With the development of information technology, the originally closed industrial control system (ICS) protocol has become more public. Existing protocol fuzzing techniques can be directly applied to public ICS protocols to discover their vulnerabilities. However, they do not consider how to obtain the complete ICS protocol message syntax and perform effective state-guided fuzzing based on this syntax. This causes a large number of generated test cases to fail the syntax checking of the ICS protocol program and are eventually discarded. In this paper, we propose CtrlFuzz, a control field aware protocol fuzzer for public ICS protocols. We find that most ICS protocol programs perform different processing based on the value of the control field when processing received messages. These messages with different control field values together constitute the complete ICS protocol message syntax. Based on the above observations, we propose an expert system-based extracting strategy for complete ICS protocol message syntax. Specifically, CtrlFuzz uses an expert system to identify and infer the control fields in ICS protocol messages. To narrow the scope of inference, CtrlFuzz uses static analysis techniques to extract specific values in the program as optional values of the control fields. Moreover, we propose a state-guided fuzzing strategy based on the inferred message syntax. Our evaluation shows that compared with the state-of-the-art protocol fuzzers AFLNET, BooFuzz, StateAFL and ChatAFL, CtrlFuzz improves branch coverage by 6.8%–10.9% and state coverage by 23.7%–147.4% on average within 24 hours. Moreover, CtrlFuzz has exposed a total of 4 unknown vulnerabilities, and all have been confirmed and fixed, with one assigned a CVE number.

We consider cyber security as one of the most significant technical challenges in current times. One of the main tasks is to detect anomalous patterns in the network streams as soon as they appear. In order to solve the above problem, previous propositions use statistical or machine learning-based methods to detect anomalous patterns in the network streams. However, these solutions incur significant low efficiency and precision due to the frequent recomputation of the results from scratch and unreasonable assumptions. In graph theory, dense subgraphs can be used to model the anomalous patterns if we abstract the network streams as a dynamic graph. This motivates us to explore dense subgraph discovery under the scenario where the network is updating. In this paper, we propose a graph-based framework, referred to as SAD, towards continuous dense subgraph discovery over network streams. In specific, we design an auxiliary data structure that is a concise representation of intermediate results, and its execution model allows a fast incremental maintenance strategy. In this way, we can detect anomalous patterns in the network streams in near real-time. Experiments demonstrate that SAD can not only get a higher accuracy of 90.2% but also faster than $11.4\times$ times compared to the state-of-the-art anomaly detection algorithms.

The application of implantable medical devices (IMDs), which solves the problems of geographical distance limitation and real‐time health monitoring that plague patients and doctors, has caused great repercussions in the medical community. Despite the great potential of wide application, it also brings some security and privacy issues, such as the leakage of health data and unauthorized access to IMDs. Although a number of authentication and key agreement (AKA) schemes have been developed, we find that some subtle attacks still remain to be addressed. Then we propose an improved AKA scheme which achieves strong security features including user anonymity and known key security. It is formally proved to be secure under the Real‐or‐Random model. Moreover, a comprehensive security analysis shows that our scheme can resist various attacks and satisfy the desired requirements. Finally, the performance analysis shows the superiority of our protocol which is suitable for the implantable medical system.