本研究基于 Microsoft Windows Defender 的大规模真实数据集，利用机器学习算法与特征工程开发预测模型，以准确预测计算机的恶意软件易感性，为企业环境提供主动防御策略。
摘要：
在网络威胁不断升级的时代，恶意软件对个人和组织构成重大风险，可能导致数据泄露、系统故障和巨额财务损失。本研究通过在从 Microsoft Windows Defender 收集的大量数据集上利用机器学习（ML）技术，解决了对有效恶意软件检测策略的迫切需求。我们的研究旨在开发一个先进的 ML 模型，该模型能够根据单台计算机的特定条件准确预测恶意软件易感性。我们超越了传统的基于签名的检测方法，结合了历史数据和创新的特征工程来增强检测能力。本研究做出了几项贡献：首先，通过采用复杂的 ML 算法推进了现有的恶意软件检测技术；其次，利用大规模真实世界数据集以确保研究结果的适用性；第三，强调了特征分析在识别恶意软件感染关键指标中的重要性；第四，提出了可适用于企业环境的模型，为保护广泛网络免受新兴威胁提供了主动方法。我们旨在提高网络安全韧性，为该领域的从业者提供关键见解，并应对数字环境中恶意软件带来的不断演变的挑战。最后，对结果、见解和结论进行了讨论。
英文摘要：
In an era of escalating cyber threats, malware poses significant risks to individuals and organizations, potentially leading to data breaches, system failures, and substantial financial losses. This study addresses the urgent need for effective malware detection strategies by leveraging Machine Learning (ML) techniques on extensive datasets collected from Microsoft Windows Defender. Our research aims to develop an advanced ML model that accurately predicts malware vulnerabilities based on the specific conditions of individual machines. Moving beyond traditional signature-based detection methods, we incorporate historical data and innovative feature engineering to enhance detection capabilities. This study makes several contributions: first, it advances existing malware detection techniques by employing sophisticated ML algorithms; second, it utilizes a large-scale, real-world dataset to ensure the applicability of findings; third, it highlights the importance of feature analysis in identifying key indicators of malware infections; and fourth, it proposes models that can be adapted for enterprise environments, offering a proactive approach to safeguarding extensive networks against emerging threats. We aim to improve cybersecurity resilience, providing critical insights for practitioners in the field and addressing the evolving challenges posed by malware in a digital landscape. Finally, discussions on results, insights, and conclusions are presented.
The Microsoft Malware Classification Challenge was announced in 2015 along with a publication of a huge dataset of nearly 0.5 terabytes, consisting of disassembly and bytecode of more than 20K malware samples. Apart from serving in the Kaggle competition, the dataset has become a standard benchmark for research on modeling malware behaviour. To date, the dataset has been cited in more than 50 research papers. Here we provide a high-level comparison of the publications citing the dataset. The comparison simplifies finding potential research directions in this field and future performance evalua…
National Institute of Technical Teachers Training and Research（国家技术教师培训与研究所）；BITS（比尔拉科学技术学院）
In today's digital world most of the anti-malware tools are signature based which is ineffective to detect advanced unknown malware viz. metamorphic malware. In this paper, we study the frequency of opcode occurrence to detect unknown malware by using machine learning technique. For the purpose, we have used kaggle Microsoft malware classification challenge dataset. The top 20 features obtained from fisher score, information gain, gain ratio, chi-square and symmetric uncertainty feature selection methods are compared. We also studied multiple classifier available in WEKA GUI based machine lear…
Anti-malware agents typically communicate with their remote services to share information about suspicious files. These remote services use their up-to-date information and global context (view) to help classify the files and instruct their agents to take a predetermined action (e.g., delete or quarantine). In this study, we provide a security analysis of a specific form of communication between anti-malware agents and their services, which takes place entirely over the insecure DNS protocol. These services, which we denote DNS anti-malware list (DNSAML) services, affect the classification of …
Machine learning based solutions have been very helpful in solving problems that deal with immense amounts of data, such as malware detection and classification. However, deep neural networks have been found to be vulnerable to adversarial examples, or inputs that have been purposefully perturbed to result in an incorrect label. Researchers have shown that this vulnerability can be exploited to create evasive malware samples. However, many proposed attacks do not generate an executable and instead generate a feature vector. To fully understand the impact of adversarial examples on malware dete…
Malicious email attachments are a growing delivery vector for malware. While machine learning has been successfully applied to portable executable (PE) malware detection, we ask, can we extend similar approaches to detect malware across heterogeneous file types commonly found in email attachments? In this paper, we explore the feasibility of applying machine learning as a static countermeasure to detect several types of malicious email attachments including Microsoft Office documents and Zip archives. To this end, we collected a dataset of over 5 million malicious/benign Microsoft Office docum…
Army Engineering University of PLA（中国人民解放军陆军工程大学）；National University of Defense Technology（国防科技大学）；University Of Science & Technology Beijing（北京科技大学）；National Computer Network and Information Security Management Center（国家计算机网络与信息安全管理中心）；Shanghai Jiao Tong University（上海交通大学）
Deep neural networks (DNNs) are increasingly being applied in malware detection and their robustness has been widely debated. Traditionally an adversarial example generation scheme relies on either detailed model information (gradient-based methods) or lots of samples to train a surrogate model, neither of which are available in most scenarios. We propose the notion of the instance-based attack. Our scheme is interpretable and can work in a black-box environment. Given a specific binary example and a malware classifier, we use the data augmentation strategies to produce enough data from which …
The purpose of this project was to collect and analyse data about the comparability and real-life applicability of published results focusing on Microsoft Windows malware, more specifically the impact of dataset size and testing dataset imbalance on measured detector performance. Some researchers use smaller datasets, and if dataset size has a significant impact on performance, that makes comparison of the published results difficult. Researchers also tend to use balanced datasets and accuracy as a metric for testing. The former is not a true representation of reality, where benign samples sig…
Towards Certified Malware Detection: Provable Guarantees Against Evasion Attacks
Department of Computer Applications, Cochin University of Science and Technology（计算机应用系，科钦大学科学与技术学院）；Department of Electrical, Computer and Biomedical Engineering, University of Pavia（电气、计算机与生物医学工程系，帕维亚大学）
A number of important applied problems in engineering, finance and medicine can be formulated as a problem of anomaly detection. A classical approach to the problem is to describe a normal state using a one-class support vector machine. Then to detect anomalies we quantify a distance from a new observation to the constructed description of the normal class. In this paper we present a new approach to the one-class classification. We formulate a new problem statement and a corresponding algorithm that allow taking into account a privileged information during the training phase. We evaluate perfo…
Machine-learning methods have already been exploited as useful tools for detecting malicious executable files. They leverage data retrieved from malware samples, such as header fields, instruction sequences, or even raw bytes, to learn models that discriminate between benign and malicious software. However, it has also been shown that machine learning and deep neural networks can be fooled by evasion attacks (also referred to as adversarial examples), i.e., small changes to the input data that cause misclassification at test time. In this work, we investigate the vulnerability of malware detec…
University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）；Oak Ridge National Laboratory（橡树岭国家实验室）
Criminals use malware to disrupt cyber-systems. The number of these malware-vulnerable systems is increasing quickly as common systems, such as vehicles, routers, and lightbulbs, become increasingly interconnected cyber-systems. To address the scale of this problem, analysts divide malware into classes and develop, for each class, a specialized defense. In this project we classified malware with machine learning. In particular, we used a supervised multi-class long short term memory (LSTM) model. We trained the algorithm with thousands of malware files annotated with class labels (the training…
Machine learning-based systems for malware detection operate in a hostile environment. Consequently, adversaries will also target the learning system and use evasion attacks to bypass the detection of malware. In this paper, we outline our learning-based system PEberus that got the first place in the defender challenge of the Microsoft Evasion Competition, resisting a variety of attacks from independent attackers. Our system combines multiple, diverse defenses: we address the semantic gap, use various classification models, and apply a stateful defense. This competition gives us the unique opp…
Unraveling the Key of Machine Learning-based Android Malware Detection
National University of Singapore（新加坡国立大学）；University College London（伦敦大学学院）；The State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全国家重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）
Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification
Department of Computer Science and Engineering, Hamad Bin Khalifa University（计算机科学与工程系，哈马德·本·卡西姆大学）；Qatar Computing Research Institute, Hamad Bin Khalifa University（卡塔尔计算研究所，哈马德·本·卡西姆大学）
Windows OS is facing a huge rise in kernel attacks. An overview of popular techniques that result in loading kernel drivers will be presented. One of the key targets of modern threats is disabling and blinding Microsoft Defender, a default Windows AV. The analysis of recent driver-based attacks will be given, the challenge is to block them. The survey of user- and kernel-level attacks on Microsoft Defender will be given. One of the recently published attackers techniques abuses Mandatory Integrity Control (MIC) and Security Reference Monitor (SRM) by modifying Integrity Level and Debug Privile…
Macquarie University（麦考瑞大学）；University of Sydney（悉尼大学）
The vulnerability of smartphones to cyberattacks has been a severe concern to users arising from the integrity of installed applications (\textit{apps}). Although applications are to provide legitimate and diversified on-the-go services, harmful and dangerous ones have also uncovered the feasible way to penetrate smartphones for malicious behaviors. Thorough application analysis is key to revealing malicious intent and providing more insights into the application behavior for security risk assessments. Such in-depth analysis motivates employing deep neural networks (DNNs) for a set of features…
University of California at Santa Cruz（加州大学圣克鲁兹分校）；Microsoft Research（微软研究院）；Microsoft Corp.（微软公司）
Malicious software, or malware, presents a continuously evolving challenge in computer security. These embedded snippets of code in the form of malicious files or hidden within legitimate files cause a major risk to systems with their ability to run malicious command sequences. Malware authors even use polymorphism to reorder these commands and create several malicious variations. However, if executed in a secure environment, one can perform early malware detection on emulated command sequences. The models presented in this paper leverage this sequential data derived via emulation in order to …
Android malware is a persistent threat to billions of users around the world. As a countermeasure, Android malware detection systems are occasionally implemented. However, these systems are often vulnerable to \emph{evasion attacks}, in which an adversary manipulates malicious instances so that they are misidentified as benign. In this paper, we launch various innovative evasion attacks against several Android malware detection systems. The vulnerability inherent to all of these systems is that they are part of Androguard~\cite{desnos2011androguard}, a popular open source library used in Andro…
Current state-of-the-art research for tackling the problem of malware detection and classification is centered on the design, implementation and deployment of systems powered by machine learning because of its ability to generalize to never-before-seen malware families and polymorphic mutations. However, it has been shown that machine learning models, in particular deep neural networks, lack robustness against crafted inputs (adversarial examples). In this work, we have investigated the vulnerability of a state-of-the-art shallow convolutional neural network malware classifier against the dead…
Nowadays most of the malware applications are either packed or protected. This techniques are applied especially to evade signature based detectors and also to complicate the job of reverse engineers or security analysts. The time one must spend on unpacking or decrypting malware layers is often very long and in fact remains the most complicated task in the overall process of malware analysis. In this report author proposes MmmBop as a relatively new concept of using dynamic binary instrumentation techniques for unpacking and bypassing detection by self-modifying and highly aggressive packed b…
During the past four years, Flash malware has become one of the most insidious threats to detect, with almost 600 critical vulnerabilities targeting Adobe Flash disclosed in the wild. Research has shown that machine learning can be successfully used to detect Flash malware by leveraging static analysis to extract information from the structure of the file or its bytecode. However, the robustness of Flash malware detectors against well-crafted evasion attempts - also known as adversarial examples - has never been investigated. In this paper, we propose a security evaluation of a novel, represen…
Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies
Virelya Intelligence Research Labs（Virelya智能研究实验室）；Google（谷歌）
Orange Innovation（Orange创新研究院）；University of Caen Normandy（卡昂诺曼底大学）
In addition to signature-based and heuristics-based detection techniques, machine learning (ML) is widely used to generalize to new, never-before-seen malicious software (malware). However, it has been demonstrated that ML models can be fooled by tricking the classifier into returning the incorrect label. These studies, for instance, usually rely on a prediction score that is fragile to gradient-based attacks. In the context of a more realistic situation where an attacker has very little information about the outputs of a malware detection engine, modest evasion rates are achieved. In this pap…
Malware attacks are costly. To mitigate against such attacks, organizations deploy malware detection tools that help them detect and eventually resolve those threats. While running only the best available tool does not provide enough coverage of the potential attacks, running all available tools is prohibitively expensive in terms of financial cost and computing resources. Therefore, an organization typically runs a set of tools that maximizes their coverage given a limited budget. However, how should an organization choose that set? Attackers are strategic, and will change their behavior to p…
Due to the completely open-source nature of Android, the exploitable vulnerability of malware attacks is increasing. Machine learning, leading to a great evolution in Android malware detection in recent years, is typically applied in the classification phase. Since the correlation between features is ignored in some traditional ranking-based feature selection algorithms, applying wrapper-based feature selection models is a topic worth investigating. Though considering the correlation between features, wrapper-based approaches are time-consuming for exploring all possible valid feature subsets …
The wide acceptance of Internet of Things (IoT) for both household and industrial applications is accompanied by several security concerns. A major security concern is their probable abuse by adversaries towards their malicious intent. Understanding and analyzing IoT malicious behaviors is crucial, especially with their rapid growth and adoption in wide-range of applications. However, recent studies have shown that machine learning-based approaches are susceptible to adversarial attacks by adding junk codes to the binaries, for example, with an intention to fool those machine learning or deep …
Machine-learning techniques are widely used in security-related applications, like spam and malware detection. However, in such settings, they have been shown to be vulnerable to adversarial attacks, including the deliberate manipulation of data at test time to evade detection. In this work, we focus on the vulnerability of linear classifiers to evasion attacks. This can be considered a relevant problem, as linear classifiers have been increasingly used in embedded systems and mobile devices for their low processing time and memory requirements. We exploit recent findings in robust optimizatio…
Shiraz University of Technology（设拉子理工大学）；University of Padua（帕多瓦大学）
The widespread adoption of smartphones dramatically increases the risk of attacks and the spread of mobile malware, especially on the Android platform. Machine learning-based solutions have been already used as a tool to supersede signature-based anti-malware systems. However, malware authors leverage features from malicious and legitimate samples to estimate statistical difference in-order to create adversarial examples. Hence, to evaluate the vulnerability of machine learning algorithms in malware detection, we propose five different attack scenarios to perturb malicious applications (apps).…
Enhancing Continual Learning for Software Vulnerability Prediction: Addressing Catastrophic Forgetting via Hybrid-Confidence-Aware Selective Replay for Temporal LLM Fine-Tuning
School of Computer Science, University of Nottingham, Nottingham, United Kingdom（诺丁汉大学计算机科学学院）；School of Informatics and Computing, Northern Arizona University, Flagstaff, United States of America（北亚利桑那大学信息学院）；School of Computer Science, University of Nottingham, Ningbo, China（诺丁汉大学宁波校区）
Is Function Similarity Over-Engineered? Building a Benchmark
Booz Allen Hamilton（博兹·艾伦·汉密尔顿公司）；Syracuse University（雪城大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩分校）；Laboratory for Physical Sciences（物理科学实验室）
