As Autonomous Driving Systems (ADS) are increasingly deployed, ensuring their safety in edge cases becomes critical to preventing catastrophic failures. However, the limited ADS test scenario diversity often hinders the discovery of new defects, especially in complex and rare situations. This paper presents ScenarioFuzz- Llm,a novel method that leverages Large Language Models (LLMs) to enhance the diversity of ADS test scenarios. By incorporating LLMs into a genetic algorithm-based testing framework, ScenarioFuzz- Llmdirects the mutation to address diversity bottlenecks, thereby enabling the exploration of a broader range of edge cases. Our experiments demonstrate that ScenarioFuzz- Llmenhances the number of violation sce-narios by 10.51 % outperforming the state-of-the-art methods, and uncovers 24 unique defects in ADS, three of which are previously undiscovered. These results highlight the superiority of our approach in enhancing ADS testing through more diverse and comprehensive simulation scenarios, ultimately improving the safety of ADS.

Abnormal driving behavior is a leading cause of road accidents. Traditional detection methods, relying on classification or unsupervised learning, struggle with accuracy and generalization. Deep Reinforcement Learning (DRL) offers potential but faces challenges such as handling unlabeled and imbalanced data, designing effective reward functions, and ensuring efficient exploration. To address these, we propose an expert-guided DRL framework that integrates a CNN-BiLSTM-Self Attention (CBSA) model and an Isolation Forest (iForest) to guide Proximal Policy Optimization (PPO), enhancing detection accuracy and computational efficiency. Our framework consists of two stages. First, a deep learning model trained on labeled data provides expert guidance. Second, unlabeled data is processed through the pre-trained model and iForest, refining the DRL model via a sparse reward function to detect unknown anomalies while mitigating class imbalance and improving generalization. Experiments on an open-source dataset show our method out-performs baselines, achieving the highest recall (0.70) and Fl-score (0.63). Additionally, attention maps and anomaly heatmaps enhance interpretability, confirming its effectiveness for real-time abnormal driving behavior detection and improved driver safety.

The stateful nature inherent in network protocol implementations presents distinctive challenges for testing and verification methods, including Fuzzing. However, not all states hold equal significance. Indiscriminate state selection for fuzzing could lead to intricate path mazes. Similar challenges emerge in the selection for seeds and mutation operators. Therefore, overcoming the efficiency constraints of current fuzzers crucially depends on making precise selections in fuzzing. In this study, we present AcSelector, a novel approach that incorporates the Composite State Model and Mutation Operator Value Table. By offering the most strategic combination of {state, seed, mutation operator}, AcSelector provides systematic guidance for fuzzing, leading to enhanced code coverage. To quantify value of targets, AcSelector employs a principled evaluation strategy. We evaluated AcSelector by fuzzing six network servers from popular open-source projects. Our experimental results demonstrate the effectiveness of AcSelector in increasing code coverage, even under low fuzzing throughput conditions.

Popular topic detection is a topic identification by the information of documents posted by users in social networking platforms. In a large body of research literature, most popular topic detection methods identify the distribution of unknown topics by integrating information from documents based on social networking platforms. However, among these popular topic detection methods, most of them have a low accuracy in topic detection due to the short text content and the abundance of useless punctuation marks and emoticons. Image information in short texts has also been overlooked, while this information may contain the real topic matter of the user's posted content. In order to solve the above problems and improve the quality of topic detection, this paper proposes a popular topic detection method based on microblog images and short text information. The method uses an image description model to obtain more information about short texts, identifies hot words by a new word discovery algorithm in the preprocessing stage, and uses a PTM model to improve the quality and effectiveness of topic detection during topic detection and aggregation. The experimental results show that the topic detection method in this paper improves the values of evaluation indicators compared with the other three topic detection methods. In conclusion, the popular topic detection method proposed in this paper can improve the performance of topic detection by integrating microblog images and short text information, and outperforms other topic detection methods selected in this paper.

Federated Learning (FL) is a promising machine learning paradigm for collaborative training on cross-soils in a privacy-protected manner. However, the existence of non-IID data causes problems such as performance degradation and thus becomes one of the key challenges in FL recently. To address this problem, we propose a clustered personalized federated learning method named as SynCPFL. SynCPFL groups clients sharing with the similar data distribution together, thereby facilitating collaboration and producing a better-personalized model for each client. In contrast to existing clustered federated learning methods, SynCPFL does not require multiple rounds of interaction between clients and server, so that the communication overhead is reduced a lot, thereby saving resources of clients. We evaluate SynCPFL on benchmark datasets, the experimental results demonstrate that SynCPFL outperforms existing methods.

This paper proposes an algorithm-specific instruction (ASI)-based fast Fourier transform (FFT) code generation framework, named FFTASI, to generate unified architecture independent butterfly kernels that can be transformed into architecture-dependent kernels by establishing the mapping between ASIs and architecture-specific instructions for various hardware platforms. FFTASI strikes a good balance between performance and productivity on CPUs.

Collaborative cloud–edge Power Internet of Things technology is required to support the development of smart grids, which have become intelligent, green, and regionally autonomous systems. The diversity of electricity customer behaviors and different computational intensities of energy management applications present challenges for task allocation among computing resources that belong to different agents. In this article, we propose a novel trilevel collaborative optimization model to comprehensively consider the relation among various agents, including users, edge nodes (ENs), a cloud center (CC), and a multiedge league (MEL). We first formulate a Stackelberg game between users and ENs modeled as the lower level and middle level. In addition, with the assistance of the CC, we propose a MEL cooperation scheme to analyze the collaborative task allocation problem among multiple edges, which is modeled as the upper level to maximize the social welfare of the multiedge system (MES) without damaging the interests of the various ENs. The proposed trilevel model is equivalent to a bilevel program, solved by the proposed collaborative dynamic task allocation (CDTA) algorithm. Numerical simulations are presented to verify the proposed scheme and the results show that this scheme is effective for task allocation among users, ENs, the cloud, and the MEL in a cloud-assisted MES.

This paper introduces a multiobjective fleet size and mix location-routing problem with time windows and designs a set of real-world benchmark instances. Then, two versions of multiobjective multiple neighborhood search algorithms based on decomposition and vector angle are developed for solving the problem. In the proposed algorithms, three different kinds of neighborhood search operators, including general local search, objective-specific local search, and large neighborhood search, are carefully designed and combined in a synergistic manner. The experimental results show the effectiveness of the proposed algorithms. Relationships between different objectives in this multiobjective problem are also discussed.

With the rapid growth of Linux-based IoT devices such as network cameras and routers, the security becomes a concern and many attacks utilize vulnerabilities to compromise the devices. It is crucial for researchers to find vulnerabilities in IoT systems before attackers. Fuzzing is an effective vulnerability discovery technique for traditional desktop programs, but could not be directly applied to Linux-based IoT programs due to the special execution environment requirement. In our paper, we propose an efficient greybox fuzzing scheme for Linux-based IoT programs which consist of two phases: binary static analysis and IoT program greybox fuzzing. The binary static analysis is to help generate useful inputs for efficient fuzzing. The IoT program greybox fuzzing is to reinforce the IoT firmware kernel greybox fuzzer to support IoT programs. We implement a prototype system and the evaluation results indicate that our system could automatically find vulnerabilities in real-world Linux-based IoT programs efficiently.

The security of Industrial Control Systems (ICS) is closely related to national security. With secret exploration and analysis of a target ICS, highly-skilled attackers can gain enough key knowledge about the system (e.g., the physical model of the system and the corresponding detection threshold), and then launch stealthy attacks by keeping the detection indicator under its threshold, thus bypasses existing intrusion detection mechanisms. However, we discover that all devices in industrial control systems consume energy at run time and the energy consumption varies according to different operation types and system states. Therefore, there exists relationships between control operation, system state and energy consumption of the device. Accordingly, we put forward a novel ICS intrusion detection approach based on multi-dimensional data fusion. This approach collects information about power consumption of physical devices, control operation and system state, and then identifies stealthy attacks by feeding the multi-dimensional information into a cascade detection algorithm. Experimental results verify that our approach has a better detection performance than other detection methods.

The Industrial Control System (ICS) refers to the national critical infrastructure, such as Energy and Water facility, which is significant for the national security. Sequence attack is a unique attack type in ICS, and many detection approaches have been proposed. A common and unrealistic hypothesis of these approaches is that they have gained the command sequences. In the real world, we can only obtain the observations from sensors. The single observation detection technique is a common approach to find anomalies by the observations. However, the highly skilled attacker can compromise some Programmable Logic Controllers (PLCs) in ICS and fake their sensor measurements. Under this circumstance, this detection approach becomes invalid and increases the false-negative rate. In this paper, we first analyze the sequence attack by their attack capability in ICS. Then we propose a State-Based Sequence Detection approach (SBSD). The SBSD uses the equipment's observation information, belonging to many PLCs, to create Hidden Markov Models (HMMs) for detecting the sequence attack. The experiment results in an ICS testbed have shown the effectiveness of SBSD.

The popularity of location-based services and the ubiquity of Internet of Things (IoT) devices have resulted in rich spatiotemporal data. These data enable researchers to study people's social relationship based on their co-occurrences and many inference models were proposed. However, there are still two challenges: How to distinguish co-occurrences between acquaintances and strangers? What kind of co-occurrence contributes to strong social strength? In this paper, we propose a mobility pattern based relationship inference model (MPRI) to address above challenges. We extract mobility patterns from spatiotemporal data and adopt them to characterize co-occurrences. A classification model is trained for social relationship inference. The experimental results on two real-world datasets demonstrate that the proposed MPRI model can properly differentiate co-occurrences by simultaneously considering spatial and temporal features. The comparison results also indicate that MPRI model significantly outperforms state-of-the-art social relationship inference models.

This paper proposes a machine-learning technique to detect vehicle anomalies from data captured by automatic number plate recognition (ANPR) system. The proposed anomaly detection technique is specially engineered to exploit both spatial and temporal features of vehicles captured by ANPR system, so as to accurately detect anomaly vehicles. We extensively evaluated the proposed technique using a two- month long dataset collected by a real world ANRP system, which has more than three hundred cameras deployed in a big city of China. The evaluation results show that our technique can effectively detect vehicle anomalies from the huge amount of data collected by the ANPR system. More importantly, our technique significantly outperforms existing schemes especially when the data collected by the ANRP system are noisy due to poor weather condition.

Automatic Number-Plate Recognition (ANPR) system is commonly deployed on the road networks and used in field of safety and security systems. It is interesting to figure out the purpose of each vehicles based on the huge amount of ANPR data captured daily by embedded ANPR cameras. In this paper, we propose spatial and temporal quantitative indicators of vehicle trace features and the extraction algorism. We applied data-mining techniques to analysis vehicle activity patterns from ANPR data, and proposed centroids based activity classification method. Evaluations show the capability and efficiency of the proposed approach.