t cht lk conditioned on a large dataset , as a starting point for a new task . Instead of training a model from scratch , the knowledge and features learned from the pre-trained model are transferred , allowing for quicker and more accurate learning on the new task . Transfer learning is beneficial in situations with limited data and reduces the computational resources required for training .
t cht lk conditioned on a large dataset , as a starting point for a new task . Instead of training a model from scratch , the knowledge and features learned from the pre-trained model are transferred , allowing for quicker and more accurate learning on the new task . Transfer learning is beneficial in situations with limited data and reduces the computational resources required for training .
Meanwhile , online learning is an approach where a model is continuously updated as new data arrives . This method is particularly useful in dynamic environments , where the distribution of data or the target task may change over time , such as recommendation systems , fraud detection and adaptive control systems . However , mutual collaboration has once again put criminals ahead .
Today , there are AI-based cyberattack tools like WormGPT , capable of learning complex behavior patterns from a database to create and disseminate malicious content . Paying attention to this asymmetry between the evolution of tools used for attack and defense can make a difference in a cybersecurity strategy .
As network behavior data is collected , a comprehensive understanding of various attack patterns and trends accumulates . This shared knowledge and information acquired through Collective Defense improve the effectiveness and precision of the models . Furthermore , as these models are adjusted using online learning techniques , they continuously refine their ability to identify emerging threats and provide proactive cybersecurity responses .
Germán Patiño , Vice President of Sales for Latin America at Lumu Technologies
In summary , adopting Collective Defense empowers organizations to transcend individual limitations and build a united front against cyberthreats , ensuring an agile response to cyber incidents through real-time sharing , with automation and analysis accelerating detection , containment and mitigation . Data sharing also strengthens resilience against threats , anticipating security breaches through analysis of network traffic , user behavior and system deviations . Additionally , such an approach leads to cost efficiency , allowing joint investments in advanced AI-based defense solutions and effective allocation of cybersecurity budgets .
By exchanging data and information , it is possible to develop more complex and resilient Machine Learning models .
It ’ s time for organizations to take a proactive stance in order to propose and intensify their collaborative efforts . By exploring all the possibilities offered by new technologies for creating more robust defense strategies , cybersecurity can evolve at a faster and constant rate , always staying one step ahead of the mechanisms used by criminals . p
www . intelligentcio . com INTELLIGENTCIO LATAM 65