Hybrid Intrusion Detection System (H-IDS) for Mobile Cloud Computing: A Resource-Aware Multi-Layered Approach

Hybrid Intrusion Detection System (H-IDS) Mobile Device Layer (Lightweight Tasks) Use resource-efficient systems like Snort, OSSEC, and TShark. Focus on quick anomaly detection, monitoring resource metrics, and signature-based analysis. Cloud-Assisted Layer (Heavyweight Tasks) Deploy systems like Suricata, Zeek, or Apache Metron. Focus on machine learning-based analysis, deep packet inspection, and threat correlation. The Hybrid Intrusion Detection System (H-IDS) leverages a combination of the features and functionalities of the mentioned IDSs to create a highly efficient and accurate system for a multi-layered architecture. Mobile Device Layer (Lightweight IDS) Detect simple intrusions and anomalies quickly using minimal resources. Perform pre-filtering of data and forward complex cases to the cloud layer. Core Components Snort for signature-based detection of known threats, such as malware and DDoS attacks. OSSEC for monitoring file integrity, log inspection, and rapid anomaly detection. TShark for lightweight real-time network traffic analysis to detect basic anomalies. Lightweight Machine Learning Algorithm (Decision Trees) to classify threats and determine whether to escalate to the cloud. Cloud-Assisted Layer (Heavyweight IDS) Conduct computationally intensive analysis. Correlate data from multiple devices and sources to detect complex or coordinated threats. Core Components Suricata for advanced signature and protocol-based detection. Zeek for behavioral and traffic analysis by extracting metadata from data packets. Apache Metron for big data analytics and anomaly detection using machine learning Federated Learning Module for privacy-preserving training on aggregated data from multiple devices. Cloud DLP (Data Loss Prevention) for monitoring sensitive data exfiltration attempts. Dynamic Collaboration Between Layers Step 1: Pre-Processing and Escalation at the Mobile Device Layer Use lightweight ML classifiers to determine whether an anomaly or suspicious traffic requires cloud-level processing. Step 2: Real-Time Data Exchange Secure data transmission using TLS 1.3 to send relevant packets or anomalies to cloud. Step 3: Deep Analysis and Learning at Cloud Layer Integrate multiple IDS (Suricata, Zeek, Apache Metron) for comprehensive detection of large-scale attacks like Advanced Persistent Threats (APTs). Step 4: Automated Feedback Loop Cloud-trained models e.g. ML classifiers or attack signatures are periodically pushed back to mobile devices for localized detection enabling the mobile layer to improve accuracy over time and adapt to evolving threats. from the above details help me write a research paper with result set taking some dataset