Proceedings of Sixth International Ethical Hacking Conference

Inhaltsverzeichnis

1. Frontmatter

2. Keynotes

   1. Frontmatter

   2. The Role of Artificial Intelligence in Enhancing Cybersecurity: Trends, Challenges, and Ethical Considerations

      Gleuto M. Serafim

      Abstract

      The increasing integration of Artificial Intelligence (AI) in the Cybersecurity domain has changed the way that countries defend themselves against ever-evolving and dynamic threats. This paper provides a broad survey of the use of AI in Cybersecurity, focusing on the new and emerging trends, the main issues, and the need for ethical steering (Harshith J, Gill MS, Jothimani M (2023) Evaluating the Vulnerabilities in ML systems in terms of adversarial attacks. arXiv preprint arXiv:2308.12918. https://arxiv.org/pdf/2308.12918). Based on a multi-disciplinary analysis, it reviews the development of AI-enhanced threat detection, real-time threat intelligence, automated incident response, and predictive analytics, with a critical assessment of adversarial machine learning and the use of AI in cyber weapons (Rafique SH, Abdallah A, Musa NS, Murugan T (2024) Machine learning and deep learning techniques for internet of things network anomaly detection—current research trends. Sensors 24(6):1968. https://doi.org/10.3390/s24061968). The research identifies the weaknesses of conventional security measures and explains how AI, especially deep learning and behavior-based models, improve detection and swift response (Schmit (2023) J Ind Inf Integr 36). In addition, the study looks at systemic issues such as data scarcity, explainability, systemic bias, and regulatory gaps. It suggests that a human-AI teaming approach is a viable way to achieve cyber resilience (Sarker IH, Janicke H, Mohammad N, Watters P, Nepal S (2023) AI potentiality and awareness: a position paper from the perspective of human-AI teaming in cybersecurity. Int Conf Intell Comput Optimiz 140–149. https://arxiv.org/pdf/2310.12162). The discussion is informed by current empirical evidence and policy, legal, and ethical frameworks for AI-driven cybersecurity practices, which emphasize transparency, fairness, and accountability (Ibid.; Bernardez Molina S, Nespoli P, Gómez Mármol F (2023) Tackling cyberattacks through ai-based reactive systems: a holistic review and future vision. arXiv e-prints, arXiv: 2312.06229. https://doi.org/10.48550/arXiv.2312.06229). Finally, this paper argues for the need for cooperation across sectors, investment in the security of AI, and the creation of adaptive and ethically aligned AI structures to protect the digital environment in a connected world with increasing cyber risks (Andrada et al. (2023) AI & Soc 38:1321–1331; Musser M, Lohn A, Dempsey JX, Spring J, Kumar RSS, Leong B, Liaghati C, Martinez C, Grant CD, Rohrer D (2023) Adversarial machine learning and cybersecurity: risks, challenges, and legal implications. arXiv preprint arXiv:2305.14553. https://arxiv.org/abs/2305.14553; Oseni A, Moustafa N, Janicke H, Liu P, Tari Z, Vasilakos A (2021) Security and privacy for artificial intelligence: opportunities and challenges. arXiv preprint arXiv:2102.04661. https://arxiv.org/pdf/2102.04661).

   3. Hidden Figures: The Missing Connection in Women, Careers and the Video Game Industry

      Ana Penteado, Shambhu Prasad Chakrabarty

      Abstract

      This paper is about the persistent gender inequality in the video game industry. This gender bias has been exacerbated by large language model applications (LLMs) that mimic human emotions. Reality intertwines with digital imagery, such as virtual reality (VR) or monitor, which affects memory and human perception of both spaces. There is a struggle in human memory that is akin to deception. These deceptive patterns integrate the environmental experiences that make the video game industry profitable as required retained attention to the game is the main asset. The alignment of a profitable industry and emerging technology to dominate a digital market may alienate other genders when job opportunities are scarce and discriminatory, cementing a lack of social and economic mobility for future generations. That articulates a narrative of exclusion in the video game experience largely informed by cisgender identity, namely by men, which will eventually disempower women, who are also video game players and aspiring female video game designers, being marginalised in the profitable market of entertainment. Thus, artificial intelligence, a technology dominated also by one gender identity, may allow further gender segregation for decades ahead of us. We conclude that corporate and social incentives in this industry are still shy to integrate genders, leading to the creation of video games. In this paper's research, we found a necessity for more multicultural and pluralistic business strategies and focused female training programmes so that a joint effort to foster a culture of inclusion and belonging throughout the entire video game ecosystem will welcome women, particularly at senior levels, with the participation of video game developers and designers in the decision-making process, would be positive. Thus, the representation of women in this industry would permit a rapid career progression in the video game corporate space, which is much needed and justified for a better enjoyment of an inclusive and lucrative game experience.

3. Evolution of AI

   1. Frontmatter

   2. Generative AI to Bridge the Gap and Improve Medical Reporting Interpretation

      Trishita Ghosh, Sudeep Ghosh, Suparna Biswas, Animesh kairi, Soumen Ghosh, Shekhar Maiti

      Abstract

      The aim and scope of this “research paper” is to design an approach for medical report generation for patients using generative artificial intelligence (GAI). The main model used in this approach is based on holistic artificial intelligence in medicine (HAIM). The integration of generative artificial intelligence (GAI) in healthcare communication promises precise diagnoses, streamlined reporting, and efficient integration with electronic health records (EHRs), thereby revolutionizing patient care to create an advanced artificial intelligence system that can analyze medical data and produce comprehensive, easy reports for patients. Additionally, the system aims to prompt immediate precautionary measures, when necessary, facilitated by the GAI. This initiative seeks to improve communication between “healthcare providers” and “patients” by delivering clear and accessible information regarding their health status, diagnosis, and treatment plans. Ultimately, the project aimed to empower patients with clearer insights into their health, thereby fostering a more collaborative decision-making process. By leveraging AI technology, this endeavor represents a major advancement in healthcare communication, addressing the challenge of complex “medical reports” and enabling individuals to make informed health decisions. It also offers numerous advantages to patients, including improved accuracy, enhanced safety measures, and tailored insights for more effective healthcare management. Furthermore, proactive health maintenance is encouraged by delivering timely and actionable recommendations.

   3. Electroencephalography-Based Autism Detection: A Comprehensive Review

      Suparna Karmakar, Suparna Biswas, Nanda Dulal Jana, Aveek Chattopadhyaya, Bishal Sarkar

      Abstract

      Autism is a neuro developmental condition that affects how a person perceives and interacts with the world with a challenging social communication and interaction, as well as restricted and repetitive behaviors. It is called Autism “spectrum” disorder (ASD) because it affects individuals differently and to varying degrees. Some people with autism may have mild symptoms, while others may have more severe challenges. Individuals with autism may have difficulty in understanding social cues, maintaining eye contact, and engaging in reciprocal conversations and also may have difficulty with verbal and nonverbal communication. To diagnose ASD, researchers have worked with Electroencephalography (EEG), Magnetic Resonance Imaging (MRI), and genetic and sociodemographic data. In this work we have reviewed the existing literature based on EEG for the diagnosis of ASD. EEG is a non-invasive technique used to record electrical activity in the brain. Small metal electrodes are placed on the scalp, and they detect electrical impulses generated by the firing of neurons in the brain. These impulses are then amplified and recorded, producing a visual representation of brain activity known as an EEG. It is commonly used in clinical settings to diagnose neurological disorders such as epilepsy, sleep disorders, and brain injuries. It's also used in research to study brain function. Here we have reviewed mainly EEG-based machine learning approaches to detect Autism. Future directions of research related to Autism are also mentioned.

   4. The Role of AI in Revolutionizing Fairness in Platform Gig Worker Contracts

      Oyndrila Ganguly

      Abstract

      The Fourth Industrial Revolution is characterized by rapid advancements in automation and artificial intelligence (AI), which are reshaping labor markets and significantly impacting platform gig workers. While the technologies such as platform economy offer great solutions, their effectiveness depends on existing policies. The government plays a crucial role in establishing labor conditions and mediating relationships between employers and workers. However, inadequate regulatory involvement often leads to the perpetuation of informal and precarious work, leaving gig workers deprived of job security, benefits, and fair wages. In this context, a contractual analysis of platform gig economy with the technologies reveals significant challenges faced by these workers under current contractual arrangements. The lack of adequate protections within these contracts allows exploitative practices to persist, rendering workers vulnerable with limited opportunities for skill development and career advancements. This highlights the critical need to examine the implications of contractual agreements on workers’ rights in an AI-driven labor landscape. Addressing the informal labor issues and improving skill development are essential for managing the effects of AI on employment; by scrutinizing the contracts governing the platform gig work, stakeholders can identify the weaknesses and advocate for stronger labor protection. This integrated approach ensures that the benefits of new technologies do not exacerbate existing inequalities, ultimately creating a more equitable environment for gig workers. The findings confirm the importance of contractual analysis as a tool to empower workers and safeguard their rights in the evolving platform gig economy.

   5. Artificial Intelligence and the Rights of Persons with Disabilities in India: Opportunities, Challenges and the Future

      Pallabi Sengupta

      Abstract

      Technological development has attempted to help improve the lives of persons with disabilities. This chapter will firstly attempt to analyze how Artificial Intelligence (AI)-enabled tools and gadgets are helping persons with different kinds of disabilities. Nowadays, people with different types of disabilities have different platforms offering them AI-based tools and applications tailored to suit their specific needs. In this context, various AI tools and gadgets suited to help persons with physical or neurological or psychiatric disabilities will be deliberated upon. Next, the chapter will explore how AI helps in realizing the various rights of persons with disabilities as enumerated under different laws on disability like the UN Convention on the Rights of Persons with Disabilities, the Rights of Persons with Disabilities Act, 2016, etc. AI has helped in realizing key fundamental rights of persons with disabilities—from the right to live with dignity to the right of non-discrimination to the freedom of movement. However, use of AI tools has sometimes posed a challenge for these people too—like the over-reliance on AI-based diagnostic tests to determine whether one is eligible for housing, or for some benefit under a disability scheme, or the use of discriminatory AI-based tests for job recruitment. In such a context, this chapter will delve into the negative impact of AI on persons with disabilities. Finally, the chapter will look into how, with more and more advancement in AI, the same can be used for the betterment of persons with disabilities.

   6. Mobilizing Automated Vehicles: Harmonizing the Intersection of Technological Innovations and Legal Regulations

      Shrabana Chattopadhyay, Ripon Bhattacharjee, Aritra Jana, Pubali De

      Abstract

      There will be significant shifts in transportation with the introduction of autonomous vehicles (AVs), which will increase efficiency, safety, and environmental friendliness. But these technologies can only be used to their full potential if technological breakthroughs are seamlessly integrated with strong legal regulations. The article delves into the ways in which technological advances and legal frameworks meet, highlighting how important regulatory measures are for ensuring the secure use of AVs. In addition, the article delves into the current legal framework around AVs, drawing attention to the difficulties caused by inconsistent regulations and the necessity for flexible rules that can stay up with the fast-paced advancements in technology. The purpose of this article is to examine current policies and case studies to shed light on how to effectively integrate technology advancements with legal requirements to create conditions that are favorable to the broad use of autonomous vehicles. The results highlight the need for manufacturers, lawmakers, and the general public to work together for the sake of society's safety and well-being during the shift to autonomous transportation.

   7. AI-Driven Analysis of Urban Heat Islands for Sustainable City Planning

      Rahul Vadisetty, Sathya Kannan, Ravi Kumar Vankayalapati, Harish Kumar Sriram, Chaitran Chakilam, Murali Malempati

      Abstract

      Due to the impact of urban heat islands (UHIs) on local temperature, air quality, energy consumption, and public health, the issue is severe in the frame of sustainable urban development. This study presents an investigation into artificial intelligence’s (AI) potential for analyzing and mitigating the effects of UHIs, with actionable insight into sustainable city planning. AI models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), using a wide variety of data ranging from satellite images, climate conditions, population density, and land cover classification, have localized UHI hotspots, analyzed various contributing factors, and simulated the efficiency of several mitigation strategies. The most critical correlations of UHI intensity with urban features-building density, vegetation cover, and material use are shown. The AI models effectively predict temperature fluctuations and quantify the effects mitigated by green infrastructure of green roofs and urban parks. These simulations support city planners in decision-making at a prioritization level by cooling potential and informing data-driven sustainable urban policies. This study shows AI’s potential in analyzing high-accuracy UHI and informing resilience strategies in urban areas. This paper describes how AI could improve the urban environment by underpinning sustainable urban planning and improving the quality of life of the urban population in the face of climatic changes. In contrast, most modern challenges related to data quality and computational demands are being addressed. The novelty here is provided through high-resolution UHI data analysis for data-driven sustainable urban policy. The results identify significant correlations of UHI intensity with features such as building density, vegetation cover, and material use. That showed the capability of AI to predict the fluctuation of temperature and to quantify the cooling effect from green infrastructure. It provides evidence-based recommendations for urban planners, focusing on AI-driven insights that will enhance urban resilience and improve the quality of life in the face of climate change.

   8. Next-Generation Sequencing of Human Genomes: Ethico-Legal Challenges with Regard to Data Security

      Abhisikta Basu, Keya De Mukhopadhyay

      Abstract

      Precision medicine is based on giving the correct treatment to the right patient at the right time. For appropriate treatment, it mainly looks for genomic information. By enabling the precise sequencing of many genes simultaneously, Next-generation sequencing (NGS) technologies are potentially transforming medical practice. Although there is still much debate on the clinical efficacy, this technology is becoming more widespread in oncology. Modern artificial intelligence (AI), especially deep learning, holds promise for improving the accuracy of variant calling, improving variant prediction, and making electronic health record (EHR) systems in NGS-based diagnostics more user-friendly. The influence of NGS on cancer patients is examined in this research based on preliminary data. The present obstacles that prevent all patients with the genetic mutation associated with a particular therapy from receiving that therapy are also highlighted. Additionally, it examines the current level of AI in NGS-based genetics, emphasizing its limitations and potential future approaches. Lastly, the research focusses on the three primary ethical domains: informed consent, privacy, and outcome review.

   9. Personality Prediction Through Graphology: Leveraging AI for Enhanced Recruitment Screening and Employee Retention

      Ansh Chaudhary, Mohuya Chakraborty

      Abstract

      This paper investigates a novel approach to personality prediction through graphology, combined with artificial intelligence (AI), as a tool for improving recruitment processes and tackling high attrition rates in modern organizations. Using a dataset of 5130 handwritten samples from subjects aged 18 to 67, this study focuses on predicting the “Big Five” personality traits: Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Openness. A convolutional neural network (CNN) model trained on this data achieved an accuracy of 74%, showcasing the potential for precise personality analysis based on handwriting patterns. This AI-driven graphological analysis can serve as a preliminary screening tool in recruitment, particularly identifying candidates prone to Neuroticism, which may correlate with mental health concerns and higher turnover risk. The performance metrics for this class shows moderate precision (0.51), high recall (0.78), and reasonable F1-score of 0.62, which indicates that the model is moderately effective in distinguishing Neuroticism from other traits as desired. Candidates who demonstrate positive indicators in traits associated with workplace stability like Agreeableness, Extraversion and Openness having high precision of 0.94, 0.95, and 0.80, respectively, and high specificity of 4.33, 9.18, and 5.63, respectively, are advanced in the hiring process. By integrating personality prediction into recruitment, organizations can better align new hires with role demands and organizational culture, fostering a workforce with higher engagement and resilience. The proposed approach addresses the critical challenges of employee retention and attrition, offering a proactive strategy for building a more stable, motivated, and cohesive workforce.