim of this study is to give an assessment of some of the popular machine learning methods for identifying some of the most dangerous online threats.

In the digital age, cyber threats have become increasingly sophisticated, necessitating innovative approaches to bolster security measures. Artificial Intelligence (AI) has emerged as a formidable tool in the realm of cyber security, offering advanced capabilities in threat detection, anomaly detection, and response automation. This article provides an overview of AI applications in cyber security, highlighting its role in mitigating risks and fortifying defense mechanisms. AI techniques such as machine learning, deep learning, and natural language processing empower security systems to analyze vast amounts of data in real-time, identifying patterns indicative of malicious activities. Through the utilization of AI-driven algorithms, cyber security platforms can proactively detect and neutralize cyber threats before they inflict substantial damage. Moreover, AI enables the automation of incident response processes, reducing response times and minimizing the impact of security breaches. Case studies from leading cyber security firms from the integral part of the studies and demonstrate the practical implementation of AI-driven solutions in safeguarding critical infrastructures against cyber threats. Resilience towards cyber-attacks and safeguarding sensitive data assets through leveraging AI technologies has been focused in the study.

In this era of ubiquitous and highly developed cyber dangers, cybersecurity has emerged as an essential issue for modern organisations. Interest in using AI to improve cyber threat detection and response skills is on the rise as conventional approaches fall behind the dynamic threat environment. Recent advancements, problems, and future prospects are highlighted in this review paper's thorough overview of cyber threat detection and response systems augmented with AI. At the outset, we cover the basics of artificial intelligence (AI) in cybersecurity and trace the development of systems to identify cyber threats. We continue by outlining the benefits and drawbacks of supervised, unsupervised, and reinforcement learning, three of the AI-driven threat detection methods now available. Here, we show how AI-powered systems may effectively mitigate cyber risks in many sectors using real-world applications and case studies. Data quality, adversarial assaults, and ethical issues are just a few of the constraints and problems that we highlight and provide solutions for. Lastly, we go into the latest developments and potential paths forward in AI-powered cybersecurity, highlighting the need of working together across disciplines and continuously doing research to keep up with ever-changing threats. Researchers, practitioners, and policymakers may use this paper as a guide to better understand AI in cybersecurity, where it is now, and how to make future breakthroughs.

Abstract: Due to the increased complexity and damage of cyberattacks in this digital age, the security of national infrastructure networks has become a vital concern. However, a possible approach to improve the cybersecurity of these crucial networks is to incorporate artificial intelligence (AI) into threat detection and response systems; to rapidly evaluate large data sets, identify anomalies, and automate countermeasures to lessen the effects of cyberattacks. The impact, implementation and approaches for anomaly detection and response automation of AI-powered solutions for safeguarding national infrastructure are examined in this paper. Understanding how AI technologies are used to automate threat detection and response, reviewing the operational usefulness of AI in enhancing cybersecurity measures and evaluating the deployment of these systems in critical infrastructure settings were also examined. The study revealed that the speed and accuracy of threat detection and response are greatly increased by AI-powered systems. The automation capacity of AI can potentially reduce the need for human analysts, while also providing faster threat mitigation. Additionally, the usefulness of AI across sectors indicates its practicality in situations and how it may adapt in response to new threats. In conclusion, AI-driven threat detection and response systems are an important development in national infrastructure network cybersecurity. Therefore, by improving the capacity to recognize and address cyber-attacks these technologies can ultimately increase the overall resilience of national infrastructures.

As digital systems become more integral to modern life, the nature of cyber threats has grown increasingly sophisticated, demanding more advanced defense strategies. Artificial Intelligence (AI) has become a pivotal force in modernizing cybersecurity, bringing real-time detection and predictive analysis into the spotlight. This paper investigates the role of AI technologies—including machine learning, deep learning, and natural language processing—in identifying, interpreting, and countering cyber threats. With AI, security frameworks can more effectively recognize unusual activity, uncover previously unknown vulnerabilities, and react swiftly to stealthy, long-term attacks like advanced persistent threats (APTs). The research also explores how AI is being blended with conventional cybersecurity tools, while addressing major challenges such as minimizing false alarms, resisting adversarial manipulation, and maintaining models that evolve with emerging threats. Ethical considerations are also examined, with a focus on ensuring that AI decisions in security contexts remain transparent and accountable. Through an analysis of real-world use cases, this study demonstrates how AI is redefining cyber defense, equipping organizations with smarter, more resilient protection against an ever-expanding array of digital threats.

The increased reliance of industrial automation on interconnected systems has made it vulnerable to cyber threats. Conventional security techniques frequently inadequately handle the dynamic and developing threat landscape. This chapter examines the essential function of Cyber Threat Intelligence (CTI) in improving the security framework of IACS environments. It specifically emphasizes utilizing artificial intelligence (AI) to automate and enhance cyber threat intelligence (CTI) processes, facilitating proactive threat detection, analysis, and response. This chapter will explore the problems associated with safeguarding IACS, the advantages of establishing a comprehensive CTI program, and the utilization of AI methodologies, including machine learning, deep learning, and natural language processing, for the gathering, analysis, and dissemination of threat data. Practical examples and case studies will demonstrate the implementation of AI-driven CTI techniques in reducing cyber risks and enhancing the resilience of essential industrial infrastructure.

BackgroundPhishing represents a category of cyber-attacks based on social engineering, with a significant impact on individuals and organizations, and a high capacity for reinvention by adapting its modus operandi according to technological advancements. With a relatively simple scenario and without using sophisticated technologies, phishing attacks exploit user vulnerabilities, convincing them to disclose sensitive personal or organizational data. Within anti-phishing solutions, the detection of spoofed URLs, counterfeit websites, and email or other types of messages that lure the user into entering their data in a form, plays an important role. Against this backdrop, artificial intelligence (AI) technologies, particularly Machine Learning (ML), have been successfully employed in phishing detection, with a rich body of literature in this field.ObjectiveA review of the existing literature on phishing detection using AI was conducted. This study aims to fill this gap by providing comprehensive bibliometric analysis, complementing existing surveys in the field, focusing on the role of AI in phishing detection.MethodsA total of 1096 documents focusing on AI, ML, Deep Learning (DL), or Natural Language Processing (NLP) in phishing detection were extracted from the Web of Science (WoS) scientific database. The information from these documents was subsequently loaded into the Biblioshiny (Bibliometrix package) and VOSviewer software.ResultsThe dataset allowed for the identification of publication trends, influential documents and publications, patterns of author collaboration, and key topics of interest within the main author clusters. A thematic analysis of the field highlighted driving themes, niche themes, emerging and declining themes, and basic themes. Furthermore, thematic evolution over time was examined based on authors’ keywords. A thorough review of the most relevant articles identified through bibliometric analysis was conducted to discuss the primary methods of phishing detection using AI.ConclusionThe research field of AI in phishing detection has evolved significantly starting with 2016, with a focus on using ML algorithms to identify phishing websites by extracting discriminative features, and experienced a consistent growth in 2024. Recent work emphasizes a shift from classical ML to DL, the importance of feature selection and engineering, and the use of hybrid models and classifier stacking.

In our prior work, we had employed a fixed photo-absorption, coherent, and incoherent cross-section material model to derive a shot-noise limited description of the X-ray measurements in check-point or a checked baggage threat-detection systems. Using this measurement model, we developed an information-theoretic metric, which provides an upper-bound on the performance of a threat-detection system. However, the fixed cross-section material model does not incorporate material variability arising from inherent variations in its composition and density. In this work, we develop a multi-energy model of material variability based on composition and density variations and combine it with the shot-noise photon detection process to derive a new X-ray measurement model. We derive a computationally scalable analytic approximation of an information-theoretic metric, i.e. Cauchy-Schwarz mutual information, based on this material variability model to quantify the upper-bound on the performance of the threat-detection task. We demonstrate the effect of material variations on the performance bounds of X-ray transmission-based threat detection systems as a function of detector energy resolution and source fluence.

With the massive growth of technology, QR Code has a wide range of applications. QR Code is a 2D matrix Bar Code where the information is stored both in horizontal and vertical dimensions. QR Code can hold a larger amount of data in a smaller space, performs reliable error-correction at higher speed and has faster response time. Increasing use of smart phones among all age groups made accessing QR Code easier by providing end-user content, including Web links, personal details etc. QR Code is versatile and is used in diverse research fields such as online banking, attendance management, health care, help visually impaired people, security applications such as different kinds of cryptography and steganography as well. Secure authentication, is achieved using data-hiding algorithms with the embedded QR Code. This paper highlights security aspects handled by QR Code in many applications and further directs to secured transportation of the data using SQRC which has its real time applications like vehicle and identity verifications.

Financial institutions in emerging economies face significant cybersecurity challenges due to the increasing sophistication of cyber threats and limited resources for detection and prevention. This paper introduces Big Data-Driven Cybersecurity Models, designed to address gaps in threat detection and prevention by leveraging advanced analytics, machine learning, and real-time data processing. These models aim to strengthen the cybersecurity frameworks of financial institutions, particularly in emerging economies, by improving threat intelligence, detection accuracy, and response efficiency. The proposed models focus on the integration of big data technologies, such as distributed computing frameworks, data lakes, and advanced visualization tools, to handle large volumes of structured and unstructured data. By analyzing data from diverse sources, including transaction logs, network traffic, and customer interactions, the models enable the identification of anomalous patterns and potential security threats. Machine learning algorithms enhance predictive capabilities, allowing institutions to detect previously unknown threats and mitigate risks proactively. A key component of the models is their scalability and adaptability to resource-constrained environments. The framework includes cloud-based solutions to reduce infrastructure costs and ensure accessibility for smaller financial institutions. Additionally, the models emphasize the importance of fostering cybersecurity awareness and regulatory compliance to address human and organizational vulnerabilities. Validation through case studies and simulations demonstrates the effectiveness of these models in mitigating cybersecurity risks. Results show significant improvements in threat detection rates, response times, and cost-efficiency. These findings underscore the potential of big data-driven approaches to enhance the resilience of financial institutions against evolving cyber threats. This research provides actionable insights for policymakers, financial institutions, and technology developers seeking to strengthen cybersecurity in emerging economies. By adopting the proposed models, financial institutions can bridge critical gaps in threat detection and prevention, ensuring the security and trust necessary for sustainable economic growth.

Background As smart phones have been introduced and produced rapidly since November 2009, methods of communication have also varied. Social network services such as Twitter and Facebook have became available without any restraints in terms of time and space courtesy of mobile applications available through online services such as Apple’s Appstore. From individuals to business management, the way of sharing information is faced with a new turning point, and it is Quick Response Codes (QR codes) that have arisen in response to this new marketing trend. Methods This study analyzed advertisements in Korea and elsewhere that adopted QR code as an expressive marketing element between 2009 and 2012. Furthermore, a survey was conducted to analyze how different types of QR codes affect the effect of advertisements. Results An analysis of research results shows there is a difference between different types of QR codes. Especially, creative QR codes that are different from conventional square images and iconotype QR codes that directly expose the actual image related to the brand scored high in advertisement attention, favor, and participation. Further, logotype QR code that indicate the logo or symbol of the brand scored high in advertisement effect in comparison to general QR codes, but were less effective than other QR codes with different designs. Conclusion While previous studies excluded external factors of QR codes, this study analyzed the advertisement effect of QR codes that belong to the media so that it can provide a starting point for producers to study the role and utility of QR codes in the future.

The new generation of network security threats present a mixed and multi-stage attack mode. Using AI technology for accurate detection has gradually become a consensus in the field of network security. The paper briefly describes the background and research status of threat detection and traceability, puts forward a network security threat detection and traceability system based on AI, and designs the overall system architecture of network data acquisition and traffic processing, security threat detection engine, situational awareness and traceability evidence. According to different attack types, AI based detection engine is studied, and threat intelligent detection and location services for cyberspace and application system is developed by using cloud security service platform. Through AI detection, intelligence detection, anomaly detection and traffic gene detection, real-time monitoring, traceability analysis and emergency disposal of network security threats are realized. At the end of the paper, three typical threat detection application scenarios are recommended.

Online Banking and Customer Service Delivery in Malaysia: Data Screening and Preliminary Findings

Risk management is one of the fundamental pillars of IT program management, particularly as organizations navigate the increasingly complex digital landscape of the 21st century. With the growing reliance on digital infrastructures, data, and interconnected systems, the challenges associated with cybersecurity, artificial intelligence (AI) integration, and infrastructure stability are becoming more pronounced. These three components—cybersecurity, AI, and infrastructure stability—are intricately linked, and their balance is essential for ensuring the long-term viability, resilience, and security of an organization’s IT systems. As organizations adopt new technologies and expand their digital footprints, IT managers face the critical task of balancing these elements in order to build robust, future-proof IT programs. The role of cybersecurity in IT program management has evolved significantly over the past few decades. Historically, cybersecurity was often relegated to being a peripheral concern, managed in isolation by dedicated security teams. Traditional approaches primarily focused on perimeter defenses, such as firewalls, intrusion detection systems (IDS), and antivirus software, to keep cyber threats at bay. While these methods provided some degree of protection, they were largely reactive and could only address known threats. As cyber adversaries have become more sophisticated, cyber threats have transcended basic attacks and evolved into more complex and persistent tactics, such as advanced persistent threats (APTs), zero-day exploits, and ransomware attacks. These evolving threats have highlighted the limitations of traditional cybersecurity measures and underscored the need for more proactive, dynamic security solutions. As cyber threats continue to evolve, organizations are increasingly turning to artificial intelligence (AI) to enhance their cybersecurity capabilities. AI, particularly in the form of machine learning (ML) and deep learning (DL), is enabling IT teams to move from traditional, rule-based security systems to more adaptive and intelligent systems that can detect anomalies, predict vulnerabilities, and respond to incidents in real-time. AI-driven cybersecurity solutions offer a range of capabilities, such as threat detection, incident response, and automated remediation, all of which can significantly reduce the time it takes to detect and mitigate security breaches. AI-powered systems can analyze vast amounts of data, identify patterns and anomalies, and learn from past incidents to improve detection accuracy over time. This enables organizations to identify and respond to threats much more quickly and effectively than traditional systems that rely on predefined rules or signatures. The integration of AI into IT program management offers numerous benefits, including improved efficiency, scalability, and accuracy in detecting and responding to cyber threats. However, the implementation of AI also presents new challenges. For one, AI technologies themselves are not immune to vulnerabilities. Adversarial attacks, where malicious actors manipulate AI systems by feeding them false data or exploiting weaknesses in their models, represent a significant risk. AI models can also be vulnerable to bias if not properly trained on diverse, representative datasets. Additionally, AI-driven systems require large amounts of data to function effectively, raising concerns about data privacy and security. Organizations must carefully consider how to manage and protect the vast amounts of sensitive data needed to train AI models, while also ensuring compliance with data protection regulations, such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA).

Online banking is a contemporary mode and a technological channel to facilitate the emerging demands of customers today. Increase in number of customers for online banking as a matter fact is related to customer’s awareness in a geographical space. Many customers today are adopting online banking through internet for their financial transactions especially in the current COVID-19 pandemic situation, however, due to lack of awareness, expertise and connectivity resources, a majority of prospect customers still have not adopted this new mode of transaction in Pakistan. The main objective of this study is to know the customers’ perception regarding the usage of online banking in Karachi Pakistan. Most of the researchers so far have suggested that mostly the customers are unaware about online banking services and the reason, moreover due to lack of trust among customers on online banking services, customers are reluctant to adopt this medium for the financial transactions. This research project thus examines the customer’s trust, perception, problems related to online banking and suggests about online banking concerns especially in the context of Pakistan. This study explains that mostly customers prefer online banking services over branch banking due to its reliability, convenience, safety, speed and security, user-friendliness, and error less system. The findings of this study will provide the banks the answer to the questions that how they can provide online banking better services and what are the major issues that should be reduces in society to satisfy customers in a better way, so that they can compete their competitors in the industry and overall increase the number of online banking customers.