Introduction
As modern society becomes increasingly reliant upon digital infrastructure, the frequency and complexity of malicious online activities intensify. Traditional approaches to combatting cybercrime struggle under the weight of rapidly evolving tactics employed by nefarious actors in today's interconnected world. Amidst this dynamic landscape emerges a promising new frontier – leveraging artificial intelligence (AI) and machine learning (ML) technologies to revolutionize cyber defense mechanisms within cloud computing environments.
This groundbreaking study spearheaded by researchers including Mohammed Ashfaeq, M. Farzan, Mohamed Chahine Ghanem, Ayman El-Hajjar, Deepthi Ratnayake unveils a comprehensive vision centering around an advanced AI-empowered system designed specifically for swiftly detecting, responding, and mitigating cyber incidents plaguing the vast expanses of public clouds such as those offered by industry titans Amazon Web Services (AWS) and Microsoft Azure.
Proposed Framework: Integrated AI Solutions Against Online Threats
The proposed framework incorporates three core components intrinsically linked together to create a cohesively powerful cyber threat detection and eradication engine tailored explicitly for cloud-native architectures. They include:
1. **Network Traffic Classifiers**: Utilizing cutting-edge deep learning techniques, network traffic patterns associated with potential risks are identified instantaneously, ensuring prompt reaction times crucial in preventing widespread damage during active assaults.
2. **Web Intrusion Detection**: Employing innovative supervised ML algorithms, suspicious web behavior indicative of hostile intent gets flagged out in real time, effectively stymieing any attempts at exploiting vulnerabilities in web applications residing on cloud servers.
3. **Post-Incident Malware Analysers**: Post-breach investigatory analysis conducted via dual model applications further fortifies defenses by meticulously examining the aftermath of successful attacks, enabling more accurate identification methodologies towards future menaces.
Seamlessly Orchestrated Across Multiple Platforms Via Container Technology
One of the most compelling aspects of this novel approach lies in its versatility; the entirety of the integrated system runs effortlessly on diverse cloud service providers due to its implementation over containers. Containers ensure optimal performance coupled with exceptional adaptability catering to varying workloads dynamically without compromising quality assurance standards.
Enhancing Accuracies Through Advanced Modelling Techniques
Central to driving efficiencies throughout the process rests the astute selection of suitable modelling methods. Notably, the ensemble technique known as "Random Forest" showcases remarkable prowess when applied here, delivering unprecedented levels of precision. With reported accuracies reaching upwards of 90 percent for network traffic classification tasks, alongside a staggering 96 per cent success rate achieved in counteracting malwares, the powerhouse trio of AI subsystems shines brightest amidst the backdrop of ever-evolving adversary maneuvers.
Conclusion: Paving the Way Towards Robust Cloud Security Ecosystems
By harnessing the collective might of state-of-art AI and ML tools, this revolutionary initiative pushes boundaries in establishing a rock-solid foundation for organizations worldwide seeking reliable means to secure their mission critical data assets housed within the sprawling confines of popular public cloud offerings. As demonstrated by the impressive outcomes yielded by the experimental setup detailed above, the symbiotic relationship between AI, automation, big data analytics, and cloud services promises nothing short of a transformational leap forward in bolstering enterprise level cyber protection measures against the looming specter of persistent digital assaults.
References: For a complete understanding, refer to original arXiv publication mentioned in the introduction.
Source arXiv: http://arxiv.org/abs/2404.05602v2