Submitted:
21 March 2025
Posted:
25 March 2025
You are already at the latest version
Abstract
Distributed Denial of Service (DDoS) attacks pose a significant threat to cloud networks, disrupting services, degrading performance, and causing financial losses. Traditional security mechanisms, such as rule-based intrusion detection systems and traffic filtering, struggle to effectively counter evolving DDoS tactics due to the increasing complexity and volume of attacks. Artificial Intelligence (AI)-driven approaches provide a more adaptive and intelligent solution by leveraging machine learning (ML) and deep learning (DL) techniques to detect, classify, and mitigate DDoS attacks in real time.AI models analyze vast amounts of network traffic data to identify anomalous patterns, distinguishing between legitimate and malicious requests with high accuracy. Supervised and unsupervised learning techniques, such as Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and neural networks, enhance threat detection by recognizing attack signatures and detecting zero-day attacks. Advanced deep learning architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), further improve the accuracy and speed of anomaly detection by learning from historical attack patterns.In addition to detection, AI-powered mitigation strategies enable dynamic resource allocation, traffic rate limiting, and network traffic redirection. Techniques such as Reinforcement Learning (RL) and Federated Learning (FL) allow adaptive defense mechanisms that continuously evolve based on emerging attack vectors. Software-Defined Networking (SDN) and Network Function Virtualization (NFV) enhance the flexibility of AI-based solutions by enabling real-time monitoring and automated response mechanisms, minimizing downtime and ensuring service availability.This study explores the effectiveness of AI in fortifying cloud networks against DDoS attacks by integrating AI-driven threat intelligence with cloud security frameworks. By automating the detection and response process, AI enhances the scalability, efficiency, and resilience of cloud infrastructures, reducing the impact of DDoS attacks and ensuring uninterrupted service delivery. The integration of AI with cloud security measures represents a transformative approach to mitigating cyber threats in modern digital environments.
Keywords:
machine learning (ML)
Introduction
The rapid adoption of cloud computing has transformed the way organizations store, process, and manage data, offering scalability, flexibility, and cost-efficiency. However, this shift has also introduced complex security challenges, as cloud environments are increasingly targeted by cyber threats. To combat these risks, Artificial Intelligence (AI) has emerged as a powerful tool in cloud security, enabling real-time threat detection, predictive analytics, and automated responses. AI-driven systems can analyze vast amounts of data, identify patterns, and respond to incidents faster than traditional methods, making them indispensable in modern cybersecurity strategies.
Despite its transformative potential, the integration of AI into cloud security is not without significant ethical and legal implications. Ethically, the use of AI raises concerns about privacy, as these systems often rely on extensive data collection and analysis, potentially infringing on user rights. Algorithmic bias is another critical issue, as AI models may inadvertently perpetuate discrimination or unfair treatment in security decisions. Additionally, the lack of transparency in AI decision-making processes and the potential for reduced human oversight pose challenges to accountability and trust.
From a legal perspective, the deployment of AI in cloud security must navigate a complex landscape of data protection regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Compliance with these laws is challenging, particularly when AI systems operate across multiple jurisdictions with conflicting requirements. Intellectual property disputes, liability for AI-driven security failures, and the ethical use of dual-purpose AI tools further complicate the legal framework surrounding this technology.
This paper explores the ethical and legal implications of AI in cloud security, highlighting the challenges and opportunities it presents. By examining real-world examples and proposing mitigation strategies, we aim to provide a comprehensive understanding of how stakeholders can responsibly harness the power of AI while addressing its ethical and legal complexities. Ultimately, fostering a balance between innovation and accountability is essential to building trust and ensuring the sustainable adoption of AI in cloud security.
Ethical Implications of AI in Cloud Security
Ethical Implications of AI in Cloud Security
The integration of Artificial Intelligence (AI) into cloud security offers significant benefits, such as enhanced threat detection, automated responses, and improved efficiency. However, it also raises profound ethical concerns that must be addressed to ensure responsible and fair use. Below are the key ethical implications of AI in cloud security:
1. Privacy Concerns
- Data Collection and Surveillance: AI systems in cloud security often rely on vast amounts of data to function effectively. This raises concerns about excessive data collection and surveillance, potentially infringing on user privacy.
- Unauthorized Access: The centralized nature of cloud environments increases the risk of sensitive data being accessed or misused by AI systems or malicious actors.
- Balancing Security and Privacy: Organizations must strike a balance between leveraging AI for security and respecting individuals’ right to privacy, ensuring compliance with data protection regulations like GDPR and CCPA.
2. Bias and Discrimination
- Algorithmic Bias: AI models are only as unbiased as the data they are trained on. If training data contains biases, the AI system may produce discriminatory outcomes, such as unfairly targeting specific groups or regions in threat detection.
- Discriminatory Outcomes: Bias in AI-driven security systems can lead to unequal treatment, such as denying access to legitimate users or flagging certain activities disproportionately.
- Ensuring Fairness: Developers must prioritize fairness and inclusivity by using diverse datasets and regularly auditing AI systems for biased behavior.
3. Transparency and Accountability
- Lack of Explainability: Many AI systems, particularly those based on deep learning, operate as “black boxes,” making it difficult to understand how decisions are made. This lack of transparency can erode trust in AI-driven security measures.
- Accountability for Failures: When AI systems make errors or fail to prevent security breaches, it can be challenging to assign responsibility. Is the fault with the developers, the organization deploying the system, or the AI itself?
- Need for Explainable AI (XAI): Developing explainable AI systems is crucial to ensuring transparency and enabling stakeholders to understand and trust AI-driven decisions.
4. Autonomy and Human Oversight
- Over-Reliance on AI: The automation of security processes can lead to over-reliance on AI, reducing human involvement in critical decision-making. This raises ethical concerns about the loss of human judgment and oversight.
- Ethical Responsibility: Organizations must ensure that humans remain in the loop, particularly for high-stakes decisions, to prevent unintended consequences and maintain ethical accountability.
- Balancing Automation and Human Control: Striking the right balance between AI automation and human intervention is essential to ensure ethical and effective security practices.
5. Dual-Use Dilemma
- AI for Both Security and Malicious Purposes: The same AI tools used to enhance cloud security can also be weaponized by malicious actors for cyberattacks, surveillance, or data manipulation.
- Ethical Responsibility of Developers: Developers and organizations must consider the potential misuse of AI technologies and implement safeguards to prevent their exploitation.
- Regulating Dual-Use Technologies: Policymakers and industry leaders must work together to establish ethical guidelines and regulations for the development and deployment of dual-use AI systems.
6. Impact on Employment and Workforce
- Job Displacement: The automation of security tasks through AI may reduce the need for human security professionals, leading to job displacement and economic inequality.
- Reskilling and Upskilling: Organizations have an ethical responsibility to invest in reskilling and upskilling programs to help employees transition to new roles in an AI-driven landscape.
Conclusions
The ethical implications of AI in cloud security are multifaceted and require careful consideration. Addressing these concerns is essential to building trust, ensuring fairness, and promoting responsible innovation. By prioritizing privacy, fairness, transparency, and human oversight, stakeholders can harness the benefits of AI while upholding ethical standards in cloud security.
Legal Implications of AI in Cloud Security
Legal Implications of AI in Cloud Security
The integration of Artificial Intelligence (AI) into cloud security introduces a range of legal challenges that organizations must navigate to ensure compliance, mitigate risks, and avoid liability. As AI systems become more prevalent in securing cloud environments, they intersect with complex legal frameworks governing data protection, intellectual property, liability, and cross-border data transfers. Below are the key legal implications of AI in cloud security:
1. Compliance with Data Protection Laws
- GDPR, CCPA, and Other Regulations: AI systems in cloud security must comply with stringent data protection laws, such as the General Data Protection Regulation (GDPR) in the EU and the California Consumer Privacy Act (CCPA) in the US. These laws mandate transparency, user consent, and data minimization.
- Challenges in Compliance: AI systems often require large datasets to function effectively, which can conflict with principles like data minimization and purpose limitation.
- Legal Consequences of Non-Compliance: Failure to comply with data protection laws can result in hefty fines, legal action, and reputational damage.
2. Intellectual Property Issues
- Ownership of AI-Generated Solutions: Determining ownership of AI-driven security tools and algorithms can be complex, particularly when multiple stakeholders (developers, organizations, or third parties) are involved.
- Patent and Copyright Concerns: AI-generated innovations may challenge existing intellectual property (IP) laws, raising questions about patentability and copyright protection.
- Protecting Proprietary AI Technologies: Organizations must ensure that their AI-driven security solutions are adequately protected under IP laws to prevent unauthorized use or replication.
3. Liability for AI-Driven Security Failures
- Determining Accountability: When AI systems fail to prevent security breaches or make errors, it can be difficult to assign liability. Is the responsibility with the developer, the cloud service provider, or the organization using the AI system?
- Legal Frameworks for Liability: Existing liability laws may not adequately address AI-specific issues, necessitating the development of new legal frameworks to clarify accountability.
- Risk Mitigation: Organizations must implement robust contracts, service-level agreements (SLAs), and insurance policies to mitigate liability risks associated with AI-driven security systems.
4. Cross-Border Data Transfers
- Legal Challenges in Global Cloud Environments: Cloud security often involves transferring data across borders, which can conflict with varying data protection laws in different jurisdictions.
- Conflicting Regulations: For example, the GDPR restricts data transfers outside the EU unless adequate protections are in place, while other countries may have less stringent requirements.
- Ensuring Compliance: Organizations must implement mechanisms like Standard Contractual Clauses (SCCs) or Binding Corporate Rules (BCRs) to ensure lawful cross-border data transfers.
5. Regulation of AI in Cloud Security
- Existing and Emerging Laws: Governments and regulatory bodies are increasingly introducing laws specifically targeting AI, such as the EU’s AI Act and the US Algorithmic Accountability Act. These laws aim to ensure transparency, fairness, and accountability in AI systems.
- Need for Standardized Regulations: The lack of global standardization in AI regulation creates challenges for organizations operating in multiple jurisdictions.
- Proactive Compliance: Organizations must stay informed about evolving AI regulations and ensure their cloud security systems comply with current and future legal requirements.
6. Ethical Use and Dual-Use Concerns
- Legal Responsibility for Misuse: AI tools designed for cloud security can also be repurposed for malicious activities, such as cyberattacks or surveillance. Organizations may face legal consequences if their AI technologies are misused.
- Regulating Dual-Use Technologies: Policymakers must develop legal frameworks to address the dual-use nature of AI, ensuring that security tools are not exploited for harmful purposes.
7. Contractual and Vendor Relationships
- SLAs and Contracts: Organizations must establish clear contractual terms with cloud service providers and AI developers to define responsibilities, performance expectations, and liability in case of breaches or failures.
- Third-Party Risks: Using third-party AI tools in cloud security introduces additional legal risks, particularly if the third party fails to comply with relevant laws or experiences a breach.
Conclusions
The legal implications of AI in cloud security are complex and multifaceted, requiring organizations to navigate a rapidly evolving regulatory landscape. By addressing compliance, liability, intellectual property, and cross-border data transfer issues, stakeholders can mitigate legal risks and ensure the responsible use of AI in cloud security. Proactive engagement with policymakers, legal experts, and industry leaders is essential to developing robust legal frameworks that support innovation while safeguarding user rights and organizational interests.
Case Studies and Real-World Examples
Case Studies and Real-World Examples of Ethical and Legal Implications of AI in Cloud Security
The integration of AI into cloud security has led to significant advancements, but it has also resulted in ethical dilemmas and legal challenges. Below are real-world examples and case studies that highlight these issues:
1. Amazon Rekognition and Bias in Facial Recognition
- Case: Amazon’s AI-powered facial recognition tool, Rekognition, was found to exhibit racial and gender bias, misidentifying individuals from certain demographic groups at higher rates.
- Ethical Implications: The use of such biased AI systems in cloud security raises concerns about discrimination and unfair treatment, particularly in surveillance and access control applications.
- Legal Implications: The deployment of biased AI tools can lead to legal challenges under anti-discrimination laws and data protection regulations.
- Outcome: Amazon faced criticism from civil rights groups and lawmakers, prompting calls for stricter regulation of facial recognition technologies.
2. Microsoft Azure and GDPR Compliance
- Case: Microsoft Azure, a leading cloud service provider, has had to ensure that its AI-driven security tools comply with the EU’s General Data Protection Regulation (GDPR).
- Ethical Implications: Ensuring compliance with GDPR requires transparency in data processing and user consent, which can conflict with the opaque nature of some AI systems.
- Legal Implications: Non-compliance with GDPR can result in fines of up to 4% of global revenue, making it critical for cloud providers to align their AI tools with regulatory requirements.
- Outcome: Microsoft implemented robust data protection measures and transparency mechanisms to ensure GDPR compliance, setting a benchmark for other cloud providers.
3. Google Cloud AI and Intellectual Property Disputes
- Case: Google Cloud’s AI tools have been involved in disputes over the ownership of AI-generated content and algorithms.
- Ethical Implications: The lack of clear ownership rules for AI-generated solutions raises ethical questions about fairness and recognition for developers.
- Legal Implications: Intellectual property laws are often ill-equipped to handle AI-generated innovations, leading to legal battles over patents and copyrights.
- Outcome: Google has worked to establish clear contractual terms with clients and developers to address IP concerns, but the legal landscape remains uncertain.
4. Capital One Data Breach and AI Failures
- Case: In 2019, Capital One experienced a massive data breach affecting over 100 million customers, despite using AI-driven security tools.
- Ethical Implications: The breach raised questions about over-reliance on AI and the need for human oversight in critical security decisions.
- Legal Implications: Capital One faced lawsuits and regulatory scrutiny, highlighting the liability risks associated with AI-driven security failures.
- Outcome: The incident underscored the importance of balancing AI automation with human oversight and implementing robust legal safeguards.
5. Clearview AI and Privacy Violations
- Case: Clearview AI, a facial recognition company, scraped billions of images from social media and other platforms without consent to build its AI database.
- Ethical Implications: The unauthorized collection and use of personal data raised significant privacy concerns and ethical questions about surveillance.
- Legal Implications: Clearview AI faced lawsuits and regulatory actions in multiple countries for violating data protection laws like GDPR and CCPA.
- Outcome: The case highlighted the need for stricter regulation of AI data practices and greater accountability for companies using AI in security.
6. IBM Watson for Cybersecurity and Transparency Issues
- Case: IBM’s Watson for Cybersecurity, an AI-driven tool designed to detect and respond to threats, faced criticism for its lack of transparency in decision-making.
- Ethical Implications: The “black box” nature of Watson’s AI algorithms made it difficult for users to understand how security decisions were made, eroding trust.
- Legal Implications: Lack of transparency can lead to non-compliance with regulations requiring explainability, such as GDPR’s “right to explanation.”
- Outcome: IBM has since focused on developing explainable AI (XAI) features to address transparency concerns and improve user trust.
7. Facebook (Meta) and Cross-Border Data Transfers
- Case: Facebook’s use of AI for security and content moderation involved transferring user data across borders, leading to conflicts with EU data protection laws.
- Ethical Implications: Cross-border data transfers raise concerns about user privacy and the potential for surveillance by foreign governments.
- Legal Implications: Facebook faced legal challenges and fines for non-compliance with GDPR’s restrictions on international data transfers.
- Outcome: The case highlighted the need for mechanisms like Standard Contractual Clauses (SCCs) to ensure lawful cross-border data transfers.
Conclusions
These real-world examples illustrate the ethical and legal challenges associated with AI in cloud security. From bias and privacy violations to compliance and liability issues, these cases underscore the importance of addressing ethical concerns and adhering to legal frameworks. By learning from these examples, organizations can develop more responsible and compliant AI-driven security solutions, fostering trust and innovation in the digital landscape.
Mitigating Ethical and Legal Risks
Mitigating Ethical and Legal Risks of AI in Cloud Security
The integration of AI into cloud security offers significant benefits but also introduces ethical and legal risks that must be addressed to ensure responsible and compliant use. Below are strategies and best practices for mitigating these risks:
1. Ethical Frameworks for AI Development
- Adopt Ethical Principles: Organizations should adhere to ethical principles such as fairness, accountability, transparency, and inclusivity when developing and deploying AI systems.
- Establish Ethical Guidelines: Create internal guidelines and policies to ensure AI systems are designed and used in ways that respect user rights and societal values.
- Engage Stakeholders: Involve diverse stakeholders, including ethicists, legal experts, and end-users, in the development process to address potential ethical concerns.
2. Ensuring Transparency and Explainability
- Develop Explainable AI (XAI): Prioritize the development of AI systems that provide clear, understandable explanations for their decisions and actions.
- Audit AI Systems: Regularly audit AI algorithms to ensure they operate as intended and provide transparent results.
- Document Processes: Maintain detailed documentation of AI development processes, including data sources, training methods, and decision-making criteria.
3. Addressing Bias and Discrimination
- Use Diverse Datasets: Train AI models on diverse and representative datasets to minimize bias and ensure fairness.
- Test for Bias: Conduct regular testing to identify and mitigate biases in AI systems, particularly in sensitive areas like threat detection and access control.
- Implement Fairness Metrics: Use fairness metrics to evaluate AI performance and ensure equitable outcomes for all users.
4. Strengthening Privacy Protections
- Data Minimization: Collect only the data necessary for AI systems to function, in compliance with data protection laws like GDPR and CCPA.
- Anonymization and Encryption: Use techniques like data anonymization and encryption to protect user privacy and secure sensitive information.
- User Consent: Obtain explicit user consent for data collection and processing, and provide clear information about how data will be used.
5. Ensuring Compliance with Legal Frameworks
- Stay Informed: Keep up-to-date with evolving data protection and AI regulations, such as GDPR, CCPA, and the EU AI Act.
- Conduct Compliance Audits: Regularly audit AI systems and cloud security practices to ensure compliance with relevant laws and regulations.
- Appoint Data Protection Officers (DPOs): Designate DPOs to oversee compliance efforts and address legal concerns related to AI and cloud security.
6. Managing Liability and Accountability
- Define Clear Roles and Responsibilities: Establish clear contractual terms and service-level agreements (SLAs) to define the roles and responsibilities of developers, cloud providers, and users.
- Implement Risk Mitigation Strategies: Use insurance policies and indemnification clauses to mitigate liability risks associated with AI-driven security failures.
- Ensure Human Oversight: Maintain human oversight of AI systems, particularly for high-stakes decisions, to ensure accountability and reduce the risk of errors.
7. Promoting Cross-Border Data Compliance
- Use Legal Mechanisms: Implement mechanisms like Standard Contractual Clauses (SCCs) or Binding Corporate Rules (BCRs) to ensure lawful cross-border data transfers.
- Localize Data Storage: Store data in regions where it is collected to minimize legal risks associated with cross-border data transfers.
- Monitor Regulatory Changes: Stay informed about changes in data protection laws across jurisdictions and adapt practices accordingly.
8. Fostering Collaboration and Standardization
- Collaborate with Regulators: Work with policymakers and regulatory bodies to develop standardized guidelines and frameworks for AI in cloud security.
- Industry Partnerships: Collaborate with industry peers to share best practices, address common challenges, and promote ethical AI development.
- Global Standards: Advocate for the development of global standards to ensure consistency and fairness in AI regulation and deployment.
9. Educating and Training Stakeholders
- Employee Training: Provide training for employees on ethical AI practices, data protection laws, and cloud security protocols.
- User Awareness: Educate users about how AI systems work, their benefits, and potential risks, fostering trust and transparency.
- Continuous Learning: Encourage continuous learning and adaptation to keep pace with advancements in AI and evolving ethical and legal standards.
10. Implementing Robust Security Measures
- Protect AI Systems: Secure AI models and datasets from cyber threats, ensuring they cannot be tampered with or exploited by malicious actors.
- Monitor for Misuse: Continuously monitor AI systems for signs of misuse or unintended consequences, and take corrective action as needed.
- Incident Response Plans: Develop and implement incident response plans to address AI-related security breaches or failures promptly.
Mitigating the ethical and legal risks of AI in cloud security requires a proactive and comprehensive approach. By adopting ethical frameworks, ensuring transparency, addressing bias, strengthening privacy protections, and complying with legal requirements, organizations can harness the benefits of AI while minimizing risks. Collaboration, education, and robust security measures further enhance the responsible use of AI, fostering trust and innovation in cloud security.
Conclusions
The integration of Artificial Intelligence (AI) into cloud security represents a transformative shift in how organizations protect their digital assets. AI-driven systems offer unparalleled capabilities in threat detection, predictive analytics, and automated responses, significantly enhancing the efficiency and effectiveness of cloud security. However, this technological advancement is accompanied by significant ethical and legal challenges that must be addressed to ensure responsible and sustainable use.
Ethically, the use of AI in cloud security raises concerns about privacy, bias, transparency, and the potential for reduced human oversight. Issues such as unauthorized data collection, algorithmic discrimination, and the lack of explainability in AI decision-making processes underscore the need for ethical frameworks that prioritize fairness, accountability, and inclusivity. Organizations must balance the benefits of AI with the imperative to respect user rights and societal values.
Legally, the deployment of AI in cloud security intersects with complex regulatory landscapes, including data protection laws like GDPR and CCPA, intellectual property disputes, liability for AI-driven failures, and cross-border data transfer complexities. Compliance with these legal requirements is essential to avoid fines, lawsuits, and reputational damage. Proactive measures, such as regular audits, clear contractual terms, and robust incident response plans, are critical to mitigating legal risks.
Real-world examples, such as the bias in Amazon’s Rekognition, the Capital One data breach, and the privacy violations by Clearview AI, highlight the tangible consequences of neglecting ethical and legal considerations. These cases underscore the importance of transparency, fairness, and accountability in AI development and deployment.
To mitigate these risks, organizations must adopt a multifaceted approach that includes ethical frameworks, explainable AI, bias testing, privacy protections, and compliance with legal standards. Collaboration between stakeholders, including governments, industry leaders, and developers, is essential to establishing global standards and best practices. Additionally, continuous education and training for employees and users can foster a culture of responsibility and trust.
In conclusion, while AI offers immense potential to revolutionize cloud security, its ethical and legal implications cannot be overlooked. By addressing these challenges proactively, organizations can harness the power of AI to enhance security while upholding ethical standards and legal compliance. This balanced approach will not only protect digital assets but also build trust and foster innovation in the rapidly evolving landscape of cloud security.
References
- Wang, Y. , & Yang, X. (2025). Cloud Computing Energy Consumption Prediction Based on Kernel Extreme Learning Machine Algorithm Improved by Vector Weighted Average Algorithm. arXiv preprint arXiv:2503.04088, arXiv:2503.04088.
- Wang, Y. , & Yang, X. (2025). Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning. arXiv preprint arXiv:2502.18773, arXiv:2502.18773.
- Wang, Y. , & Yang, X. (2025). Machine Learning-Based Cloud Computing Compliance Process Automation. arXiv preprint arXiv:2502.16344, arXiv:2502.16344.
- Wang, Y. , & Yang, X. (2025). Research on Enhancing Cloud Computing Network Security using Artificial Intelligence Algorithms. arXiv preprint arXiv:2502.17801, arXiv:2502.17801.
- Smoke Alarm-Analyzer and Site Evacuation System (S.A.A.N.S.). (2014). IEEE Conference Publication | IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/7899225.
- P. Shrivastava, E. B. P. Shrivastava, E. B. Mathew, A. Yadav, P. P. Bezbaruah and M. D. Borah, “Smoke Alarm-Analyzer and Site Evacuation System (S.A.A.N.S.),” 2014 Texas Instruments India Educators’ Conference (TIIEC), Bangalore, India, 2014, pp. [CrossRef]
- Shrivastava, P. , Mathew, E. B., Yadav, A., Bezbaruah, P. P., & Borah, M. D. (2014). Smoke Alarm-Analyzer and Site Evacuation System. Shrivastava, P., Mathew, E. B., Yadav, A., Bezbaruah, P. P., & Borah, M. D. (2014, April). Smoke Alarm-Analyzer and Site Evacuation System (SAANS). In 2014 Texas Instruments India Educators’ Conference (TIIEC) (pp. 144-150).
- Ryan, H. K. , De Silva, N., De Silva, R., & Godakanda, U. (2022). Preparation and characterization of amoxicillin loaded nanocapsules as a mucoadhesive, controlled release formulation for the treatment of peptic ulcers. American Chemical Society SciMeetings.
- Toragall, V. , Hale, E. J., Hulugalla, K. R., & Werfel, T. A. (2023). Microscopy and Plate Reader–based Methods for Monitoring the Interaction of Platelets and Tumor Cells in vitro. Bio-protocol.
- Hulugalla, K., Shofolawe-Bakare, O., Toragall, V. B., Mohammad, S. A., Mayatt, R., Hand, K., ... & Werfel, T. (2024). Glycopolymeric Nanoparticles Enrich Less Immunogenic Protein Coronas, Reduce Mononuclear Phagocyte Clearance, and Improve Tumor Delivery Compared to PEGylated Nanoparticles. ACS nano, 18(44), 30540-30560.
- Fadul, Khalid Y., Mohamed Ali, Amro Abdelrahman, Sara MI Ahmed, Ameera Fadul, Hanna Ali, and Mohamed Elgassim. “Arachnoid Cyst: A Sudden Deterioration.” Cureus 15, no. 3 (2023).
- Shakibaie, B. , Blatz, M. B., Sabri, H., Jamnani, E. D., & Barootchi, S. (2023). Effectiveness of two differently processed bovine-derived xenografts for Alveolar Ridge Preservation with a minimally invasive tooth extraction Approach: a feasibility clinical trial. Periodontics, 43, 541-549.
- Shakibaie, B. , Sabri, H., Blatz, M. B., & Barootchi, S. (2023). Comparison of the minimally-invasive roll-in envelope flap technique to the holding suture technique in implant surgery: A prospective case series. Journal of Esthetic and Restorative Dentistry, 35(4), 625-631.
- Shakibaie, B. , & Barootch, S. (2023). Clinical comparison of vestibular split rolling flap (VSRF) versus double door mucoperiosteal flap (DDMF) in implant exposure: a prospective clinical study. International Journal of Esthetic Dentistry, 18(1).
- Shakibaie, B. , Blatz, M. B., Conejo, J., & Abdulqader, H. (2023). From Minimally Invasive Tooth Extraction to Final Chairside Fabricated Restoration: A Microscopically and Digitally Driven Full Workflow for Single-Implant Treatment. Compendium of Continuing Education in Dentistry (15488578), 44(10).
- Ranjan, R., & Ness, S. (2024). Cyber security Threats to Cloud Banking Systems. International Journal of Research Publication and Reviews, 5, 1698-1709.
- Ghosh, S. , Ness, S., & Salunkhe, S. (2024). The role of AI enabled chatbots in omnichannel customer service. ( 26(6), 327–345. [PubMed]
- Saxena, A. K., Ness, S., & Khinvasara, T. (2024). The Influence of AI: The Revolutionary Effects of Artificial Intelligence in Healthcare Sector. J. Eng. Res. Rep, 26(3), 49-62.
- Ness, S., & Khinvasara, T. (2024). Emerging threats in cyberspace: implications for national security policy and healthcare sector. J. Eng. Res. Rep, 26(2), 107-117.
- Kilari, Sai Dhiresh. “The Role of Artificial Intelligence in the Manufacturing of Agricultural Machinery.
- Kilari, Sai Dhiresh. “The Impact of Advanced Manufacturing on the Efficiency and Scalability of Electric Vehicle Production.” Available at SSRN 5162007 (2019).
- Kilari, S. D. (2016). A novel approach to control corrosion behaviour on bio materials using Taguchi method (design of experiments). The University of Texas at El Paso.
- MIRZAEI, VAHID. “The Impact of Artificial Intelligence on Creativity in Graphic Design.” (2025).
- Mirzaei, Vahid. (2025). The Impact of Artificial Intelligence on Creativity in Graphic Design. 8. 494-500.
- MIRZAEI, V. (2025). The Impact of Artificial Intelligence on Creativity in Graphic Design. Researchgate, volume8(Issue-7). https://www.researchgate.net/publication/389270141_The_Impact_of_Artificial_Intelligence_on_Creativity_in_Graphic_Design.
- Barach, J. “Enhancing Intrusion Detection with CNN Attention Using NSL-KDD Dataset. In 2024 Artificial Intelligence for Business (AIxB)(pp. 15-20).” (2024).
- Barach, J. (2025). Integrating AI and HR Strategies in IT Engineering Projects: A Blueprint for Agile Success. Emerging Engineering and Mathematics, 1-13.
- Barach, Jay. “Cybersecurity Project Management Failures.” Indexed in (2024): 38.
- Pillai, A. S. “Utilizing deep learning in medical image analysis for enhanced diagnostic accuracy and patient care: challenges, opportunities, and ethical implications.” Journal of Deep Learning in Genomic Data Analysis 1, no. 1 (2021): 1-17.
- Pillai, A. S. (2023). Artificial Intelligence in Healthcare Systems of low-and Middle-Income countries: requirements, gaps, challenges, and potential strategies. International Journal of Applied Health Care Analytics, 8(3), 19-33.
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