The intersection of artificial intelligence and cybersecurity brings forth innovative approaches to tackle evolving threats in digital environments. As serverless computing environments gain traction, specific challenges like the denial of wallet (DoW) attacks necessitate novel solutions. The development of advanced detection methodologies becomes crucial amidst the ever-changing threat landscape.

The present research introduces an innovative technique termed MHARNN-DoWAD crafted meticulously to identify DoW attacks in serverless ecosystems. This approach encompasses steps like data preprocessing, feature selection, attack detection, and parameter tuning, each playing a vital role in enhancing the detection process.

Data Preprocessing and Feature Selection

The MHARNN-DoWAD model initiates with structured data preprocessing through min-max normalization, transforming raw inputs into a consistent form. By standardizing data to fixed intervals, the technique enhances anomaly recognition, focusing on resource usage irregularities indicative of potential threats. This crucial step ensures that varying metrics do not obscure recognition processes, thereby boosting detection accuracy.

Following this is the WPP method, a feature selection strategy inspired by the social hierarchy observed in wolf packs. This method models the behavior seen in nature, where different ranks within a wolf pack contribute uniquely to decision-making and exploration processes. Here, wolves represent candidate solutions, with the top ranks signifying optimal outcomes. This metaphorical approach leverages cooperative behavior to solve complex optimization challenges effectively.

Detection and Classification Mechanism

Once features are selected, the MHA-BiGRU model steps in to manage the detection and classification of DoW attacks. An advancement over traditional Long Short Term Memory models (LSTM), the Gated Recurrent Unit (GRU) excels in capturing long-term dependencies without the computational overhead. The BiGRU variant further enhances prediction accuracy by utilizing both past and future data, thereby addressing the limitations observed in GRU models.

The integration of a sophisticated Attention Mechanism aids in the efficient distribution of data, emphasizing critical tasks while mitigating overload. By employing value, key, and query vectors, the mechanism calculates attention scores that prioritize crucial data points, thereby bolstering the detection efficiency.

The Multi-Head Attention (MHA) technique, derived from the Transformer framework, enriches the model by mapping vectors across varied dimensions and computing the attention. This process enables a comprehensive extraction of temporal features, crucial for accurate sequence prediction and model training.

Parameter Optimization

Enhancing the efficacy of the MHA-BiGRU model, the ISBOA-based (Improved Secretary Bird Optimization Algorithm) parameter tuning plays a pivotal role. Drawing inspiration from nature’s predator-prey dynamics, ISBOA optimizes complex problems by simulating the exploration and evasion strategies of secretary birds. The multi-phase strategy includes global search tactics, fine local searches, and escape maneuvers to refine the model’s hyperparameters.

The integration of the good point-set technique within ISBOA enhances population initialization, crucial for optimizing search capabilities in stochastic methods. By improving spatial distribution, the technique mitigates typical initialization biases, leading to enhanced model performance.

Performance Analysis

To validate the MHARNN-DoWAD model, extensive tests were conducted on a dataset comprising 100,000 instances divided into multiple classes. Utilizing 18 features, of which 11 key features were highlighted, the model’s performance was rigorously examined. The evaluation parameters included metrics like precision, recall, F-score, and Matthews correlation coefficient, exhibiting remarkable results. Indeed, the MHARNN-DoWAD consistently outperformed contemporary models in various configurations, including Logistic Regression, ResNet50-Softmax, and LSTM.

The model demonstrated robust training and validation accuracies, underpinning its reliability and effectiveness in unseen scenarios. Moreover, the precision-recall analysis affirmed the model’s adeptness at sustaining high performance across different classes, portraying its robust classification abilities.

Finally, the model’s computational efficiency was highlighted, showing significant performance gains over traditional methodologies. Despite being computationally demanding, the MHARNN-DoWAD model achieved the lowest computational time, underscoring its suitability for real-time applications where speed is of essence.

The research underscores the promise of leveraging advanced AI techniques for cybersecurity solutions, particularly in serverless computing environments where resource constraints and scalability are paramount. The MHARNN-DoWAD methodology sets a new benchmark in proactive threat detection, illustrating the potential of heuristic-driven neural networks to evolve alongside emerging cyber threats.

To summarize, as serverless technologies continue to reshape digital interactions, adapting and advancing our defensive strategies remain essential. The integration of methodologies such as MHARNN-DoWAD heralds a new era of intelligent security solutions capable of adapting to future challenges.