Intrusion detection systems are a crucial technique for securing the Internet of Things (IoT) from malicious attacks. Additionally, due to the continuous emergence of new vulnerabilities and unknown attack types, only a small number of attack samples in the IoT environments can be captured for analysis. In this work, we introduce an anchor graph clustering (AGC) method for intrusion detection to address the challenge of limited labeled samples in the IoT environments. AGC initially transforms the raw data into the embedding space to obtain more representative anchors. Then, AGC unifies anchor graph construction, anchor graph learning, and graph clustering into a unified framework, solving the resulting optimization problem through an iterative solution algorithm. Finally, AGC leverages the powerful analytical capabilities of graph learning to achieve fine-grained classification of low-quality labels. Experimental results on both real and synthetic datasets confirm that AGC can identify intrusions with high precision, while also being time-efficient in detection.

Unmanned Aerial Vehicles (UAVs) are vulnerable to network attacks. Designing an effective intrusion detection system (IDS) for UAVs is crucial. However, UAVs have limited computing resources and need to deal with massive amounts of network data, which further increases the difficulty of detection. Moreover, most existing IDSs have large parameters. In this study, we develop a tiny machine learning-based IDS to solve the above issue. We first establish an improved fuzzy rough set (FRS) model based on adaptive neighborhoods. Then, using the proposed FRS model, we employ a feature selection (FS) method to select optimal features and reduce overall computational cost of the IDS. Furthermore, we proposed a tiny intrusion detection model that attains high-precision detection via shallow deep learning. Additionally, the proposed method can address intrusion detection problems in scenarios with partial data missing. According to the evaluations, the proposed method can effectively address intrusion detection in UAVs.

Software vulnerability detection is a critical aspect of ensuring the security and reliability of software systems. However, traditional vulnerability detection approaches often have limitations due to the scarcity and need for more diversity in labeled data. This research introduces a novel approach to overcome these challenges by utilizing user-defined identifiers in the source code to generate counterfactual training data. User-defined identffiers, such as variable and function names, contain essential information about the intentions and logic of the program. By perturbing these identifiers while maintaining the syntactic and semantic structure of the code, we create a diverse set of counterfactual examples that simulate potential vulnerabilities. When combined with existing labeled data, these counterfactual examples enrich the training process for vulnerability detection models. To evaluate the effectiveness of our approach, we conduct experiments on various datasets, achieving state-of-the-art performance on the VulDeePecker and Draper datasets. Our approach also outperforms models that utilize the same pre-trained language model in terms of accuracy.

Numerous interaction demands for smart cities have arisen due to the explosive growth of the Internet of Things and mobile communication. Due to its passive low energy consumption and affordable deployment cost, reconfigurable intelligent surface (RIS) is a potential key technology to develop a novel paradigm of wireless communication at unsignalized intersections. This article studies the signal interaction between multiple vehicle platoons in different paths before entering an unsignalized intersection supported by unmanned aerial vehicle, which is usually difficult to solve without the assistance of communication. Under the limitation of calculation and transmission resources, the objective is to optimize the system’s transmission efficiency. The joint resource scheduling model of communication time and transmission power includes the coupling effects of vehicle dynamics, onboard computing, signal transmission and reflection, and energy consumption. A method based on the quasi-Newton and sequential quadratic programming (SQP) algorithm is designed to carry out optimization iteration. Finally, the simulation results show that this method is generally efficient and outperforms the benchmark methods.

A prominent security threat to unmanned aerial vehicle (UAV) is to capture it by GPS spoofing, in which the attacker manipulates the GPS signal of the UAV to capture it. This paper introduces an anti‐spoofing model to mitigate the impact of GPS spoofing attack on UAV mission security. In this model, linear regression (LR) is used to predict and model the optimal route of UAV to its destination. On this basis, a countermeasure mechanism is proposed to reduce the impact of GPS spoofing attack. Confrontation is based on the progressive detection mechanism of the model. In order to better ensure the flight security of UAV, the model provides more than one detection scheme for spoofing signal to improve the sensitivity of UAV to deception signal detection. For better proving the proposed LR anti‐spoofing model, a dynamic Stackelberg game is formulated to simulate the interaction between GPS spoofer and UAV. In particular, for GPS spoofer, it is worth mentioning that for the scenario that the UAV is cheated by GPS spoofing signal in the mission environment of the designated route is simulated in the experiment. In particular, UAV with the LR anti‐spoofing model, as the leader in this game, dynamically adjusts its response strategy according to the deception’s attack strategy when upon detection of GPS spoofer’s attack. The simulation results show that the method can effectively enhance the ability of UAV to resist GPS spoofing without increasing the hardware cost of the UAV and is easy to implement. Furthermore, we also try to use long short‐term memory (LSTM) network in the trajectory prediction module of the model. The experimental results show that the LR anti‐spoofing model proposed is far better than that of LSTM in terms of prediction accuracy.

This paper focused on the safety verification of the multithreaded programs for mobile crowdsourcing networks. A novel algorithm was proposed to find a way to apply IC3, which is typically the fastest algorithm for SAT‐based finite state model checking, in a very clever manner to solve the safety problem of multithreaded programs. By computing a series of overapproximation reachability, the safety properties can be verified by the SAT‐based model checking algorithms. The results show that the new algorithm outperforms all the recently published works, especially on memory consumption (an advantage that comes from IC3).

Scalability of cache coherence protocol is a key component in future shared-memory multi-core or multi-processor systems. The state space explosion is the first hurdle while applying model-checking to scalable protocols. In order to validate parameterized cache coherence protocols effectively, we present a new method of reducing the state space of parameterized systems, two-dimensional abstraction (TDA). Drawing inspiration from the design principle of parameterized systems, an abstract model of an unbounded system is constructed out of finite states. The mathematical principles underlying TDA is presented. Theoretical reasoning demonstrates that TDA is correct and sound. An example of parameterized cache coherence protocol based on MESI illustrates how to produce a much smaller abstract model by TDA. We also demonstrate the power of our method by applying it to various well-known classes of protocols. During the development of TH-1A supercomputer system, TDA was used to verify the coherence protocol in FT-1000 CPU and showed the potential advantages in reducing the verification complexity.