Learning Word Representation for the Cyber Security Vulnerability Domain

Mumtaz, Sara; Rodríguez, Carlos; Benatallah, Boualem; Al-Banna, Mortada; Zamanirad, Shayan

doi:10.1109/ijcnn48605.2020.9207140

Cited by 12 publications

(6 citation statements)

References 27 publications

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“…Natural Language Interfaces (NLIs), such as chatbots and virtual assistants, are central parts of this alignment. These NLIs facilitate smooth communication between humans and machines (Mumtaz et al, 2020). NLIs have become crucial in improving user experiences by offering intuitive and conversational interfaces.…”

Section: Human-centric Ai and Natural Language Interfacesmentioning

confidence: 99%

Human-Centric AI : Enhancing User Experience through Natural Language Interfaces

Saber Ismail

2024

JOWUA

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AI has significantly altered the way humans interact with technology. It is important to observe the impact of Natural Language Interfaces (NLIs) on user experiences in Human-Centric AI across various industries. Therefore, we specifically focus on the influence of Human-Centric AI and user interactions within AI chatbots in the United Arab Emirates (UAE). The aim of this study is to assess the factors that influence the acceptance of AI, examine its practical implications across different industries, and offer valuable insights for the responsible development of AI. A quantitative survey methodology was employed, involving 230 participants in the UAE. The research design, data collection, and analysis followed the Unified Theory of Acceptance and Use of Technology (UTAUT) model, which emphasizes performance expectancy, effort expectancy, social influence, and facilitating conditions. The survey encompassed a variety of participants from various organizations, with a majority expressing positive attitudes towards AI chatbots. The survey found that 80% of users agreed that AI systems improve task efficiency, 84% believe they help achieve goals, and 84% view them as practical. According to 75% of participants, the social impact is strongly influenced by AI chatbot system adoption. However, 80% understood the relevance of organizational infrastructure and favorable conditions. In particular, 72% of users stated that Natural Language Interfaces transform, indicating satisfactory user experiences. These features demonstrate the influence of Human-Centric AI adoption and its use in different organizations. Natural language interfaces play a critical role in improving human-centered AI user experiences, investigating theoretical issues and real-world applications, and providing guidance for the ethical use of AI.

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Section: Human-centric Ai and Natural Language Interfacesmentioning

confidence: 99%

Human-Centric AI : Enhancing User Experience through Natural Language Interfaces

Saber Ismail

2024

JOWUA

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“…Motivated by the large-scale foundational models's successes in many general domain NLP tasks, several domain-specific language models have been developed (Roy et al, 2017(Roy et al, , 2019Mumtaz et al, 2020). In scientific and bio-medical domains, there are SciBERT (Beltagy et al, 2019), Blue-BERT (Peng et al, 2019), ClinicalBERT (Huang et al, 2019), BioBERT (Lee et al, 2020) and Pub-MedBERT (Gu et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

“…They train Word2Vec models using malware descriptions. Similarly, Mumtaz et al (2020) train a Word2Vec model using security vulnerability-related bulletins and Wikipedia pages.…”

Section: Related Workmentioning

confidence: 99%

A Pretrained Language Model for Cyber Threat Intelligence

Park,

You

2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

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We present a new BERT model for the cybersecurity domain, CTI-BERT, which can improve the accuracy of cyber threat intelligence (CTI) extraction, enabling organizations to better defend against potential cyber threats. We provide detailed information about the domain corpus collection, the training methodology and its effectiveness for a variety of NLP tasks for the cybersecurity domain. The experiments show that CTI-BERT significantly outperforms several general-domain and security-domain models for these cybersecurity applications, indicating that the training data and methodology have a significant impact on the model performance.

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“…It's also likely that many ethical hackers, like bug bounty hunters, came into the craft the same way [15]. S. Mumtaz et al[2020] propose a word embedding for the cyber security vulnerability domain. They train our embedding model on multiple, rich, and heterogeneous security vulnerability information sources publicly available on the web.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach for Mitigating Insecure Direct Object Reference (IDOR) Bug Bounty Method

Singh¹,

Dandotiya²

2023

IJRASET

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This real-time paper explores Insecure Direct Object Reference (IDOR) vulnerabilities in the context of bug bounty programs. It provides an overview of IDOR attacks and their impact on web application security. The paper discusses common IDOR vulnerabilities, including how they can be discovered and exploited by attackers. It also highlights the importance of implementing proper access controls and other security measures to prevent IDOR attacks. The paper concludes with a discussion of best practices for identifying and mitigating IDOR vulnerabilities in bug bounty programs, including the use of manual testing

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Learning Word Representation for the Cyber Security Vulnerability Domain

Cited by 12 publications

References 27 publications

Human-Centric AI : Enhancing User Experience through Natural Language Interfaces

Human-Centric AI : Enhancing User Experience through Natural Language Interfaces

A Pretrained Language Model for Cyber Threat Intelligence

An Efficient Approach for Mitigating Insecure Direct Object Reference (IDOR) Bug Bounty Method

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