Optimized Deep Learning for Enhanced Trade-off in Differentially Private Learning

Geetha, P.; Naikodi, Chandrakant; Suresh, L. Padma

doi:10.48084/etasr.4017

Cited by 3 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To effectively detect insider threats and protect employee privacy, research should focus on the analysis of cryptographic protocols, secure multiparty computation, and differential privacy. Ensuring that these techniques not only maximize efficiency and benefits but also unquestionably protect sensitive data integrity throughout the entire detection process is crucial [37][38]. Finally, human-centric methods that enable cooperation between AI systems and human analysts represent an important field for future study.…”

Section: Wwwetasrcom Yilmaz and Can: Unveiling Shadows: Harnessing Ar...mentioning

confidence: 99%

Unveiling Shadows: Harnessing Artificial Intelligence for Insider Threat Detection

Yilmaz,

Can

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Insider threats pose a significant risk to organizations, necessitating robust detection mechanisms to safeguard against potential damage. Traditional methods struggle to detect insider threats operating within authorized access. Therefore, the use of Artificial Intelligence (AI) techniques is essential. This study aimed to provide valuable insights for insider threat research by synthesizing advanced AI methodologies that offer promising avenues to enhance organizational cybersecurity defenses. For this purpose, this paper explores the intersection of AI and insider threat detection by acknowledging organizations' challenges in identifying and preventing malicious activities by insiders. In this context, the limitations of traditional methods are recognized, and AI techniques, including user behavior analytics, Natural Language Processing (NLP), Large Language Models (LLMs), and Graph-based approaches, are investigated as potential solutions to provide more effective detection mechanisms. For this purpose, this paper addresses challenges such as the scarcity of insider threat datasets, privacy concerns, and the evolving nature of employee behavior. This study contributes to the field by investigating the feasibility of AI techniques to detect insider threats and presents feasible approaches to strengthening organizational cybersecurity defenses against them. In addition, the paper outlines future research directions in the field by focusing on the importance of multimodal data analysis, human-centric approaches, privacy-preserving techniques, and explainable AI.

show abstract

Section: Wwwetasrcom Yilmaz and Can: Unveiling Shadows: Harnessing Ar...mentioning

confidence: 99%

Unveiling Shadows: Harnessing Artificial Intelligence for Insider Threat Detection

Yilmaz,

Can

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

show abstract

“…very effective. And it provides a reference model for the customer churn prediction method of e-commerce retail enterprises [2]. Nanqi Y E believes that the task of commodity recognition is the foundation of e-commerce development.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in E-commerce Retail

2022

View full text Add to dashboard Cite

With the continuous development of the e-commerce economy, consumers have gradually stepped from the dilemma of lack of information to the dilemma of excess information. In addition, under the influence of the epidemic, this year has set off a wave of live broadcasts in e-commerce, making more and more many people join the industry. In order to solve the shortcomings of existing research in the field of e-commerce retail, this paper discusses the functional equations of deep neural networks, the deep learning Caffe framework and the characteristics of e-commerce retail, and aims at the sample data of deep learning in the field of e-commerce retail and parameter settings are briefly introduced. And the design of the deep learning neural network e-commerce retail product recommendation system is discussed. Finally, the product click-through rate (CTR), transaction rate and favorable rate in the application of the deep learning neural network e-commerce retail product recommendation system designed in this paper are used in Adaboost. , RNN model for experimental comparison. The experimental data show that the click-through rate (CTR), transaction rate and favorable rate of products in the deep learning neural network application designed in this paper reach an average of 93%, while the Adaboost and RNN models reach 83% and 87% respectively. Therefore, it is verified that the model designed in this paper has good training effect and practical value in the field of e-commerce retail.

show abstract

Evaluating the Efficiency of Finance Methods in Residential Complex Projects in Iraq

Khameesa

altaay

2022

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Financial funding of a construction firm plays an important role in all aspects of the process development. It has been noted that financial crises have a direct impact on the construction industry. The Iraqi government, whether locally or globally, has faced a severe shortage of financing which has resulted in incomplete projects. Due to the financial crisis that Iraq went through which led to the suspension of many residential complex projects and the difficulty of the use of public financing methods, we researched the private financing (public-private partnership) methods instead of public financing methods in residential complex projects implementation. This study verified the financial problems and the factors that relate to the possibility of their occurrence with the use of a questionnaire that was designed and distributed to professionals in the field. Arbitration of the questionnaire, pilot study, questionnaire distribution, and statistical tests were conducted. The T-test (paired samples T-test) was used to find out if there are differences between the public and private financing methods. The results showed that the private financing (public-private partnership) methods, under the current conditions in Iraq, are better than the public financing methods.

show abstract

Optimized Deep Learning for Enhanced Trade-off in Differentially Private Learning

Cited by 3 publications

References 12 publications

Unveiling Shadows: Harnessing Artificial Intelligence for Insider Threat Detection

Unveiling Shadows: Harnessing Artificial Intelligence for Insider Threat Detection

Deep Learning in E-commerce Retail

Evaluating the Efficiency of Finance Methods in Residential Complex Projects in Iraq

Contact Info

Product

Resources

About