Cyber Threat Intelligence Framework

Additionally, different analysis methods including fuzzy logic assessment system, visual analytics techniques and natural language processing algorithms, will be employed to analyze these datasets. Moreover, based on the evaluation of these existing frameworks and analysis methods, a new CTI framework will be developed which can adequately address the problem of adversarial threats against AI models. This framework should include not only efficient detection mechanisms but also adaptive countermeasures at various levels in order to quickly detect any potential attacks and then apply appropriate countermeasures for protection against them. The ultimate goal is to increase the resilience of AI systems by developing a robust CTI framework which ensures secure operations even when facing complex adversarial threats. In order to achieve this goal successfully, this dissertation study will use both qualitative and quantitative methodologies in analyzing existing security frameworks and implementing the proposed CTI framework. In terms of qualitative research strategy, semi-structured interviews with experts involved in cybersecurity or computer engineering fields would help us gain insights into how existing technologies are applied towards securing artificial intelligence models from adversaries’ attacks. On top of that we are going to conduct literature reviews related to CTI frameworks along with other relevant sources such as blogs posts or technical reports available online regarding current developments in cybersecurity field related with artificial intelligence systems protection from adversaries’ infiltration attempts . Lastly our experimental designs are going assess effectiveness of different approaches adopted by analyzed security framworks according international standards like Common Vulnerability Scoring System(CVSS). All those activities mentioned above would provide us valuable feedback which ultimately helps us design more resilient cyber threat intelligence framework for protecting artificial intelligent models from adversary threats more effectively than ever before..

Sample Solution 

Cyber Threat Intelligence Framework to Increase the Robustness of Artificial Intelligence Models Against Adversarial Attacks is a dissertation that aims to create an effective framework for cyber threat intelligence (CTI) and its application in defending machine learning models from malicious attacks. The research will evaluate current security frameworks such as Machine Learning Security Scorecard (MLSS), Deep Learning Intrusion Detection System (DLIDS), and Automated Networked Defense (AND) to investigate their capability of providing adequate defense against adversarial attack. For this purpose, open-source datasets on adversarial attack, such as the Adversarial ML Testbed dataset and NIST Cybersecurity Framework dataset, will be used.