Biblio

Malicious login, especially lateral movement, has been a primary and costly threat for enterprises. However, there exist two critical challenges in the existing methods. Specifically, they heavily rely on a limited number of predefined rules and features. When the attack patterns change, security experts must manually design new ones. Besides, they cannot explore the attributes' mutual effect specific to login operations. We propose MLTracer, a graph neural network (GNN) based system for detecting such attacks. It has two core components to tackle the previous challenges. First, MLTracer adopts a novel method to differentiate crucial attributes of login operations from the rest without experts' designated features. Second, MLTracer leverages a GNN model to detect malicious logins. The model involves a convolutional neural network (CNN) to explore attributes of login operations, and a co-attention mechanism to mutually improve the representations (vectors) of login attributes through learning their login-specific relation. We implement an evaluation of such an approach. The results demonstrate that MLTracer significantly outperforms state-of-the-art methods. Moreover, MLTracer effectively detects various attack scenarios with a remarkably low false positive rate (FPR).

In the process of mobile intelligent terminal for file transfer, ensure the safety of data transmission is significant. It is necessary to prevent the file from being eavesdropped and tampered during transmission. The method of using double encryption on covert channel is proposed in this paper based on the analysis of encryption algorithms and covert channel, which uses asymmetric encryption algorithm to encrypt the key of symmetric encryption, to form hidden information, and to carry out covert transmission through covert channels to enhance the security of mobile terminal data transmission. By simulating the above scenarios in intelligent mobile terminal, the confidentiality and concealment of important information are realized in the transmission process.

Along with the rapid development of hardware security techniques, the revolutionary growth of countermeasures or attacking methods developed by intelligent and adaptive adversaries have significantly complicated the ability to create secure hardware systems. Thus, there is a critical need to (re)evaluate existing or new hardware security techniques against these state-of-the-art attacking methods. With this in mind, this paper presents a novel framework for incorporating active learning techniques into hardware security field. We demonstrate that active learning can significantly improve the learning efficiency of physical unclonable function (PUF) modeling attack, which samples the least confident and the most informative challenge-response pair (CRP) for training in each iteration. For example, our experimental results show that in order to obtain a prediction error below 4%, 2790 CRPs are required in passive learning, while only 811 CRPs are required in active learning. The sampling strategies and detailed applications of PUF modeling attack under various environmental conditions are also discussed. When the environment is very noisy, active learning may sample a large number of mislabeled CRPs and hence result in high prediction error. We present two methods to mitigate the contradiction between informative and noisy CRPs.

Secure logging as an indispensable part of any secure system in practice is well-understood by both academia and industry. However, providing security for audit logs on an untrusted machine in a large distributed system is still a challenging task. The emergence and wide availability of log management tools prompted plenty of work in the security community that allows clients or auditors to verify integrity of the log data. Most recent solutions to this problem focus on the space-efficiency or public verifiability of forward security. Unfortunately, existing secure audit logging schemes have significant performance limitations that make them impractical for realtime large-scale distributed applications: Existing cryptographic hashing is computationally expensive for logging in task intensive or resource-constrained systems especially to prove individual log events, while Merkle-tree approach has fundamental limitations when face with highly concurrent, large-scale log streams due to its serially appending feature. The verification step of Merkle-tree based approach requiring a logarithmic number of hash computations is becoming a bottleneck to improve the overall performance. There is a huge gap between the flux of log streams collected and the computational efficiency of integrity verification in the large-scale distributed systems. In this work, we develop a novel scheme, performance of which favorably compares with the existing solutions. The performance guarantees that we achieve stem from a novel data structure called concurrent authenticated tree, which allows log events concurrently appending and removes the need to wait for append operations to complete sequentially. We implement a prototype using chameleon hashing based on discrete log and Merkle history tree. A comprehensive experimental evaluation of the proposed and existing approaches is used to validate the analytical models and verify our claims. The results demonstrate that our proposed scheme verifying in a concurrent way is significantly more efficient than the previous tree-based approach.