MTFuzz: Fuzzing with a Multi-Task Neural Network

Fuzzing is a widely used technique for detecting software bugs and vulnerabilities. Most popular fuzzers generate new inputs using an evolutionary search to maximize code coverage. Essentially, these fuzzers start with a set of seed inputs, mutate them to generate new inputs, and identify the promising inputs using an evolutionary fitness function for further mutation. Despite their success, evolutionary fuzzers tend to get stuck in long sequences of unproductive mutations. In recent years, machine learning (ML) based mutation strategies have reported promising results. However, the existing ML-based fuzzers are limited by the lack of quality and diversity of the training data. As the input space of the target programs is high dimensional and sparse, it is prohibitively expensive to collect many diverse samples demonstrating successful and unsuccessful mutations to train the model. In this paper, we address these issues by using a Multi-Task Neural Network that can learn a compact embedding of the input space based on diverse training samples for multiple related tasks (i.e., predicting for different types of coverage). The compact embedding can guide the mutation process by focusing most of the mutations on the parts of the embedding where the gradient is high. \tool uncovers $11$ previously unseen bugs and achieves an average of $2\times$ more edge coverage compared with 5 state-of-the-art fuzzer on 10 real-world programs.Comment: ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 202

Discriminability-Based Transfer between Neural Networks

Previously, we have introduced the idea of neural network transfer, where learning on a target problem is sped up by using the weights obtained from a network trained for a related source task. Here, we present a new algorithm, called Discriminability-Based Transfer (DBT), which uses an information measure to estimate the utility of hyperplanes defined by source weights in the target network, and rescales transferred weight magnitudes accordingly. Several experiments demonstrate that target networks initialized via DBT learn significantly faster than networks initialized randomly. 1 INTRODUCTION Neural networks are usually trained from scratch, relying only on the training data for guidance. However, as more and more networks are trained for various tasks, it becomes reasonable to seek out methods that avoid "reinventing the wheel", and instead are able to build on previously trained networks' results. For example, consider a speech recognition network that was only trained on Ameri..

Back-Propagation Learning on Ribosomal Binding sites in DNA sequences using preprocessed features

Several studies have explored how neural networks can be used to find genes within regions of previously uncharacterized deoxyribonucleic acid (DNA). This paper describes the creation of a neural network training set for determining which part of a DNA strand codes for an important genetic feature called a Ribosomal Binding Site, or RBS. Based on previous research on detecting other genetic features, this data set contains preprocessed features that reflect biologically meaningful patterns in the raw base pair [ACTG] ? language. We also describe preliminary empirical results indicating neural network performance that is superior to all other automated methods for detecting RBS's. 1 Introduction NEURAL networks have recently been used for a variety of applications [ Maren et al., 1990 ] . Comparisons between neural networks and competing techniques have also shown that neural network performance is competitive to more traditional methods on many tasks [ Shavlik et al., 1991, Weiss ..

Back-Propagation Learning on Ribosomal Binding sites in DNA sequences using preprocessed features

Several studies have explored how neural networks can be used to find genes within regions of previously uncharacterized deoxyribonucleic acid (DNA). This paper describes the creation of a neural network training set for determining which part of a DNA strand codes for an important genetic feature called a Ribosomal Binding Site, or RBS. Based on previous research on detecting other genetic features, this data set contains preprocessed features that reflect biologically meaningful patterns in the raw base pair [ACTG] ? language. We also describe preliminary empirical results indicating neural network performance that is superior to all other automated methods for detecting RBS's. 1 Introduction NEURAL networks have recently been used for a variety of applications [ Maren et al., 1990 ] . Comparisons between neural networks and competing techniques have also shown that neural network performance is competitive to more traditional methods on many tasks [ Shavlik et al., 1991, Weiss ..

Campus tobacco control policies and cessation interventions in college students: a commentary calling for research and action to address tobacco-related health disparities

Tobacco-related health disparities (TRHDs) have a significant impact on population health in the USA. Effectively preventing and controlling TRHDs among young adult populations require multiple prevention and cessation points, including within college/university contexts. This commentary addresses current campus tobacco control policies and cessation interventions for U.S. college students, with an emphasis on TRHDs and opportunities for research and research translation to reduce these disparities. This commentary is informed by literature published between 2010 and 2020 regarding (a) prevalence and impact of campus tobacco control policies; and/or (b) behavioral outcomes from cessation interventions for young adults attending colleges. Despite a doubling of college campuses adopting tobacco-free policies from 2012 to 2017, roughly two-thirds continue to operate without such policies. Few policies address alternative tobacco products (e.g., e-cigarettes, cigars/cigarillos, and hookah), and communication about and enforcement of existing policies is extremely limited. A broad range of cessation intervention strategies have targeted individuals in this age group, but with little focus on TRHDs and limited intervention dissemination. Importantly, college students representing populations at risk for TRHDs (e.g., racial/ethnic/sexual/gender minorities, low socioeconomic status) are less likely to be exposed to strong tobacco control policies or supports for cessation. There are untapped opportunities for behavioral medicine approaches to reduce TRHDs in college settings. Research findings regarding multilevel (policy, community-level, and individual-level) interventions must be translated to policy/practice in order to address tobacco use, particularly among vulnerable college student populations