Enterprise AI Analysis
Efficient Feature Ranked Hybrid Framework for Android IoT Malware Detection
Android-based IoT devices are still exposed to increasing sophistication in malware; therefore, detecting this malware using lightweight and accurate approaches is very important. This paper presents a hybrid malware detection framework, incorporating static and dynamic analysis with a dual feature-ranking mechanism based on Information Gain and Gini Index, for selecting the most relevant features.
Executive Impact at a Glance
Our innovative framework delivers industry-leading performance critical for securing Android-IoT ecosystems.
0
Average Accuracy
0
Performance Gain over Baselines
0
Avg. Inference Latency (Drebin)
0
Validated Datasets
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Hybrid Analysis
Static Analysis
Dynamic Analysis
Feature Selection
Performance & Interpretability
Hybrid Analysis Advantage
This framework combines static and dynamic analysis techniques to detect and classify IoT malware more effectively. By integrating both, it provides a comprehensive view, enhancing detection accuracy and robustness, particularly against obfuscation and zero-day threats.
Static Analysis Efficiency
Static analysis examines applications without executing them, obtaining evidence based on permissions, intents, or API calls. It is lightweight and intuitive, making it useful in resource-constrained IoT environments, though it can be susceptible to obfuscation.
Dynamic Analysis Robustness
Dynamic analysis enables malware detection by executing applications in controlled environments to observe real-time behaviors like system calls, memory usage, and network activity. This approach is more resistant to code obfuscation and encryption, making it effective against sophisticated threats.
Dual Feature Ranking
A dual feature-ranking mechanism using Information Gain and Gini Index is employed to select the most relevant features. This approach optimizes dimensionality, retains crucial discriminative power, and enhances model interpretability and inference efficiency.
Optimized Performance & Interpretability
The Random Forest classifier provides a lightweight and transparent means of malware detection, performing well with high-dimensional hybrid features. Its feature-ranking capability enhances interpretability, allowing analysts to identify the strongest indicators of malicious behavior.
99.5%
Average Detection Accuracy Across Benchmark Datasets
Enterprise Process Flow
Data loading and setup
→
Data preprocessing
→
Feature selection
→
Train-Test split
→
Train random forest model
→
Evaluate model
→
Explainability and optimization
| Framework | Accuracy | F1-Score | Computational Cost | Key Advantages |
|---|---|---|---|---|
| MLDroid (2021) | 97.2% | 0.96 | High |
|
| DeepAMD (2021) | 96.1% | 0.95 | Very High (GPU) |
|
| Chybridroid (2020) | 94.8% | 0.92 | High |
|
| Our Proposed Framework (2025) | 99.03-100% | 0.98-1.00 | Low |
|
Case Study: Navigating Class Imbalance in IIoT Malware (CIMD-2024)
The CIMD-2024 industrial IoT malware dataset presented a unique challenge due to its extreme class imbalance, with benign traffic dominating around 70%. While the model achieved a stable overall accuracy of 69.82%, its macro-F1 score was very low (0.137), indicating a systematic failure to detect minority malware classes (Botnet, Ransomware, Spyware, Trojan, Worm). This highlights that for highly skewed datasets, relying solely on raw accuracy is misleading, and imbalance-handling strategies (e.g., resampling, class-weighting) are crucial for effective real-world threat detection in IoT environments.
Calculate Your Potential AI-Driven Savings
Estimate the significant operational efficiency gains and cost reductions your enterprise could achieve with AI integration.
Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Your AI Implementation Roadmap
A structured approach to integrate this advanced malware detection framework into your enterprise.
Phase 1: Data Ingestion & Preprocessing
Gathering, cleaning, and normalizing diverse Android malware datasets (static and dynamic features) to establish a clean, balanced, and ready-to-use input for model training.
Phase 2: Dual Feature Engineering
Applying Information Gain and Gini Index to rank and select the most discriminative features, significantly reducing dimensionality while preserving crucial predictive power for efficient model performance.
Phase 3: Model Training & Validation
Training the optimized Random Forest classifier using cross-validation on stratified data to ensure robust generalization and stable performance across various malware types, including unseen threats.
Phase 4: Performance Evaluation & Optimization
Rigorously assessing the model with accuracy, precision, recall, F1-score, and confusion matrix analysis, followed by hyperparameter tuning for optimal performance and resource efficiency.
Phase 5: Interpretability & Deployment Prep
Analyzing feature importance to provide actionable insights into malware behavior, and preparing the lightweight model for seamless integration into resource-constrained Android-IoT environments.
Ready to Secure Your IoT Ecosystem?
Book a complimentary strategy session to discuss how our AI solutions can safeguard your Android-IoT devices and infrastructure.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat