相关论文: Toward Unbiased Multiple-Target Fuzzing with Path …
Fuzzing is an effective bug-finding technique but it struggles with complex systems like JavaScript engines that demand precise grammatical input. Recently, researchers have adopted language models for context-aware mutation in fuzzing to…
Fuzzing is one of the most effective approaches to finding software flaws. However, applying it to microcontroller firmware incurs many challenges. For example, rehosting-based solutions cannot accurately model peripheral behaviors and thus…
As a promising distributed machine learning paradigm, Federated Learning (FL) has attracted increasing attention to deal with data silo problems without compromising user privacy. By adopting the classic one-to-multi training scheme (i.e.,…
Data privacy is a major concern in industries such as healthcare or finance. The requirement to safeguard privacy is essential to prevent data breaches and misuse, which can have severe consequences for individuals and organisations.…
Many energy-aware routing protocols have been proposed for wireless sensor networks. Most of them are only energy savers and do not take care about energy balancing. The energy saver protocols try to decrease the energy consumption of the…
Fuzzing is a popular dynamic program analysis technique used to find vulnerabilities in complex software. Fuzzing involves presenting a target program with crafted malicious input designed to cause crashes, buffer overflows, memory errors,…
Fuzzing is a popular vulnerability automated testing method utilized by professionals and broader community alike. However, despite its abilities, fuzzing is a time-consuming, computationally expensive process. This is problematic for the…
In this paper, we propose a new fuzzy clustering algorithm based on the mode-seeking framework. Given a dataset in $\mathbb{R}^d$, we define regions of high density that we call cluster cores. We then consider a random walk on a…
Fuzzing is a powerful software testing technique renowned for its effectiveness in identifying software vulnerabilities. Traditional fuzzing evaluations typically focus on overall fuzzer performance across a set of target programs, yet few…
To ensure the reliability of DNN systems and address the test generation problem for neural networks, this paper proposes a fuzzing test generation technique based on many-objective optimization algorithms. Traditional fuzz testing employs…
Of coverage-guided fuzzing's three main components: (1) testcase generation, (2) code coverage tracing, and (3) crash triage, code coverage tracing is a dominant source of overhead. Coverage-guided fuzzers trace every testcase's code…
Federated Learning (FL) enables collaborative model training across multiple clients while preserving data privacy. Traditional FL methods often use a global model to fit all clients, assuming that clients' data are independent and…
High scalability and low running costs have made fuzz testing the de facto standard for discovering software bugs. Fuzzing techniques are constantly being improved in a race to build the ultimate bug-finding tool. However, while fuzzing…
FuSeBMC is a test generator for finding security vulnerabilities in C programs. In earlier work [4], we described a previous version that incrementally injected labels to guide Bounded Model Checking (BMC) and Evolutionary Fuzzing engines…
A wireless sensor network consists of several sensor nodes. Sensor nodes collaborate to collect meaningful environmental information and send them to the base station. During these processes, nodes are prone to failure, due to the energy…
The trackers based on lightweight neural networks have achieved great success in the field of aerial remote sensing, most of which aggregate multi-stage deep features to lift the tracking quality. However, existing algorithms usually only…
Active multi-target tracking requires a mobile robot to balance exploration for undetected targets with exploitation of uncertain tracked ones. Diffusion policies have emerged as a powerful approach for capturing diverse behavioral…
Fuzzing continues to be the most effective method for identifying security vulnerabilities in software. In the context of fuzz testing, the fuzzer supplies varied inputs to fuzz targets, which are designed to comprehensively exercise…
This dissertation presents three independent novel approaches for distinct scenarios to solve one or more open challenges. The first concern explains the focus on the lifetime of the networks: this dissertation will utilize a fuzzy…
Federated learning (FL) systems are susceptible to attacks from malicious actors who might attempt to corrupt the training model through various poisoning attacks. FL also poses new challenges in addressing group bias, such as ensuring fair…