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Hybrid fuzzing that combines fuzzing and concolic execution has become an advanced technique for software vulnerability detection. Based on the observation that fuzzing and concolic execution are complementary in nature, state-of-the-art hybrid fuzzing systems deploy 'optimal concolic testing' and 'demand launch' strategies. Although these ideas sound intriguing, we point out several fundamental limitations in them, due to unrealistic or oversimplified assumptions. Further, we propose a novel 'discriminative dispatch' strategy and design a probabilistic hybrid fuzzing system to better utilize the capability of concolic execution. Specifically, we design a Monte Carlo-based probabilistic path prioritization model to quantify each path's difficulty, and then prioritize them for concolic execution. Our model assigns the most difficult paths to concolic execution. We implement a prototype named DigFuzz and evaluate our system with two representative datasets and real-world programs. Results show that the concolic execution in DigFuzz outperforms than those in state-of-the-art hybrid fuzzing systems in every major aspect. In particular, the concolic execution in DigFuzz contributes to discovering more vulnerabilities (12 versus 5) and producing more code coverage (18.9 versus 3.8 percent) on the CQE dataset than the concolic execution in Driller.

Abnormal network traffic detection is a key cybersecurity technology that assists in identifying and preventing malicious network attacks. Existing methods for detecting abnormal network traffic typically rely on complex machine learning models and a large amount of labeled data. Consequently, these methods are challenging to apply to different scenarios without retraining the model and cannot effectively handle large-scale, ongoing network attacks in real-time. To address these issues, this paper proposes a classification method based on a network flow energy model. It utilizes a reverse statistical physics model to learn target traffic features in the network, allowing it to be based on macroscopic real observations or real data without the need for manual labeling. Subsequently, the paper combines the concept of the energy model to construct a network traffic recognition model. This model judges whether a sample conforms to the main statistical distribution. Specifically, the method describes individual behavior characteristics and interaction features between traffic packets through the local field and coupling field in the energy model. By combining these two features, the method calculates the sample' s energy. If the energy is below a threshold, the sample aligns with the main distribution, indicating normal data; otherwise, it is considered abnormal data. As this method does not rely on manual labeling, it can adapt to various network environments without the need for repetitive training. This addresses current issues in traffic abnormality detection methods, which struggle to adapt to different scenarios and require extensive labeling. To evaluate the effectiveness of this method, the paper validates it using the Kitsune-2018 and CTU-13 datasets. Experimental results demonstrate that the proposed method achieves good classification performance and overall effectiveness in network traffic classification tasks. This further indicates its accuracy in performing network flow classification tasks and its adaptability to changing scenarios. [ABSTRACT FROM AUTHOR]

Ni-based coating, as an excellent anti-corrosion material, is widely used in the petroleum field and has become a research hotspot in recent years. With the continuous progress of the petrochemical industry, especially in the carbon capture, utilization, and storage and enhanced oil recovery (CCUS-EOR) systems, Ni-based coatings face significant challenges. Based on the latest research status, this paper describes the influence factors of Ni-based coatings and the applicability of different Ni-based coatings. Research shows that CO2 and H2S are the key factors affecting corrosion. With the increase in CO2 and H2S content in the environment, the corrosion rate of carbon steel will be accelerated. When the CO2 content reaches a certain critical value, further increasing the CO2 content will reduce the corrosion rate. The corrosion mechanism of carbon steel under the combined action of CO2 and H2S was also analyzed. At a high CO2/H2S partial pressure ratio, the corrosion process is dominated by CO2 corrosion, and with the increase in the H2S/CO2 partial pressure ratio, the corrosion process shifts to H2S corrosion control. The application of Ni-P protective coatings in this corrosive environment has been investigated. A surface Ni-P coating is extremely hard, wear-resistant, and corrosion-resistant. It can withstand the corrosion of CO2, high salinity, and other media while preventing electrolyte penetration. It exhibits strong corrosion resistance in NaCl solution and can help prevent scaling. However, in the presence of CO2-H2s, it has been discovered that due to inherent faults in Ni-P, the corrosion phenomenon may be exacerbated by the action of H2S, and its protection ability has to be further investigated. The mechanical properties and corrosion resistance of the coating can be enhanced by adding particles to the coating, and four new coating systems, Ni-Fe-P, Ni-Cu-P, Ni-W-P, and Ni-W-P-nSiO2, were introduced. Finally, the future development focus and prospect of CO2 corrosion protection coatings under harsh conditions are prospected. [ABSTRACT FROM AUTHOR]

In the research of drone wireless charging, the lightweight design of the wireless charging device has a large impact on the overall performance of the drone. To realize the lightweight, the tubular coil, which caused by high‐frequency excitation to produce skin effect in the conductor becomes a way to optimize the weight of the receiver coil. This paper proposes an aluminum tube with a thin copper layer plated on the surface as a drone receiver coil and landing gear. This solution can not only replace the original bracket but also be lightweight and obtain high coupling coefficient. Experiments show that this novel receiver can receive power up to 30 W at 250 kHz excitation source with 78% average transmission efficiency and has good antimisalignment ability. This new tubular coupler is more suitable for the demand of lightweight wireless charging for drones. [ABSTRACT FROM AUTHOR]

The hybrid fuzzing technique leverages the symbolic execution techniques and the fuzz testing techniques to achieve complementary goals. The synchronization of testcase is the key to collaborate between modules. However, the testcase synchronization was implemented with an exchange and integration approach in existing hybrid fuzzing techniques, which ignores runtime information when the symbolic execution module was exploring the program state. To solve the above problem, a testcase synchronization method for hybrid fuzzing based on keypoints, namely Sol-QSYM, was proposed. Sol-QSYM aims to analyze the execution process of symbolic execution, locate and identify the code coverage oriented program keypoints, and then guide the testcase scheduling and mutation process of fuzz testing to achieve a finer-grained testcase synchronization. First, a set of variables corresponding to the hard-to-reach branches of the fuzz testing module was identified in the symbolic execution and extracted as the keypoints of the program. Secondly, to fully utilize the results of the symbolic solution, the keypoints of the single solution were further combined to help the symbolic execution module to additionally generate more testcases that can be imported by the fuzz testing module. Finally, in the fuzz testing module, the testcases containing keypoints in the seed selection step were preferentially selected to guide the testing process in exploring specific areas of the program, and the keypoint locations were highlighted in the mutation step to guide the generation of testcases that would cover new branches of code. A prototype system was implemented based on the hybrid vulnerability mining tool QSYM, and 12 real programs were selected for experimental evaluation. The results of experiments showed that compared to QSYM, Sol-QSYM improved the import rate of test cases by 12.73%, code coverage rate by 9.07%. Meanwhile, it found more program crashes. All these results indicated that the enhanced test case synchronization method can improve the utilization of program state exploration results in symbolic execution by hybrid vulnerability mining. [ABSTRACT FROM AUTHOR]