Guanjun Lin

• Sanming University

In this study, an improved version of Aquila Optimizer (AO) known as EHAOMPA has been developed by using the Marine Predators Algorithm (MPA). MPA is a recent and well-behaved optimizer with a unique memory saving and FADs mechanism. At the same time, it suffers from various defects such as inadequate global search, sluggish convergence, and stagna...

In large-scale industrial and agricultural production environments, many nodes of the Internet of Things (IoT) were deployed. These nodes are of various types and contain a wide range of domain-related themes, and their distribution is of theme and hierarchy. Starting from the thematic and hierarchical nature of the IoT, this paper proposes the con...

Detecting vulnerabilities in programs is an important yet challenging problem in cybersecurity. The recent advancement in techniques of natural language understanding enables the data-driven research on automated code analysis to embrace Pre-trained Contextualized Models (PCMs). These models are pre-trained on the large corpus and can be fine-tuned...

To date, being benefited from the ability of automated feature extraction and the performance of software vulnerability identification, deep learning techniques have attracted extensive attention in data-driven software vulnerability detection. Many methods based on deep learning have been proposed to speed up and intelligentize the process of vuln...

Software vulnerability is a fundamental problem in cybersecurity, which poses severe threats to the secure operation of devices and systems. In this paper, we propose a new vulnerability detection framework of employing advanced neural embedding. For example, CodeBERT is a large‐scale pre‐trained embedding model for natural language and programming...

Due to multitudinous vulnerabilities in sophisticated software programs, the detection performance of existing approaches requires further improvement. Multiple vulnerability detection approaches have been proposed to aid code inspection. Among them, there is a line of approaches that apply deep learning (DL) techniques and achieve promising result...

Exploitable vulnerabilities in software systems are major security concerns. To date, machine learning (ML) based solutions have been proposed to automate and accelerate the detection of vulnerabilities. Most ML techniques aim to isolate a unit of source code, be it a line or a function, as being vulnerable. We argue that a code segment is vulnerab...

Detecting source-code level vulnerabilities at the development phase is a cost-effective solution to prevent potential attacks from happening at the software deployment stage. Many machine learning, including deep learning-based solutions, have been proposed to aid the process of vulnerability discovery. However, these approaches were mainly evalua...

To date, the Medical Internet of Things (MIoT) technology has been recognized and widely applied due to its convenience and practicality. The MIoT enables the application of machine learning to predict diseases of various kinds automatically and accurately, assisting and facilitating effective and efficient medical treatment. However, the MIoT are...

As a key service of the future 6G network, healthcare digital twin is the virtual replica of a person, which employs Internet of Things (IoT) technologies and AI-powered models to predict the state of health and provide suggestions to a range of clinical questions. To support healthcare digital twins, the right cyber resilience technologies and pol...

The constantly increasing number of disclosed security vulnerabilities have become an important concern in the software industry and in the field of cybersecurity, suggesting that the current approaches for vulnerability detection demand further improvement. The booming of the open-source software community has made vast amounts of software code av...

The popularity of social media networks, such as Twitter, leads to an increasing number of spamming activities. Researchers employed various machine learning methods to detect Twitter spam. However, majorities of existing researches are limited to theoretically study, few of them can apply detection techniques to real-time scenario. In this paper,...

A major cause of security incidents such as cyber attacks is rooted in software vulnerabilities. These vulnerabilities should ideally be found and fixed before the code gets deployed. Machine learning-based approaches achieve state-of-the-art performance in capturing vulnerabilities. These methods are predominantly supervised. Their prediction mode...

The application of Deep Learning (DL) technique for code analysis enables the rich and latent patterns within software code to be revealed, facilitating various downstream tasks such as the software defect and vulnerability detection. Many DL architectures have been applied for identifying vulnerable code segments in recent literature. However, the...

Software vulnerability has long been an important but critical research issue in cybersecurity. Recently, the machine learning (ML) based approach has attracted increasing interest in the research of software vulnerability detection. However, the detection performance of existing ML-based methods require further improvement. There are two challenge...

Machine learning (ML) has great potential in automated code vulnerability discovery. However, automated discovery application driven by off-the-shelf machine learning tools often performs poorly due to the shortage of high-quality training data. The scarceness of vulnerability data is almost always a problem for any developing software project duri...

Machine learning is now widely used to detect security vulnerabilities in software, even before the software is released. But its potential is often severely compromised at the early stage of a software project, when we face a shortage of high-quality training data and have to rely on overly generic hand-crafted features. This paper addresses this...

In cybersecurity, vulnerability discovery in source code is a fundamental problem. To automate vulnerability discovery, Machine learning (ML) based techniques has attracted tremendous attention. However, existing ML-based techniques focus on the component or file level detection, and thus considerable human effort is still required to pinpoint the...

With the trend that the Internet becoming more accessible and our devices being more mobile, people are spending an increasing amount of time on social networks. However, due to the popularity of online social networks, cyber criminals are spamming on these platforms for potential victims. The spams lure users to external phishing sites or malware...