Control flow errors (CFEs) pose a serious threat to the reliability of embedded systems, particularly under increasing integration density and shrinking feature sizes. Existing CFE detection techniques typically rely on coarse-grained analysis and uniform checking strategies, lacking fine-grained awareness of structural and runtime characteristics. This limitation often leads to considerable overhead, making such approaches less suitable for resource-constrained embedded systems. To tackle this shortcoming, we propose a CFE Detection approach guided by Basic block Vulnerability Analysis (CDBVA) that aims to strike a balance between the detection effectiveness and the overhead. Specifically, we first extract the CFE-related structural and execution features to characterize basic block vulnerability. Then, we train a learning-based model to predict basic blocks that are vulnerable to CFEs. Finally, we design a hybrid signature checking strategy that performs appropriate checks on vulnerable and non-vulnerable basic blocks separately. Experimental results demonstrate that CDBVA achieves an average prediction accuracy of 86.2% and an average CFE coverage of 95.79%, outperforming state-of-the-art approaches. While maintaining high CFE coverage, CDBVA improves the evaluation factor by 12.14%–20.82%, achieving a favorable tradeoff between detection effectiveness and overhead. In addition, CDBVA demonstrates stable performance across diverse input conditions and heterogeneous hardware architectures.

Control flow errors (CFEs) are prevalent and de-structive runtime faults that compromise software reliability and security. Existing CFE detection approaches either perform coarse-grained modeling at the basic-block level, missing fine-grained implicit features, or rely on exhaustive fault injection for detailed error patterns. These limitations hinder efficient detection, especially for silent CFEs, where branches deviate from correct paths, yet still follow compiler-defined ones. To bridge this gap, we propose CEDAR, a novel CFE detection approach, which detects silent CFEs by localizing and hardening vulnerable instructions. Specifically, we first conduct multi-level control flow analysis with limited fault injection, enhanced by dynamic data propagation, to construct a dual-layer heterogeneous graph representation of the program. Subsequently, we employ Graph Neural Networks (GNNs) to learn silent CFE-relevant embeddings and develop a model to localize vulnerable instructions. Finally, targeted program hardening mechanisms are integrated to detect silent CFEs and improve overall CFE coverage. Experiments demonstrate that CEDAR achieves 92.28% coverage of silent CFEs and 96.47% on average for overall CFEs. While maintaining high coverage, it improves the Evaluation Factor (EF) by 21.97% over the state-of-the-art approach, achieving a favorable trade-off between effectiveness and overhead. More-over, it demonstrates robustness across diverse input scenarios and Instruction Set Architectures (ISAs).

One of the most difficult data flow errors to detect caused by single-event upsets in space radiation is the Silent Data Corruption (SDC). To solve the problem of multi-bit upsets causing program SDC, an instruction multi-bit SDC vulnerability prediction model based on one-class support vector machine classification is built using SDC vulnerability analysis, which has more accurate vulnerability instruction identification capabilities. By hardening the program with selective instruction redundancy, we propose a multi-bit data flow error detection method for detecting SDC error (SDCVA-OCSVM), aiming to protect the data in the memory or register used by the program. We have also verified the effectiveness of the method through comparative experiments. The method has been verified to have a higher error detection rate and lower code size and time overhead.

To improve the security and efficiency of remote attestation (RA) for embedded systems, this study proposes mutual authentication‐based RA scheme for embedded systems. Especially, the authors design an RA framework based on authentication agents and measurement agents, which combines the mutually anonymous identity authentication scheme with the platform integrity attestation. During the identity authentication period, based on the traditional direct anonymous authentication scheme, the time‐stamping mechanism and the mutual direct anonymous attestation mechanism are proposed to achieve bidirectional anonymous authentication of both parties in the communication. During the platform integrity attestation period, combining with the locality principle, they improve the data structure for storing the integrity measurements of the module and propose an RA mechanism based on locality principle‐based hash tree. This mechanism can shorten the length of the certification path and improve the verification efficiency of platform configuration integrity certification. Furthermore, experimental results and analysis show that the efficiency of the proposed scheme is superior to the existing schemes.

The Aerial Internet-of-Things (Aerial-IoT) systems, deploying sensors on high-altitude platforms, e.g., drones, parachutes, and aircrafts, are a crucial monitor due to its agile maneuverability and augmentation of observation, collection, and communication. As such, the measurement accuracy and requirements of Aerial-IoT are far beyond the ability of general commercial-off-the-shelf sensors, especially in the high-altitude environment, where environmental factors (air pressure, temperature, humidity, wind movement, etc.) tend to change rapidly and lead to highly deviated readings. In this article, we tackle this challenge. First, we introduce our designed measurement system for Aerial-IoT. Then, to compensate for the low data quality and calibrate the deviation data from sensors, we take into account the inherent correlations and interaction between sensor data and environmental factors, and construct a data calibration model, called data calibration based on the neural network (DC-NN). Finally, to illustrate the effectiveness of our system, we carry out a real-world implementation by deploying sensors on the surface of parachutes in a dynamic airdrop environment. Extensive experiments on temperature-humidity-material-tensile-testing (THMTT) and high-altitude airdrop are conducted to show the significant improvements of our proposed DC-NN model.

To protect core functions, applications often utilize the countermeasure techniques such as antidebugging to avoid analysis by outsiders, especially the malware. Dynamic binary instrumentation is commonly used in the analysis of binary programs. However, it can be easily detected and has stability and applicability problems as it involves program rewriting and just-in-time compilation. This paper proposes a new lightweight analysis method for binary programs with the assistance of hardware features and the operating system kernel, named BAHK, which can automatically analyze the target program by stealth and has wide applicability. With the support of underlying infrastructures, this paper designs several optimization strategies and specific analysis approaches at instruction level to reduce the impact of fine-grained analysis on the performance of target program so that it can be well applied in practice. The experimental results show that the proposed method has good stealthiness, low memory consumption, and positive user experience. In some cases, it shows better analysis performance than the traditional dynamic binary instrumentation method. Finally, the real case studies further show its feasibility and effectiveness.

With the widespread use of mobile embedded devices in the Internet of Things, mobile office, and edge computing, security issues are becoming more and more serious. Remote attestation, one of the mobile security solutions, is a process of verifying the identity and integrity status of the remote computing device, through which the challenger determines whether the platform is trusted by discovering an unknown fingerprint. The remote attestation on the mobile terminal faces many security challenges presently because there is a lack of trusted roots, devices are heterogeneous, and hardware resources are strictly limited. To ARM’s mobile platform, we propose a mobile remote attestation scheme based on ARM TrustZone (TZ-MRAS), which uses the highest security authority of TrustZone to implement trusted attestation service. Compared with the existing mobile remote attestation scheme, it has the advantages of wide application, easy deployment, and low cost. To defend against the time-of-check-to-time-of-use (TOC-TOU) attack, we propose a probe-based dynamic integrity measurement model, ProbeIMA, which can dynamically detect unknown fingerprints that generate during kernel and process execution. Finally, according to the characteristics of the improved dynamic measurement model, that is, the ProbeIMA will expand the scale of the measurement dataset, an optimized stored measurement log construction algorithm based on the locality principle (LPSML) is proposed, which has the advantages of shortening the length of the authentication path and improving the verification efficiency of the platform configuration. As a proof of concept, we implemented a prototype for each service and made experimental evaluations. The experimental results show the proposed scheme has higher security and efficiency than some existing schemes.

Autonomous navigation systems on unmanned aerial vehicles (UAVs) equipped with multiple sensors are essential to various applications in the smart city and intelligent transportation. However, the general autonomous navigation models are markedly influenced by the prior knowledge from training environments, which in turn are not applicable in unknown environments. To address this issue, we propose an online autonomous UAV navigation system named as multi-sensor data-fusion-based autonomous navigation (MDFAN) system for unknown flight environments, including the collision avoidance and path planning. Specifically, first, the newly MDFAN system formulates the navigation problem as a decision-making path planning problem to reduce the dependence of prior knowledge of the flight environment. Secondly, we develop a multi-sensor data-fusion-based method to extract more effective local environment information for mining the inherent inter-relationship between the local environment information and the current state of the UAV. Thirdly, we propose a deep reinforcement learning method for handling uncertain situations of the unknown environment. Finally, we validated our method both on the simulated and real-world environments.

Many famous attacks take web browsers as transmission channels to make the target computer infected by malwares, such as watering hole and domain name hijacking. In order to protect the data transmission, the SSL/TLS protocol has been widely used to defeat various hijacking attacks. However, the existence of such encryption protection makes the security software and devices confront with the difficulty of analyzing the encrypted malicious traffic at endpoints. In order to better solve this kind of situation, this paper proposes a new efficient and transparent method for large-scale automated TLS traffic analysis, named as hyper TLS traffic analysis (HTTA). It extracts multiple types of valuable data from the target system in the hyper mode and then correlates them to decrypt the network packets in real time, so that overall data correlation analysis can be performed on the target. Additionally, we propose an aided reverse engineering method to support the analysis, which can rapidly identify the target data in different versions of the program. The proposed method can be applied to the endpoints and cloud platforms; there are no trust risk of certificates and no influence on the target programs. Finally, the real experimental results show that the method is feasible and effective for the analysis, which leads to the lower runtime overhead compared with other methods. It covers all the popular browser programs with good adaptability and can be applied to the large-scale analysis.

Efficient resource allocation which aims to maximize the network utility under energy neural operation is well known as a key issue in energy harvesting wireless sensor networks (EHWSNs). However, as the energy resource is unstable in practical systems, it's challenging to tackle the uncertainty in harvested energy profile. Instead of designing sophisticated harvested energy prediction model, we directly make uncertainty involved in the resource allocation design. Considering the uncertainty of harvested energy profile, a flexible network utility optimization approach is proposed that can achieve high network utility and robustness against uncertain harvested energy. We firstly formulate the network utility maximization problem subject to energy constraints involving uncertainty. We then introduce a flexible uncertainty model to describe the harvested energy and transform the network utility maximization with uncertainties into a traditional optimization problem. Our experimental results demonstrate the proposed approach is able to provide flexible energy allocation and achieve robustness.

Compressed sensing for wireless sensor networks has attracted a lot of research attention in the last decade for its advantages in energy saving, robustness, and so on. Nevertheless, existing solutions mostly focus on the data compression performance while neglecting the energy efficiency. In this paper, we first present the joint resource allocation problem formulation based on compressed sensing. Then a distributed algorithm to compute the sampling rate and routes utilizing local network status is proposed. We conduct extensive experiments based on meteorological wireless sensor networks to verify the merit of our mechanism; it is shown that the proposed mechanism is able to achieve very high efficiency in terms of network lifetime and sensing quality compared with existing approaches.

Research on endpoint security involves both traditional PC platform and prevalent mobile platform, among which the analysis of software vulnerability and malware is one of the important contents. For researchers, it is necessary to carry out nonstop exploration of the insecure factors in order to better protect the endpoints. Driven by this motivation, we propose a new threat model named Process Memory Captor (PMCAP) on the Windows operating system which threatens the live process volatile memory data. Compared with other threats, PMCAP aims at dynamic data in the process memory and uses a noninvasive approach for data extraction. In this paper we describe and analyze the model and then give a detailed implementation taking four popular web browsers IE, Edge, Chrome, and Firefox as examples. Finally, the model is verified through real experiments and case studies. Compared with existing technologies, PMCAP can extract valuable data at a lower cost; some techniques in the model are also suitable for memory forensics and malware analysis.

Co-saliency detection, an emerging research area in saliency detection, aims to extract the common saliency from the multi images. The extracted co-saliency map has been utilized in various applications, such as in co-segmentation, co-recognition and so on. With the rapid development of image acquisition technology, the original digital images are becoming more and more clearly. The existing co-saliency detection methods processing these images need enormous computer memory along with high computational complexity. These limitations made it hard to satisfy the demand of real-time user interaction. This paper proposes a fast co-saliency detection method based on the image block partition and sparse feature extraction method (BSFCoS). Firstly, the images are divided into several uniform blocks, and the low-level features are extracted from Lab and RGB color spaces. In order to maintain the characteristics of the original images and reduce the number of feature points as well as possible, Truncated Power for sparse principal components method are employed to extract sparse features. Furthermore, K-Means method is adopted to cluster the extracted sparse features, and calculate the three salient feature weights. Finally, the co-saliency map was acquired from the feature fusion of the saliency map for single image and multi images. The proposed method has been tested and simulated on two benchmark datasets: Co-saliency Pairs and CMU Cornell iCoseg datasets. Compared with the existing co-saliency methods, BSFCoS has a significant running time improvement in multi images processing while ensuring detection results. Lastly, the co-segmentation method based on BSFCoS is also given and has a better co-segmentation performance.

Wireless sensor networks (WSNs) are increasingly deployed for mission-critical applications such as emergency management and health care, which impose stringent requirements on the communication performance of WSNs. To support these applications, it is crucial to model and measure the effect of wireless interference, which is the major factor that limits WSN performance. Accurate modeling and measurement of interference faces two key challenges. First, as shown in our experimental results, interference yields considerable spatial and temporal variations of WSN performance, which poses a major challenge for measurement at rum-time. Second, in the unlicensed band, the communication of WSN is interfered by coexisting wireless devices such as smartphones and laptops equipped with 802.11 radios, which lead to cross-technology interference that are difficult to characterize due to the heterogeneous PHY. To tackle these challenges, this paper presents a novel accuracy-aware approach to interference modeling and measurement for WSNs. First, we propose a new regression-based interference model and analytically characterize its accuracy based on statistics theory. Second, we develop a novel protocol called accuracy-aware interference measurement for measuring the proposed interference model with assured accuracy at run time. Third, building on interference modeling, we propose an algorithm that accurately forecasts the performance of WSNs in the presence of cross-technology interference. Our extensive experiments on a testbed of 17 TelosB motes show that the proposed approaches achieve high accuracy of interference modeling and WSN performance forecasting with significantly lower overhead than state-of-the-art approaches.

Ensuring dependability is significant in the development process of Real-Time Embedded Systems (RTESs). The dependability of a system model is usually presented by temporal and data constraints, which are ambiguous and incomplete when using semi-formal methods. Formal methods have precise semantics and strong verifiability, but few can capture the dependability features for RTESs. This paper presents Z-MARTE, an extensible modeling method combining MARTE profile and Z notation, to provide rigorous specifications towards the dependability features of RTESs. To extend the descriptive ability of Z, we design the time model, structure model and behavior model in Z-MARTE, specifying temporal and data constraints in the form of predicates. Z-MARTE can be edited and verified by the existing tools for Z. The converting from MARTE to Z-MARTE is supported by ZMT, a model transformation tool we design. A case study of a communication system is given to illustrate the modeling and verification procedure of Z-MARTE.