Malicious domains are increasingly common and pose a severe cybersecurity threat. Specifically, many types of current cyber attacks use URLs for attack communications (e.g., C\&C, phishing, and spear-phishing). Despite the continuous progress in detecting these attacks, many alarming problems remain open, such as the weak spots of the defense mechanisms. Since machine learning has become one of the most prominent methods of malware detection, A robust feature selection mechanism is proposed that results in malicious domain detection models that are resistant to evasion attacks. This mechanism exhibits high performance based on empirical data. This paper makes two main contributions: First, it provides an analysis of robust feature selection based on widely used features in the literature. Note that even though the feature set dimensional space is reduced by half (from nine to four features), the performance of the classifier is still improved (an increase in the model's F1-score from 92.92\% to 95.81\%). Second, it introduces novel features that are robust to the adversary's manipulation. Based on an extensive evaluation of the different feature sets and commonly used classification models, this paper shows that models that are based on robust features are resistant to malicious perturbations, and at the same time useful for classifying non-manipulated data.
A note on "achieving security, robust cheating resistance, and high-efficiency for outsourcing large matrix multiplication computation to a malicious cloud"
Different types of malicious activities have been flagged in multiple permissionless blockchains such as bitcoin, Ethereum etc. While some malicious activities exploit vulnerabilities in the infrastructure of the blockchain, some target its users through social engineering techniques. To address these problems, we aim at automatically flagging blockchain accounts that originate such malicious exploitation of accounts of other participants. To that end, we identify a robust supervised machine learning (ML) algorithm that is resistant to any bias induced by an over representation of certain mali…
Open-source software supply chain attacks aim at infecting downstream users by poisoning open-source packages. The common way of consuming such artifacts is through package repositories and the development of vetting strategies to detect such attacks is ongoing research. Despite its popularity, the Java ecosystem is the less explored one in the context of supply chain attacks. In this paper we present indicators of malicious behavior that can be observed statically through the analysis of Java bytecode. Then we evaluate how such indicators and their combinations perform when detecting maliciou…
University of Science and Technology of China（中国科学技术大学）；Duke University（杜克大学）
Federated learning (FL) is vulnerable to model poisoning attacks, in which malicious clients corrupt the global model via sending manipulated model updates to the server. Existing defenses mainly rely on Byzantine-robust FL methods, which aim to learn an accurate global model even if some clients are malicious. However, they can only resist a small number of malicious clients in practice. It is still an open challenge how to defend against model poisoning attacks with a large number of malicious clients. Our FLDetector addresses this challenge via detecting malicious clients. FLDetector aims t…
Robust and Actively Secure Serverless Collaborative Learning
Northwestern University（西北大学）；CISPA（CISPA亥姆霍兹信息安全中心）；Google（谷歌）；University of Toronto and Vector Institute（多伦多大学与向量研究所）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
List-Decodable Byzantine Robust PIR: Lower Communication Complexity, Higher Byzantine Tolerance, Smaller List Size
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；School of Physical and Mathematical Sciences, Nanyang Technological University（南洋理工大学物理与数学科学学院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Tsinghua University（清华大学）；Beijing Institute of Technology（北京理工大学）；Peng Cheng Laboratory（鹏城实验室）
Machine learning (ML) based malicious traffic detection is an emerging security paradigm, particularly for zero-day attack detection, which is complementary to existing rule based detection. However, the existing ML based detection has low detection accuracy and low throughput incurred by inefficient traffic features extraction. Thus, they cannot detect attacks in realtime especially in high throughput networks. Particularly, these detection systems similar to the existing rule based detection can be easily evaded by sophisticated attacks. To this end, we propose Whisper, a realtime ML based m…
Blockchains are intended to be immutable, so an attacker who is able to delete transactions through a chain reorganization (a malicious reorg) can perform a profitable double-spend attack. We study the rate at which an attacker can execute reorgs in the Tezos Proof-of-Stake protocol. As an example, an attacker with 40% of the staking power is able to execute a 20-block malicious reorg at an average rate of once per day, and the attack probability increases super-linearly as the staking power grows beyond 40%. Moreover, an attacker of the Tezos protocol knows in advance when an attack opportuni…
In this paper, we present MORTON, a method that identifies compromised devices in enterprise networks based on the existence of routine DNS communication between devices and disreputable host names. With its compact representation of the input data and use of efficient signal processing and a neural network for classification, MORTON is designed to be accurate, robust, and scalable. We evaluate MORTON using a large dataset of corporate DNS logs and compare it with two recently proposed beaconing detection methods aimed at detecting malware communication. The results demonstrate that while MORT…
The hypothesis here states that neural network algorithms such as Multi-layer Perceptron (MLP) have higher accuracy in differentiating malicious and semi-structured phishing URLs. Compared to classical machine learning algorithms such as Logistic Regression and Multinomial Naive Bayes, the classical algorithms rely heavily on substantial corpus data training and machine learning experts' domain knowledge to perform complex feature engineering. MLP could perform non-linear separable multi-classes classification and focus less on corpus feature training. In addition, backpropagation weight adjus…
FLTG: Byzantine-Robust Federated Learning via Angle-Based Defense and Non-IID-Aware Weighting
Hunan University of Technology and Business（湖南工程大学）；Xiangjiang Laboratory（湘江实验室）；Shenzhen Institute for Advanced Study（深圳先进研究 institute）；University of Electronic Science and Technology of China（电子科技大学）
Machine Learning (ML) has proven to be effective in many application domains. However, ML methods can be vulnerable to adversarial attacks, in which an attacker tries to fool the classification/prediction mechanism by crafting the input data. In the case of ML-based Network Intrusion Detection Systems (NIDSs), the attacker might use their knowledge of the intrusion detection logic to generate malicious traffic that remains undetected. One way to solve this issue is to adopt adversarial training, in which the training set is augmented with adversarial traffic samples. This paper presents an adv…
Laboratory for Advanced Cybersecurity Research（高级网络安全研究实验室）；National Security Agency（国家安全局）
Rule-based IDS (intrusion detection systems) are being replaced by more robust neural IDS, which demonstrate great potential in the field of Cybersecurity. However, these ML approaches continue to rely on ad-hoc feature engineering techniques, which lack the capacity to vectorize inputs in ways that are fully relevant to the discovery of anomalous cyber activity. We propose a deep end-to-end framework with NLP-inspired components for identifying potentially malicious behaviors on enterprise computer networks. We also demonstrate the efficacy of this technique on the recently released DARPA OpT…
EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
School of Cyberspace Security, Beijing University of Posts and Telecommunications（信息安全部门，北京邮电大学）；School of Computer Science and Technology, Huazhong University of Science and Technology（计算机科学与技术学院，华中科技大学）；Department of Electronic Engineering, Tsinghua University（电子工程系，清华大学）
Ruhr University Bochum（波鸿鲁尔大学）；imec-COSIC KU Leuven（鲁汶大学imec-COSIC研究中心）
In this paper we present LiM ("Less is More"), a malware classification framework that leverages Federated Learning to detect and classify malicious apps in a privacy-respecting manner. Information about newly installed apps is kept locally on users' devices, so that the provider cannot infer which apps were installed by users. At the same time, input from all users is taken into account in the federated learning process and they all benefit from better classification performance. A key challenge of this setting is that users do not have access to the ground truth (i.e. they cannot correctly i…
SafeSpeech: Robust and Universal Voice Protection Against Malicious Speech Synthesis
Beijing University of Posts and Telecommunications（北京邮电大学）；CSIRO’s Data61（联邦科学与工业研究组织数据61）；National University of Singapore（新加坡国立大学）；The University of Hong Kong（香港大学）
Laboratory for Advanced Cybersecurity Research, National Security Agency（国家安全局高级网络安全研究实验室）
Intrusion Detection Systems (IDS) enhanced with Machine Learning (ML) have demonstrated the capacity to efficiently build a prototype of "normal" cyber behaviors in order to detect cyber threats' activity with greater accuracy than traditional rule-based IDS. Because these are largely black boxes, their acceptance requires proof of robustness to stealthy adversaries. Since it is impossible to build a baseline from activity completely clean of that of malicious cyber actors (outside of controlled experiments), the training data for deployed models will be poisoned with examples of activity that…
Detecting Malicious Concepts without Image Generation in AI-Generated Content (AIGC)
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics（江西财经大学计算机与人工智能学院）；Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing（江西省多媒体智能处理重点实验室）；Department of Data Science, City University of Hong Kong（香港城市大学数据科学系）
Federated Learning (FL) has received increasing attention due to its privacy protection capability. However, the base algorithm FedAvg is vulnerable when it suffers from so-called backdoor attacks. Former researchers proposed several robust aggregation methods. Unfortunately, many of these aggregation methods are unable to defend against backdoor attacks. What's more, the attackers recently have proposed some hiding methods that further improve backdoor attacks' stealthiness, making all the existing robust aggregation methods fail. To tackle the threat of backdoor attacks, we propose a new agg…
