A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (1807.11023v1)

Abstract: The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT system effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML /DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

The paper "Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security" by Al-Garadi et al. provides an exhaustive review of the contemporary ML and deep learning (DL) techniques applied to resolve security challenges in IoT systems. This summary aims to present an expert evaluation of the methodologies, results, and potential research directions discussed.

Overview of IoT Systems and Security Challenges

IoT systems are rapidly evolving, integrating billions of smart devices capable of sensing, data collection, and autonomous communication. With an aim to enhance various applications such as smart healthcare, transportation, home automation, and more, the IoT landscape introduces complex multidimensional security challenges. Basic security measures including encryption, authentication, access control, and network security are increasingly ineffective when faced with the massive scale and diverse attack surfaces of IoT environments. The authors highlight the necessity for advanced security methods that integrate ML and DL to effectively safeguard IoT ecosystems amidst pervasive threats and vulnerabilities.

Machine Learning Methods for IoT Security

Machine learning algorithms offer valuable capabilities for anomaly detection and pattern recognition within the IoT systems. Specific ML methods include:

1. Decision Trees (DTs): The paper emphasizes DTs' applicability to classify system behaviors but points out their inefficiency in large-scale, complex applications due to high computational and storage requirements.
2. Support Vector Machines (SVMs): Known for their generalization capabilities and suitability for datasets with many feature attributes but few samples, SVMs manifest robustness in intrusion and malware detection.
3. Bayesian Networks: Primarily used for problems with probabilistic characteristics, these methods are highlighted for their simplicity and robustness but are found limited in handling complex feature interactions.
4. K-Nearest Neighbors (KNN): Effective for large datasets; however, determining the optimal 'k' value remains a challenge. KNN’s applications include effective intrusion and anomaly detection in various scenarios.
5. Random Forests (RF): Combining multiple DTs, RFs reduce overfitting and bypass feature selection, outperforming other methods in certain IoT security applications. However, they require large training datasets which can be a limitation in real-time applications.
6. Association Rule (AR) Algorithms and Ensemble Learning (EL): While AR algorithms show promise for intrusion detection, EL combines multiple classifiers to enhance performance but also increases computational complexity.

Deep Learning Methods for IoT Security

Deep learning methods, differentiated by their multilayered structure and capacity for high-dimensional data abstraction, present superior performance in IoT systems with vast data streams. Significant DL methods discussed include:

1. Convolutional Neural Networks (CNNs): Efficient at learning features from raw data, CNNs excel in image classification-related security tasks but face challenges due to high computational costs.
2. Recurrent Neural Networks (RNNs): Ideal for sequential data, RNNs, and their variants (e.g., LSTM), offer robust solutions for time-dependent threat detection but suffer from gradient issues.
3. Deep Autoencoders (AEs): These excel in feature extraction and dimensionality reduction, although they require significant computational time.
4. Restricted Boltzmann Machines (RBMs) and Deep Belief Networks (DBNs): Effective for unsupervised learning and anomaly detection, but high computational complexity limits their on-board implementation.
5. Generative Adversarial Networks (GANs): Display potential in generating samples for zero-day attack mitigation but present stability challenges during the training process.
6. Ensemble of Deep Learning Networks (EDLNs): While effective, they increase system complexity and training time, necessitating further exploration for real-world applicability.

Application of ML/DL in IoT Security Layers

The paper categorizes ML/DL applications as follows:

1. Perception Layer: Techniques such as extreme learning machines and DL models for user authentication, leveraging IoT-derived data like Wi-Fi signals, have shown significant promise.
2. Network Layer: Here, DL algorithms like CNNs and RNNs contribute substantially to network traffic analysis and intrusion detection systems, improving responsiveness to dynamic threats.
3. Application Layer: Usage of DL for malware detection on platforms like Android emphasizes the need for feature learning, which outstrips traditional manual feature engineering methods.

Challenges and Future Directions

The authors delineate several challenges and propose future research directions:

1. Data Availability and Quality: Establishing high-quality, comprehensive security datasets remain a challenge. Crowd-sourcing and augmentation techniques might mitigate data insufficiency issues.
2. Real-time Threat Detection: Developing efficient, real-time detection methods is essential, especially considering the limited computational resources of many IoT devices.
3. Transfer and Lifelong Learning: Leveraging knowledge from adequately trained models to new scenarios (transfer learning) and continuous learning from evolving data (lifelong learning) are critical to maintain up-to-date security models.
4. Integration with Blockchain and Edge Computing: Integrating blockchain technology for decentralized security and deploying ML/DL on edge devices to reduce latency and enhance real-time security measures form promising research avenues.
5. Computational Complexity vs. Trade-offs: Striking a balance between high-security levels and system constraints like availability and energy efficiency is paramount for deploying ML/DL in practical IoT applications.

Conclusion

The survey performs a meticulous analysis of how ML/DL methodologies transform IoT security, enhancing the detection, response, and adaptation capabilities of systems facing numerous, evolving threats. The exploration of current achievements, challenges, and potential research avenues provides invaluable insights into developing more secure, intelligent IoT systems in the future.