To investigate side-channel vulnerabilities in the microarchitecture of multicore processors and develop effective protection strategies, we analyze the primitives of the transient attack known as Meltdown. Our study reveals that the relative window of the exception handler significantly impacts the attack's effectiveness. Focusing on the Intel Skylake architecture, we conduct a comparative analysis between the memory order buffer (MOB), which regulates the execution order of load and store instructions, and the multi-level cache, which enhances data access efficiency. Our findings indicate that threads sharing resources within the core can exploit load instructions to directly access data stored by other users in the store buffer. Additionally, the data reside in cache, influenced by the Least Recently Used (LRU) policy, is particularly vulnerable to data leakage through side-channel attacks. By leveraging transient data from the store buffer and cache, the relative window of the exception handler can be expanded, we demonstrate that incorporating Meltdown can facilitate a transient attack that successfully retrieves the complete communication key of OpenSSH AES.

With the continuous development of network technology, the diversity of network traffic constantly increased (intra-class diversity). Nevertheless, the boundary between malicious and benign actions became even ambiguous (inter-class similarity), causing lots of false detection and hindering the further optimization of the detection model. Focusing on challenges brought by intra-class diversity and inter-class similarity, we proposed a novel approach to enhance intrusion detection based on contrastive learning, which can make the right decision while disentangling samples from different classes. First, to bridge the gap when applying contrastive learning to intrusion detection data, we proposed a heuristic method to build contrastive tasks based on random masking of network packet sequences, which can reflect semantic relationships among samples. Then contrastive loss can be calculated to measure the inter-class and intra-class distances. Second, contrastive cross-entropy loss was proposed, which was a combination of contrastive loss and classification loss. Together with a dual branch deep structure, we can optimize the detection and sample distance requirements at the same time. Thirdly, experiments were conducted on diverse real-world and benchmark datasets using different model architectures under various parameter settings. Results on real-world dataset showed that our methods could stable experience a 5% increase in accuracy, and an 8% improvement in detection rate on those easily misdetected scenarios. To verify the method’s effectiveness on different traffic representations, we further conducted experiments on NSL-KDD and UNSW-NB15, which achieved a 7% accuracy improvement on NSL-KDD and a 6% accuracy improvement on UNSW-NB15. Extensive comparison with state of art intrusion detection models in recent five years showed that the proposed methods could effectively improve the accuracy of detection models.

Internet of Things (IoT) applications have been used in a wide variety of domains ranging from smart home, healthcare, smart energy, and Industrial 4.0. While IoT brings a number of benefits including convenience and efficiency, it also introduces a number of emerging threats. The number of IoT devices that may be connected, along with the ad hoc nature of such systems, often exacerbates the situation. Security and privacy have emerged as significant challenges for managing IoT. Recent work has demonstrated that deep learning algorithms are very efficient for conducting security analysis of IoT systems and have many advantages compared with the other methods. This paper aims to provide a thorough survey related to deep learning applications in IoT for security and privacy concerns. Our primary focus is on deep learning enhanced IoT security. First, from the view of system architecture and the methodologies used, we investigate applications of deep learning in IoT security. Second, from the security perspective of IoT systems, we analyse the suitability of deep learning to improve security. Finally, we evaluate the performance of deep learning in IoT system security.