Concolic execution follows the execution paths of concrete inputs, capable of generating new inputs for unexplored code by solving negated path constraints. However, implicit flows can hinder concolic execution, reducing the code coverage. Implicit flows occur when inputs influence control flow, and the control flow variation affects the values of some variables. During concolic execution, the preceding path selections limit the potential values of these variables. This limitation may result in unsolvable constraints, subsequently restricting the generation of new inputs for unexplored paths. Our insight is that following the same preceding paths is unnecessary, and we can adapt preceding paths to make the latest constraints solvable. We divide states into general states and implicit-flow-solving states (IFSSs). We utilize the general states to perform concolic execution. When solving constraints influenced by implicit flows, we switch to the IFSSs. We use the IFSSs to explore the relevant code region and adapt paths. To mitigate path explosion and construct the relation between inputs and the variables, we merge the IFSSs. State merging does not burden the general states, and we limit the code regions for the IFSSs to minimize the introduced overhead. Finally, we replace the variable symbols in the target constraints with new expressions and attempt to solve the new constraints. We implement our approach in Backsolver and build a test suite to evaluate it. Backsolver successfully identifies all the implicit flows in the test suite and resolves most of them. When evaluated on 6 real-world binaries, Backsolver resolves the highest number of branches related to implicit flows in total. Besides, Backsolver has the highest code coverage in PlutoSVG and finds a 0-day vulnerability. We reported the vulnerability and obtained a CVE ID.
Modality representation learning is an important problem for multimodal sentiment analysis (MSA), since the highly distinguishable representations can contribute to improving the analysis effect. Previous works of MSA have usually focused on multimodal fusion strategies, and the deep study of modal representation learning was given less attention. Recently, contrastive learning has been confirmed effective at endowing the learned representation with stronger discriminate ability. Inspired by this, we explore the improvement approaches of modality representation with contrastive learning in this study. To this end, we devise a three-stages framework with multi-view contrastive learning to refine representations for the specific objectives. At the first stage, for the improvement of unimodal representations, we employ the supervised contrastive learning to pull samples within the same class together while the other samples are pushed apart. At the second stage, a self-supervised contrastive learning is designed for the improvement of the distilled unimodal representations after cross-modal interaction. At last, we leverage again the supervised contrastive learning to enhance the fused multimodal representation. After all the contrast trainings, we next achieve the classification task based on frozen representations. We conduct experiments on three open datasets, and results show the advance of our model.
Clock synchronization is critical for many WSNs due to the need of inter-node coordination and collaborative information processing. Existing protocols based on message passing achieve satisfactory clock synchronization accuracy, however, incur prohibitively high overhead especially in large-scale networks. In this paper, we propose a new clock synchronization approach called ROCS which exploits the radio data system (RDS) from FM radio stations. First, we design a new hardware FM receiver that can extract a periodic pulse from FM broadcasts, referred to as RDS clock. We then conduct a large-scale measurement study of RDS clock in our lab for a period of six days and on a vehicle driving through a metropolitan area of over 40km 2 . Our results show that RDS clock is highly stable and hence is a viable means to calibrate the clocks of large-scale city-wide sensor networks. To reduce the high power consumption of FM receiver, ROCS adaptively calibrates the native clock via the RDS clock. We implement ROCS in TinyOS on our hardware FM receiver and a TelosB-compatible WSN platform. Our extensive experiments using a 12-node testbed and our driving measurement traces show that ROCS achieves accurate and precise clock synchronization with low power consumption.
Fingerprinting Internet-of-Things(IoT) devices on types and brands is a necessary work for security analysis in the cyberspace. The existing approaches mainly rely on the dominant features of devices which is response to information in order to identify these online devices. However, the web server components reusing and products rebranding are the common phenomenons of these embedded IoT devices. It caused the existing approaches difficult to identify most devices even errors due to the similar responses. In this paper, we present an approach, IoTXray, which improves the work efficiently of information collection about accelerating the relations between reusing/rebranding devices with the corresponding manufacturers. And these relations can generate more accurate and reliable fingerprints than previous approaches. Using the mixed neural networks, IoTXray comprehensively detects the real manufactures of online IoT devices upon three different kinds of data sources. In the experiment, our approach can identify 7,025,854 IoT devices on HTTP-hosts. The identification rate has reached to several times higher than previous approaches. Our approach has especially detected 3,268,953 reusing and 963,653 rebranding devices with their original manufacturers.
Few-shot named entity recognition can identify new types of named entities based on a few labeled examples. Previous methods employing token-level or span-level metric learning suffer from the computational burden and a large number of negative sample spans. In this paper, we propose the Hybrid Multi-stage Decoding for Few-shot NER with Entity-aware Contrastive Learning (MsFNER), which splits the general NER into two stages: entity-span detection and entity classification. There are 3 processes for introducing MsFNER: training, finetuning, and inference. In the training process, we train and get the best entity-span detection model and the entity classification model separately on the source domain using meta-learning, where we create a contrastive learning module to enhance entity representations for entity classification. During finetuning, we finetune the both models on the support dataset of target domain. In the inference process, for the unlabeled data, we first detect the entity-spans, then the entity-spans are jointly determined by the entity classification model and the KNN. We conduct experiments on the open FewNERD dataset and the results demonstrate the advance of MsFNER.
Underwater sensor networks are envisioned to enable a wide range of underwater applications such as pollution monitoring, offshore exploration, and oil/gas spill monitoring. Such applications require precise location information as otherwise the sensed data might be meaningless. On the other hand, security and privacy are critical issues as underwater sensor networks are typically deployed in harsh environments. Nevertheless, most underwater localization schemes are vulnerable to many attacks and suffer from potential privacy violations as they are designed for benign environments. However, a localization scheme that does not consider security and privacy could lead to serious consequences, especially in critical applications such as military monitoring. In this article, we discuss the security and privacy issues in underwater localization, and investigate the techniques that can provide security and preserve node privacy in underwater sensor networks.
In this paper, a decentralized adaptive TDMA scheduling strategy (DATS) for Vehicular Ad-hoc Network (VANET) with the ability of reducing the collision of nodes occupying the same slot is proposed. In DATS, slots are divided into left and right slot sets. The ratio between the two slot sets can be adjusted according to the node density. According to the locations and neighbors' slots, nodes competition slots in corresponding time slot sets. The simulation experiments show that the DATS has higher successful assignment ratio and lower delay than several existing protocols. Through extensive simulations, we will demonstrate that the proposed strategy can reduce collisions by over 50% with an acceptable overhead.
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