Time Series Management System (TSMS) is a specialized database management system designed for storing, querying, and analyzing time series data. Its correctness is essential for accurate data processing. However, logic bugs can lead to erroneous query outputs, severely compromising the reliability of data analysis. Compared with traditional relational database SQL, time series SQL exhibits significant syntactic and semantic differences, making existing tools inapplicable. To the best of our knowledge, the detection of logic bugs remains an open problem. In this paper, we propose TSGuard, a tool for detecting logic bugs in TSMSs via time series algebra. The core idea of TSGuard is to convert time series SQL queries into equivalent time series algebra expressions, evaluate these expressions to derive the expected result set, and then compare it with the actual query result set to detect potential logic bugs in the TSMS. Additionally, we introduce a feedback mechanism for query generation and develop query syntax validators for different TSMSs to improve the efficiency of logic bug detection. Through extensive testing, TSGuard discovered 48 previously unknown bugs, including 45 logic bugs and 3 crash bugs.

The efficiency of informed path planning algorithms is contingent upon how quickly the planner can find the initial solution and the associated overhead involved in collision detection. Existing informed planners do not fully exploit the information contained in historical collision detection results, resulting in additional unnecessary collision detections. Furthermore, they optimize paths through rewiring before discovering an initial solution, which not only hampers the planner’s space exploration, but also generates a superfluous amount of unproductive over-head. To address the shortcomings of existing algorithms, this paper proposes an Obstacle-Sensitive and Initial-Solution-first path planning algorithm (OSIS). OSIS uses historical collision detection results to predict the distribution of obstacles in space and utilizes an initial-solution-first path optimization strategy to avoid useless path optimization. Experiments show that OSIS can efficiently bypass obstacles and converge the cost of the solution compared to existing algorithms.

In recent years, Ethereum has become a hotspot for criminal activities such as phishing scams that seriously compromise Ethereum transaction security. However, existing methods cannot accurately model Ethereum transaction data and make full use of the temporal structure information and basic account features. In this paper, we propose an Ethereum phishing detection framework based on temporal motif features. By designing a sampling method, we convert labeled Ethereum addresses into multi-directed transaction subgraphs with time and amount to avoid losing structure and attribute information. To learn representations for subgraphs, we define and extract the temporal motif features and general transaction features. Extensive experiments on Support Vector Machine, Random Forest, Logistic Regression, and XGBoost demonstrate that our method significantly outperforms all baselines and provides an effective phishing scams detection for Ethereum.

HarmonyOS is an operating system boasting a substantial global user base and provides multiple versions of its SDK. Various open-source applications continue to utilize older versions, leading to compatibility issues arising from system constraints. These prevalent issues can substantially affect the user experience. Although numerous solutions have been suggested for addressing compatibility issues in Android, the subject remains largely unexplored within the context of HarmonyOS. To bridge this gap, we investigate the evolution of APIs in HarmonyOS to pinpoint those potentially causing compatibility issues. Based on these insights, we implement CiD4HMOS, a tool designed to detect and categorize compatibility issues in HarmonyOS. We evaluate the feasibility of CiD4HMOS with open-source apps and subsequently apply it to commercially released apps, highlighting its effectiveness in accurately identifying HarmonyOS compatibility issues. The experimental results uncover that CiD4HMOS is effective in detecting compatibility issues in HarmonyOS apps, achieving an accuracy rate of 86.8 % in open-source apps. And, developers of commercially released apps have significantly endorsed our reports. Our research emphasizes the necessity of continuous exploration into compatibility issues within HarmonyOS, underlining the significant role tools like CiD4HMOS play in enhancing the overall user experience.

With the widespread of cyber attacks, network intrusion detection system (NIDS) is becoming an important and essential tool to protect Internet of Things (IoT) environments. However, it is well known that the NIDS performance depends heavily on the effectiveness of the detection model, which can be influenced significantly by the learning mechanism and the available training data. Many existing studies try to mitigate the above challenges, but few of them consider the adaptability and the cost of deploying an NIDS, the integrity of the learning process, the capacity of model based on concrete traffic samples at the same time. To fill this gap and improve the detection performance, we propose a collaborative learning-based detection framework called ADCL, which can mitigate the limitations on the knowledge of a single model by leveraging multiple models trained in similar environments and detecting intrusions in a collaborative manner. Our evaluation results indicate that ADCL can provide better performance compared with a single model on detecting various attacks in IoT networks. Specifically, ADCL improves F-score by up to 80% for adaptability, 42% in mitigating the reliance on learning integrity, 85% for model capacity. Furthermore, the detection results of ADCL guide those single models to update and increase the F-score by 15%.

In recent years, Unmanned Aerial Vehicle (UAV) networks are widely used in both military and civilian scenarios. However, due to the distributed nature, they are also vulnerable to threats from adversaries. Time delay attack is a type of internal attack which maliciously delays the transmission of data packets and further causes great damage to UAV networks. Furthermore, it is easy to implement and difficult to detect due to the avoidance of packet modification and the unique characteristics of UAV networks. However, to the best of our knowledge, there is no research on time delay attack detection in UAV networks. In this paper, we propose an Efficient Time Delay Attack Detection Framework (ETD). First, we collect and select delay-related features from four different dimensions, namely delay, node, message and connection. Meanwhile, we utilize the pre-planned trajectory information to accurately calculate the real forwarding delay of nodes. Then, one-class classification is used to train the detection model, and the forwarding behaviors of all nodes can be evaluated, based on which their trust values can be obtained. Finally, the K-Means clustering method is used to distinguish malicious nodes from benign ones according to their trust values. Through extensive simulation, we demonstrate that ETD can achieve higher than 80% detection accuracy with less than 2.5% extra overhead in various settings of UAV networks and different routing protocols.

Named entity recognition (NER) is an essential subtask in natural language processing field. Recent studies have demonstrated that character-word lattice models are efficient for Chinese NER, which can leverage useful word boundary information to enhance the representation of characters. However, previous models only consider the integration of local matched word features and neglect the semantic interactions with long-range matched words. Moreover, prior methods solely achieve superficial fusion in the character-word feature space with simple methods, such as feature concatenation, but fail to implement fine-grained semantic fusion. In this paper, we propose a mixed-lattice self-attention network (MLSAN) to integrate richer word boundary information, which can explicitly capture the fine-grained correlations across characters and long-range matched words and achieve the integration of global word features. In addition, we design an end-to-end method for incorporating lexical information at the bottom layer of BERT. Compared with existing methods, our model achieves a deeper lexical knowledge fusion, which makes MLSAN perform well on cases of a small number of samples. Experimental results on four Chinese NER datasets show that our model obtains competitive performance.

With the development of airborne equipment and integrated avionics technology, the unmanned aerial vehicle (UAV) network replaces human beings in many works. However, the limited energy capacity of UAVs has great restrictions on long-time missions. To prolong the lifetime of the UAV network, we introduce the wireless static chargers (WSCs) into the UAV network, and a nondisruptive wireless rechargeable UAV network (WRUN) model is proposed, in which UAVs can be wirelessly charged without returning back to the charging platform and WSCs are scheduled to turn on and release energy only in the charging time periods. The goal of this article is to minimize the energy waste of WSCs under the premise that UAVs will not run out of energy. To calculate the efficient charging time periods of WSCs, we first discretize flight paths of UAVs so that the nondisruptive charging time schedule problem (nCTSP) that has an infinite solution space both in the spatial and time dimensions can be formalized as an optimization problem. Then, we propose the baseline algorithm exhaust candidate solutions (ECSs) to calculate the charging time periods and propose an improved algorithm using the idea of pruning (PECS) to reduce the computational complexity of ECS. Finally, experiments are conducted and PECS achieves better performance in terms of saving energy consumption and maximizing energy utilization.

The rapid development of the Internet of Things (IoT) has brought challenges to IoT platforms for high-efficiency deployments and low-budget management. Identifying IoT devices is the prerequisite for monitoring, protecting, and managing them. Considering different providers and IoT device renovation, centralized device identification solutions require large amounts of training data and frequent model updates. Traditional solutions based on machine learning cannot preserve identification precision for the long term at a low cost in reality. In this paper, we propose a multi-level IoT device identification framework, alleviating the problem of novel class detection and large-scale updating of IoT models in IoT device identification. The proposed framework improves the usability of device identification technology in the real world. We also designed an IoT device identification method, achieving an average identification accuracy of 93.37 %. With this proposed multi-level IoT device identification framework, IoT device identification can achieve a high precision over a long time.

Recently, wireless power transmission technology (WPT) is used in wireless rechargeable unmanned aerial vehicle (UAV) networks (WRUNs) to replenish energy for UAVs. In WRUN, if UAVs cannot replenish energy in time, the task will fail or the task completion time will be greatly prolonged. So, when and how long should the wireless static chargers (WSCs) be scheduled to replenish energy for UAVs while the energy utilization rate of WSCs can be optimized is very critical. However, the existing methods only optimize the charging time periods, that is the time durations during which WSCs release energy, ignoring the impact of the adjustable source power of WSCs. In fact, inappropriate source power will lead to the low energy utilization rate of WSCs. This paper takes both the charging time periods and the adjustable source power of WSCs into account and formulates the power-aware charging time scheduling (PACTS) problem as an optimization problem. Namely, how to decide the optimal charging time periods and the appropriate source power in each charging time period simultaneously to improve the energy utilization rate of WSCs. To solve PACTS, we first discretize the flight paths of UAVs spatiotemporally and then present a novel method to reformulate PACTS as a binary integer programming (BIP) problem that can be solved by existing algorithms. Finally, experiments are conducted to evaluate the performance of our scheme.

With the development of wireless sensor networks (WSNs), WSNs have been widely used in various fields such as animal habitat detection, military surveillance, etc. This paper focuses on protecting the source location privacy (SLP) in WSNs. Existing algorithms perform poorly in non-uniform networks which are common in reality. In order to address the performance degradation problem of existing algorithms in non-uniform networks, this paper proposes a robust fixed path-based random routing scheme (RFRR), which guarantees the path diversity with certainty in non-uniform networks. In RFRR, the data packets are sent by selecting a routing path that is highly differentiated from each other, which effectively protects SLP and resists the backtracking attack. The experimental results show that RFRR increases the difficulty of the backtracking attack while safekeeping the balance between security and energy consumption.

With the development of airborne equipment and integrated avionics technology, the unmanned aerial vehicle (UAV) network replaces human beings in many fields. To improve the durability of the UAV network, we propose a nondisruptive wireless rechargeable UAV network (WRUN) model, in which UAVs can be charged by wireless static chargers (WSCs) without returning back to the charging platform. Under the nondisruptive WRUN model, a baseline algorithm is proposed to solve the nondisruptive charging time schedule problem (nCTSP), in which chargers do not release energy all the time and can ensure UAVs do not run out of energy. Then to improve the energy utilization rate of WSCs, we propose an efficient charging time scheduling algorithm (ECTSA), in which the flight time and paths of UAVs are discretized and nCTSP is transformed into a linear binary integer programming (LBIP) problem to calculate the efficient charging time periods of WSCs. Finally, experiments are conducted to verify that ECTSA can improve the energy utilization of WSCs.

Internet of Things (IoT) network is vulnerable to various cyberattacks, especially insider attacks. Most existing studies mainly detect nontargeted insider attackers, who manipulate all packets forwarded by them with a probability. Compared with nontargeted attackers, targeted attackers only manipulate specific packets, which makes them more efficient and covert. In this article, we propose a targeted insider attack model called conditional packets manipulation attack (CPMA), in which attackers maliciously manipulate the packets whose attribute values meet specific conditions with a probability. When resisting the CPMA attack, most existing detection algorithms are inefficient to find such malicious behavior. Also, they detect malicious nodes by collecting and analyzing the overall behavior of nodes, which are not appropriate for energy-constrained nodes in the IoT network. To solve these problems, we present CPMAED, a malicious nodes detection framework against CPMA attack. CPMAED maintains some partial trust metrics for each relay node, which indicate the probability of launch attacks when forwarding the packets with different attribute values. Also, our scheme leverages regression and clustering algorithms to evaluate the trust values of nodes and classify them into benign or malicious. In order to obtain higher detection accuracy, we optimize the routing of transmitted packets and inject the packets to collect more information about nodes to enhance detection. The experimental results show that our proposed scheme utilizing support vector machine and$K$-means can achieve good detection performance and identify malicious nodes’ attack modes with high accuracy.

Multi-drone swarm has been widely used in disaster monitoring, mapping and remote sensing, national defense military and other fields, and has become a research hotspot in recent years. Due to the openness of its operating environment, attackers can invade the control system to capture drone, and then carry out data attacks such as tamper attack, drop attack and replay attack in drone ad-hoc network, which causes a great threat to the security of drone network. Existing malicious nodes detection algorithms are not efficient when applied to drone ad-hoc network, for the following reasons: (1) The malicious node detection algorithms based on reputation usually adopt a static threshold to determine whether a node is malicious, which is inefficient in dynamic drone network. (2) Mutual cooperation based malicious node detection algorithms rely on the high meeting probability of nodes. In order to solve the above problems, we propose a Malicious Drones Detection Algorithm(MDA) based on supervised learning and clustering algorithms. The ground station calculates the reputation value of each routing path according to the received packets from different source nodes, and then evaluates the reputation value of drones with linear regression algorithm. Finally, gaussian clustering algorithm is used to cluster drones and find out malicious drones. Experiments were conducted in indoor and outdoor drone network. The experimental results indicate that the accuracy of MDA outperforms the existing methods by 10% 20%. And in the case of fewer malicious nodes, the accuracy can reach more than 90%, and the error rate is less than 10%.

The security of wireless network devices has received widespread attention, but most existing schemes cannot achieve fine-grained device identification. In practice, the security vulnerabilities of a device are heavily depending on its model and firmware version. Motivated by this issue, we propose a universal, extensible and device-independent framework called SCAFFISD, which can provide fine-grained identification of wireless routers. It can generate access rules to extract effective information from the router admin page automatically and perform quick scans for known device vulnerabilities. Meanwhile, SCAFFISD can identify rogue access points (APs) in combination with existing detection methods, with the purpose of performing a comprehensive security assessment of wireless networks. We implement the prototype of SCAFFISD and verify its effectiveness through security scans of actual products.

Unmanned aerial vehicles (UAVs) can gather data in the air and transmit the data to the ground station. Multi-UAV systems have been used in an increasing number of mission scenarios and routing protocols play a critical role in UAV network communications. It is now well established that unstable link quality and frequently changing network topology pose significant challenges for messages forwarding in UAV networks. Hence, traditional mobile ad-hoc network routing protocols do not fit well in UAV networks. In many UAV applications, the flight paths of UAVs are planned in advance before performing missions. The positions and motion information of UAVs are available through Global Positioning System (GPS) and inertial sensors, which can be utilized to calculate the future positions of UAVs. Therefore, the future topology of the UAV network is also available. However, existing work does not take advantage of this information. Based on the trajectory, location and motion information of the UAVs, this paper proposes a future network topology-aware routing (FNTAR) protocol, which uses future location information to make superior routing decisions. Moreover, to mitigate data loss problems caused by unstable links and highly dynamic topology, FNTAR can forward messages to multiple excellent next-hop UAVs based on future network topology, and these UAVs can deliver messages to destinations faster. We implement FNTAR in the simulation experiment, the simulation results demonstrate that FNTAR can achieve lower latency and higher delivery ratio than DTNgeoprotocol.

With the high popularity of IoT devices, industrial IoT platforms, such as smart factories and oilfield industrial control systems, have become a new trend in the development of smart city. Although various manufacturers pay wide attention to the different functional requirements of IoT platforms, they seldom consider security issues, especially in terms of data security, which has led to a large number of cases of privacy leakage. Some works have been made to provide secure and reliable communication solutions for industrial IoT platforms, unfortunately, as different communication protocols and interaction models are adopted in different scenarios, these solutions are mainly isolated and fragmented. Therefore, it is an urgent challenge to construct a universal cross-platform secure communication scheme for industrial IoT platforms. In this article, we analyze the logic and requirements of different industrial IoT scenarios to abstracts them into a universal model. We summarize the possible attacks on different industrial IoT platforms and design a security scheme to capture these attacks based on the conditional proxy re-encryption primitive. The proposed scheme ensures that data cannot be accessed by an unauthorized user. We also evaluate the security and performance of our scheme, and the experimental results show that our scheme can achieve the functionality and security requirements with low overhead.

A large number of wireless devices like WiFi cameras and 4G robots have been deployed in the rapidly growing wireless network such as Internet of Things. All of the devices (sensors) are collecting and analyzing multimedia data all the time while they are actively working, and it is also required to share data among these the sensors. Typically, the wireless data is transmitted through the network gateway or the cloud platforms. In such a wireless environment, if there is no appropriate protection to the data, it is easy to cause potential data leakage. In reality, the owner of the sensor might only want to share the multimedia data stored in the sensor with a trusted third party (e.g., a family member or a coworker) through an internet gateway or the cloud platform. Ideally, the gateway or the cloud platform in the wireless network should transform one user’s encrypted data (wireless multimedia data) directly into another ciphertext under a set of new users (e.g., a trusted third party) without accessing the user’s plaintext data. In this work, a new secure notion called fuzzy-conditional proxy broadcast re-encryption (FC-PBRE) is presented to address the concern. In a FC-PBRE scheme, the proxy (the gateway or cloud server) uses a broadcast re-encryption key to re-encrypt the encrypted wireless multimedia data which can be decrypted by a set of delegatees if and only if the broadcast key’s conditional set W is close to the conditional set W′ of the ciphertext. With the FC-PBRE scheme, the wireless multimedia data is not disclosed and cannot be learnt by the proxy (the gateway or cloud server). In this paper, we first present the definition of security against chosen-ciphertext attacks for FC-PBRE. Second, we propose an efficient fuzzy-conditional proxy broadcast re-encryption scheme. Third, we prove that our FC-PBRE scheme is CCA-secure in the random oracle model based on the Decisional nBDHE assumption.