Chatbots in Cybersecurity: Enhancing Security Chatbot Efficacy through Iterative Feedback Loops and User-Centric Approaches
Bibliographic record
Abstract
Chatbots are of continuous importance in our interactive lives. Although used in several domains, there are questions about its security assurance; therefore, there is a need to know its capabilities, limitations, and challenges in cybersecurity. The research explores the use of chatbots in enhancing cyber defences and their potentials. It examines chatbots’ current applications in cybersecurity, including IT services, information protection, and user education. Furthermore, the research proposes implementing an Intelligent Chatbot Security Assistant (ICSA) model on WhatsApp to detect and respond to cyberattacks based on user conversations and identifies the challenges with this implementation. To address these challenges, it suggests incorporating enhanced privacy measures, real-time monitoring, rigorous evaluation and validation, and concludes with user-centric design principles using iterative feedback. This research provides valuable insights into the use of chatbots in cybersecurity, their current level of research and implementation as a cybersecurity tool, and directions for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".