The Taint Rabbit: Optimizing Generic Taint Analysis with Dynamic Fast Path Generation

Generic taint analysis is a pivotal technique in software security. However, it suffers from staggeringly high overhead. In this paper, we explore the hypothesis whether just-in-time (JIT) generation of fast paths for tracking taint can enhance the performance. To this end, we present the Taint Rabbit, which supports highly customizable user-defined taint policies and combines a JIT with fast context switching. Our experimental results suggest that this combination outperforms notable existing implementations of generic taint analysis and bridges the performance gap to specialized trackers. For instance, Dytan incurs an average overhead of 237x, while the Taint Rabbit achieves 1.7x on the same set of benchmarks. This compares favorably to the 1.5x overhead delivered by the bitwise, non-generic, taint engine LibDFT

Obesity and male breast cancer: Provocative parallels?

While rare compared to female breast cancer the incidence of male breast cancer (MBC) has increased in the last few decades. Without comprehensive epidemiological studies, the explanation for the increased incidence of MBC can only be speculated. Nevertheless, one of the most worrying global public health issues is the exponential rise in the number of overweight and obese people, especially in the developed world. Although obesity is not considered an established risk factor for MBC, studies have shown increased incidence among obese individuals. With this observation in mind, this article highlights the correlation between the increased incidence of MBC and the current trends in obesity as a growing problem in the 21st century, including how this may impact treatment. With MBC becoming more prominent we put forward the notion that, not only is obesity a risk factor for MBC, but that increasing obesity trends are a contributing factor to its increased incidence

Generating synthetic trajectory data using GRU

With the rise of mobile network, user location information plays an increasingly important role in various mobile services. The analysis of mobile users’ trajectories can help develop many novel services or applications, such as targeted advertising recommendations, location-based social networks, and intelligent navigation. However, privacy issues limit the sharing of such data. The release of location data resulted in disclosing users’ privacy, such as home addresses, medical records, and other living habits. That promotes the develop-ment of trajectory generators, which create synthetic trajectory data by simulating moving objects. At current, there are some disadvantages in the process of gen-eration. The prediction of the following position in the trajectory generation is very dependent on the historical location data, but the relationship between trajectory positions tends to be ignored. Most commonly used methods only adopt the probability distribution of users’ positions to generate synthetic data. On the one hand, this type of statistical method is too rough, and on the other hand, it cannot bring more benefits in availability by increasing data volume. We propose a new trajectory generation method in this paper–Trajectory Generation Model with RNNs(TGMRNN), to address the deficiencies above. It adopts the RNN model to replace the traditional Markov model to generate trajectory data with higher availability. Meanwhile, it solves the problem that RNNs are unsuitable for continuous location data by representing trajectories as discretized data with the grid method. We have conducted experiments in a real data set. Compared with the Markov model, the results of TGMRNN demonstrate that it is superior to some existing methods.Published versionThe work was supported by National Natural Science Foundation of China (61941114), National Natural Science Foundation of China (Grant No. 61802025), National Natural Science Foundation of China (No. 62001055), Beijing Natural Science Foundation (4204107), Funds of “YinLing” (No. A02B01C03-201902D0)

PSOFuzzer: A Target-Oriented Software Vulnerability Detection Technology Based on Particle Swarm Optimization

Coverage-oriented and target-oriented fuzzing are widely used in vulnerability detection. Compared with coverage-oriented fuzzing, target-oriented fuzzing concentrates more computing resources on suspected vulnerable points to improve the testing efficiency. However, the sample generation algorithm used in target-oriented vulnerability detection technology has some problems, such as weak guidance, weak sample penetration, and difficult sample generation. This paper proposes a new target-oriented fuzzer, PSOFuzzer, that uses particle swarm optimization to generate samples. PSOFuzzer can quickly learn high-quality features in historical samples and implant them into new samples that can be led to execute the suspected vulnerable point. The experimental results show that PSOFuzzer can generate more samples in the test process to reach the target point and can trigger vulnerabilities with 79% and 423% higher probability than AFLGo and Sidewinder, respectively, on tested software programs

PSOFuzzer: A Target-Oriented Software Vulnerability Detection Technology Based on Particle Swarm Optimization

Coverage-oriented and target-oriented fuzzing are widely used in vulnerability detection. Compared with coverage-oriented fuzzing, target-oriented fuzzing concentrates more computing resources on suspected vulnerable points to improve the testing efficiency. However, the sample generation algorithm used in target-oriented vulnerability detection technology has some problems, such as weak guidance, weak sample penetration, and difficult sample generation. This paper proposes a new target-oriented fuzzer, PSOFuzzer, that uses particle swarm optimization to generate samples. PSOFuzzer can quickly learn high-quality features in historical samples and implant them into new samples that can be led to execute the suspected vulnerable point. The experimental results show that PSOFuzzer can generate more samples in the test process to reach the target point and can trigger vulnerabilities with 79% and 423% higher probability than AFLGo and Sidewinder, respectively, on tested software programs

Analyzing and Discovering Spatial Algorithm Complexity Vulnerabilities in Recursion

The algorithmic complexity vulnerability (ACV) that may lead to denial of service attacks greatly disrupts the security and availability of applications, and due to the widespread use of third-party libraries, its impact may be amplified through the software supply chain. The existing work in the field is dedicated to abstract loop or iterative patterns and fuzzing the entire application to discover algorithm complexity vulnerabilities, but they still face efficiency and effectiveness issues. Our research focuses on: (1) proposing a representation and extraction method for code features related to algorithmic complexity vulnerabilities, helping analysts quickly understand program logic; (2) providing a new ACV detecting model, focusing on the spatial complexity anomalies caused by deep recursion structures, and proposing a new filtering method; and (3) aiming at the difficulty of efficiently generating complex-data-type-related payloads using existing symbol execution techniques, a call-chain-guided payload construction method is proposed. We tested third-party components in the open-source Java Maven Repository, identified many unexposed vulnerabilities, and eight of them received Common Vulnerabilities and Exposures (CVE) identifiers, and demonstrated that our method can discover more algorithmic complexity vulnerabilities compared to existing tools with better performance

In Vitro Antifungal Activity of Dihydrochelerythrine and Proteomic Analysis in Ustilaginoidea virens

Dihydrochelerythrine (DHCHE) is an isoquinoline compound, which has distinct antifungal activity and can induce apoptosis. The antifungal activity of DHCHE against five rice pathogenic fungi was studied in vitro. At the concentration of 7.5 mg/L, DHCHE exhibited the highest efficacy among tested compounds in inhibiting mycelium growth, with an inhibition rate of 68.8% in Ustilaginoidea virens, which was approximately 2.4 times of that of validamycin (28.7%). After exposure to DHCHE, transmission electron micrographs revealed spores showed incomplete organelles, malformed cell walls and nuclear membranes, as well as irregular lipid spheres. Reactive oxygen species accumulation in treated spores was markedly higher than that in control spores. DHCHE induced cell damage increased in a dose-dependent manner, as indicated by the decrease in mitochondrial membrane potential and initiation of apoptosis. The differences of expression levels of Fip1, ACP1, PMS2 and COX13 that are important for oxidative phosphorylation and mismatch repair pathway were significant, which may be some of the reasons for the induction of apoptosis in DHCHE-treated U. virens. The protein levels of Fip1, ACP1, PMS2 and COX13 agreed with protein fold change ratio from parallel reaction monitoring Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathway of differentially expressed proteins were further analyzed. These findings will help to elucidate the mechanisms associated with antifungal and pro-apoptotic effects of DHCHE on U. virens, thereby aiding the potential development of novel pesticides

Malicious JavaScript Detection Based on Bidirectional LSTM Model

JavaScript has been widely used on the Internet because of its powerful features, and almost all the websites use it to provide dynamic functions. However, these dynamic natures also carry potential risks. The authors of the malicious scripts started using JavaScript to launch various attacks, such as Cross-Site Scripting (XSS), Cross-site Request Forgery (CSRF), and drive-by download attack. Traditional malicious script detection relies on expert knowledge, but even for experts, this is an error-prone task. To solve this problem, many learning-based methods for malicious JavaScript detection are being explored. In this paper, we propose a novel deep learning-based method for malicious JavaScript detection. In order to extract semantic information from JavaScript programs, we construct the Program Dependency Graph (PDG) and generate semantic slices, which preserve rich semantic information and are easy to transform into vectors. Then, a malicious JavaScript detection model based on the Bidirectional Long Short-Term Memory (BLSTM) neural network is proposed. Experimental results show that, in comparison with the other five methods, our model achieved the best performance, with an accuracy of 97.71% and an F1-score of 98.29%

Grey-Box Fuzzing Based on Reinforcement Learning for XSS Vulnerabilities

Cross-site scripting (XSS) vulnerabilities are significant threats to web applications. The number of XSS vulnerabilities reported has increased annually for the past three years, posing a considerable challenge to web application maintainers. Black-box scanners are mainstream tools for security engineers to perform penetration testing and detect XSS vulnerabilities. Unfortunately, black-box scanners rely on crawlers to find input points of web applications and cannot guarantee all input points are tested. To this end, we propose a grey-box fuzzing method based on reinforcement learning, which can detect reflected and stored XSS vulnerabilities for Java web applications. We first use static analysis to identify potential input points from components (i.e., Java code, configuration files, and HTML files) of the Java web application. Then, an XSS vulnerability payload generation method is proposed, which is used together with the reinforcement learning model. We define the state, action, and reward functions of three reinforcement learning models for XSS vulnerability detection scenarios so that the fuzz loop can be performed automatically. To demonstrate the effectiveness of the proposed method, we compare it against four state-of-the-art web scanners. Experimental results show that our method finds all XSS vulnerabilities and has no false positives