MFAAMDTL: An Efficient Multimodal Feature Analysis Model to Mitigation Cloud Attacks using Transfer Learning Operations

Abstract

The persistent issue is that cloud applications fortified post-deployment with security patches remain susceptible to sophisticated attack vectors. In response to this, the discourse introduces an innovative, lightweight header layer designed to preemptively filter incoming requests prior to their processing by Cloud Virtual Machines (CVMs). Leveraging a combination of instantaneous and temporal analytics, this layer is adept at the early detection and neutralization of a broad spectrum of both active and passive cybersecurity threats, significantly bolstering the resilience of cloud deployments against malicious endeavors. To operationalize this defense mechanism, the system deploys an advanced logging framework capable of high-velocity data capture, triggered by an array of header-level events such as authentication attempts, access requests, and the temporal intervals between successive requests. This granular data collection strategy equips the system with a comprehensive dataset, derived from continuous user interactions, which is subsequently subjected to an intricate post-processing regimen aimed at the extraction of multimodal features. This process involves the manual tagging of request-response pairs by a curated group of users, facilitating the identification of diverse threat signatures such as temporal attack probabilities, IP-based attack typologies, user access patterns, and anomalies in request-response dynamics. At the heart of this model lies a sophisticated deep transfer learning framework, integrating the nuanced capabilities of Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU)-based Recurrent Neural Networks (RNNs), trained on an extensive corpus of user-generated data. This hybrid RNN methodology enables the model to discern and classify a wide array of attack vectors with remarkable accuracy. An incremental learning module further refines the model's efficacy, enabling dynamic adaptation and continuous improvement in its predictive accuracy, precision, and recall metrics across various attack scenarios, including but not limited to Distributed Denial of Service (DDoS), brute force, cross-site scripting, SQL injection, as well as more passive threats like access control breaches and restricted ownership transfer attempts. Empirical evaluations of this model underscore its superior performance, achieving notable accuracy rates in detecting authentication attacks (99.3%), unauthorized access attempts (97.1%), DDoS and similar request-pattern aberrations (99.1%), and Man in the Middle (MITM) attacks (99.2%). When benchmarked against contemporary models, this innovative approach demonstrated a performance uplift of 6.5%, underscoring its viability for real-time deployment and scalability across diverse cloud networking scenarios.