Article
Computer Science, Interdisciplinary Applications
C. Catalano, A. Chezzi, M. Angelelli, F. Tommasi
Summary: The study critically analyzes the strengths and weaknesses of using CNN for static malware detection, starting from the conversion of binary executable files to pixel images. It aims to achieve fast and accurate malware classification by relying solely on the binary content of the file.
COMPUTERS IN INDUSTRY
(2022)
Article
Computer Science, Theory & Methods
Aqib Rashid, Jose Such
Summary: ML models are vulnerable to adversarial query attacks, and this paper presents a stateful defense system called MalProtect that can reduce the evasion rate of adversarial attacks in the malware detection domain.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Telecommunications
Neha Bala, Aemun Ahmar, Wenjia Li, Fernanda Tovar, Arpit Battu, Prachi Bambarkar
Summary: In recent years, there has been a significant increase in mobile devices, particularly those based on the Android operating system. This popularity has made them a prime target for mobile malware, posing risks to privacy and property. Researchers have made efforts to develop new and effective detection mechanisms to combat the growing security issues caused by mobile malware. However, malware authors have started to employ adversarial example attacks to evade detection. This paper investigates various types of adversarial example attacks and proposes a feasible approach to counter them. Experimental results indicate a significant degradation in Android malware detection performance when facing these attacks.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
Article
Computer Science, Information Systems
Aqib Rashid, Jose Such
Summary: This paper introduces StratDef, a strategic defense system based on a moving target defense approach, for machine learning-based malware detection. StratDef dynamically selects the best models and strategically uses them to maximize adversarial robustness. Comprehensive evaluations show that StratDef outperforms other defenses in facing adversarial threats.
COMPUTERS & SECURITY
(2023)
Article
Computer Science, Information Systems
Xiang Ling, Lingfei Wu, Jiangyu Zhang, Zhenqing Qu, Wei Deng, Xiang Chen, Yaguan Qian, Chunming Wu, Shouling Ji, Tianyue Luo, Jingzheng Wu, Yanjun Wu
Summary: Malware poses significant threats to computers, and efforts have been made to propose various detection methods. However, machine learning and deep learning models are vulnerable to adversarial attacks. This paper focuses on adversarial attacks against Windows PE malware and reviews the current attacks and defenses.
COMPUTERS & SECURITY
(2023)
Article
Computer Science, Information Systems
Hemant Rathore, Sanjay K. Sahay, Piyush Nikam, Mohit Sewak
Summary: The study proposed two novel attack strategies against Android malware detection systems, ultimately achieving the goal of increasing the fooling rate by making minimum modifications to the detection models. The research demonstrates that the proposed Android malware detection system using reinforcement learning is more robust against adversarial attacks.
INFORMATION SYSTEMS FRONTIERS
(2021)
Article
Computer Science, Artificial Intelligence
Lukasz Korycki, Bartosz Krawczyk
Summary: This paper proposes a framework for robust concept drift detection in the presence of adversarial and poisoning attacks. It introduces a taxonomy for two types of adversarial concept drifts and a robust trainable drift detector. Extensive computational experiments prove the high robustness and efficacy of the proposed framework in adversarial scenarios.
Article
Computer Science, Information Systems
Hemant Rathore, Adithya Samavedhi, Sanjay K. Sahay, Mohit Sewak
Summary: The android ecosystem has experienced significant growth, but the increase in android malware poses a threat. This study examines the adversarial robustness of twenty-four malware detection models and proposes defense strategies to enhance their resilience against attacks.
INFORMATION SYSTEMS FRONTIERS
(2023)
Article
Computer Science, Artificial Intelligence
Fahri Anil Yerlikaya, Serif Bahtiyar
Summary: The popularity of machine learning technology has been growing rapidly in the past decade, and it has made significant contributions to various fields. This paper empirically analyzes the performance and robustness of six machine learning algorithms against different types of adversarial attacks, with results showing that different algorithms exhibit varying performances and robustness under different attack scenarios.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Theory & Methods
Zhibo Wang, Jingjing Ma, Xue Wang, Jiahui Hu, Zhan Qin, Kui Ren
Summary: Machine learning has been widely used in various fields for automated decisions, but outsourced ML training increases the risk of attacks. A prime threat is poisoning attack, where adversaries try to subvert machine learning systems by contaminating training data or other forms of interference. This survey summarizes and categorizes existing attack methods and defenses, and demonstrates attractive application scenarios, providing a unified framework to analyze poisoning attacks.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Artificial Intelligence
Hemant Rathore, Animesh Sasan, Sanjay K. Sahay, Mohit Sewak
Summary: This study validates the vulnerability of machine learning-based malware detection models to adversarial samples and proposes countermeasures to improve their accuracy and resistance. The proposed MalDQN agent achieves a high fooling rate and reduces the accuracy of the malware detection models. The defensive strategies significantly enhance the capability of the models to detect and resist adversarial applications.
PATTERN RECOGNITION LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Dengpan Ye, Chuanxi Chen, Changrui Liu, Hao Wang, Shunzhi Jiang
Summary: This paper discusses the saliency map method for enhancing model interpretability, as well as a novel approach combined with additional noises and inconsistency strategy to detect adversarial examples. Experimental results demonstrate that the proposed method effectively detects adversarial attacks with high success rate across common datasets and models, showing its generality compared to existing state-of-the-art techniques.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Information Systems
A. Jyothish, Ashik Mathew, P. Vinod
Summary: Android is the most targeted mobile operating system for malware attacks. Most modern anti-malware solutions largely incorporate deep learning or machine learning techniques to detect malwares. In this paper, we conducted a comprehensive analysis on the abilities of 10 deep learning and 5 machine learning classifiers to identify Android malware applications. Among the different classifiers, XGBoost with 2-gram dataset showed the highest F1-score of 0.98, and the extreme learning machine with the system call images demonstrated the best F1-score of 0.952.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Deqiang Li, Qianmu Li, Yanfang (Fanny) Ye, Shouhuai Xu
Summary: In this article, the field of Adversarial Malware Detection (AMD) is surveyed and systematized through a unified conceptual framework. The article provides insights into the attack-defense arms race in the AMD context and discusses several future research directions.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Hardware & Architecture
Abraham Peedikayil Kuruvila, Shamik Kundu, Kanad Basu
Summary: In the era of Internet of Things, researchers have proposed hardware-assisted Malware detection using hardware performance counters and machine learning classifiers to distinguish Malware from benign programs. A moving target defense strategy has been introduced to counter adversarial attacks, by training multiple classifiers on different sets of hardware performance counters to confuse attackers and improve classification accuracy.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2021)
Article
Computer Science, Hardware & Architecture
Huaxin Li, Qingrong Chen, Haojin Zhu, Di Ma, Hong Wen, Xuemin (Sherman) Shen
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2020)
Article
Computer Science, Information Systems
Yan Meng, Jinlei Li, Haojin Zhu, Xiaohui Liang, Yao Liu, Na Ruan
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2020)
Article
Computer Science, Theory & Methods
Yaping Liu, Shuo Zhang, Haojin Zhu, Peng-Jun Wan, Lixin Gao, Yaoxue Zhang, Zhihong Tian
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2020)
Article
Computer Science, Information Systems
Lichuan Ma, Qingqi Pei, Lu Zhou, Haojin Zhu, Licheng Wang, Yusheng Ji
Summary: The study proposed a federated data cleaning protocol, FedClean, for edge intelligence scenarios to achieve data cleaning without compromising data privacy. By generating Boolean shares of data and privately computing AVF scores, abnormal data entries are filtered out through a bitonic sorting network.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Yiting Qu, Suguo Du, Shaofeng Li, Yan Meng, Le Zhang, Haojin Zhu
Summary: The study introduces an automatic permission optimization framework called Permizer to recommend different app permission configurations to users with varying privacy preferences. Permizer is the first module to achieve a balance between privacy protection and app functionality under personal privacy preference conditions.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Jing Xu, Kai Xing, Chi Zhang, Shuo Zhang, Zhonghu Xu, Chunlin Zhong, Haojin Zhu, Zheng Yang, Yunhao Liu
Summary: This article discusses the threat of clone attacks in the Internet of Things and proposes a solution for global topology and identity tracing through a localized computing paradigm, which can reduce communication, storage, and computation overhead while ensuring deterministic detection.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Hardware & Architecture
Edwin Yang, Song Fang, Ian Markwood, Yao Liu, Shangqing Zhao, Zhuo Lu, Haojin Zhu
Summary: This research identifies a new type of keystroke eavesdropping attack that does not require a training phase, and proposes a defense mechanism against it. The attack is based on channel state information extracted from wireless signals, and establishes a mapping between observed environmental changes and dictionary word structures to eavesdrop on keystrokes.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2022)
Article
Automation & Control Systems
Jiachun Li, Yan Meng, Lichuan Ma, Suguo Du, Haojin Zhu, Qingqi Pei, Xuemin Shen
Summary: The rapid development of smart healthcare system has made early-stage detection of dementia more user-friendly and affordable, but the concern of potential privacy leakage remains. This article presents ADDetector, a convenient and privacy-preserving system designed with the assistance of IoT devices and security mechanisms, taking Alzheimer's disease as an example. ADDetector utilizes audio collected by widely deployed IoT devices in smart home environment, and employs novel topic-based linguistic features to improve detection accuracy. To address privacy breaches in data, feature, and model levels, ADDetector adopts a unique three-layer architecture and incorporates federated learning, differential privacy, and cryptography-based aggregation mechanisms. Evaluation results demonstrate ADDetector's high accuracy of 81.9% and low time overhead of 0.7s with all privacy-preserving mechanisms implemented.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Review
Computer Science, Information Systems
Shaofeng Li, Tian Dong, Benjamin Zhao, Jason Xue, Suguo Du, Haojin Zhu
IEEE SECURITY & PRIVACY
(2022)
Article
Engineering, Electrical & Electronic
Jiachun Li, Weijiong Zhang, Yan Meng, Shaofeng Li, Lichuan Ma, Zhen Liu, Haojin Zhu
Summary: With the development of 5G and other communication techniques, the space-air-ground integrated network (SAGIN) is considered a promising solution to provide wide-range, cost-effective, and real-time wireless access. However, unmanned aerial vehicle (UAV) tracking still faces challenges, especially in the presence of malicious attackers. In this study, a secure object tracking system named SecTracker is proposed to enhance the security and efficiency of SAGIN, addressing the issues of message spoofing and routing misbehavior attacks.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Proceedings Paper
Computer Science, Information Systems
Xin Yin, Zhen Liu, Guomin Yang, Guoxing Chen, Haojin Zhu
Summary: Cryptocurrency has experienced rapid development in the past decade, with digital wallets serving as the primary means for public access to cryptocurrency assets. Hierarchical Deterministic Wallet (HDW) and Stealth Address (SA) have gained significant attention and usage in the community due to their convenience and privacy features. However, there is currently no secure wallet algorithm that combines the virtues of both HDW and SA. This study comprehensively investigates HDW and SA, defines the syntax and security models of a Hierarchical Deterministic Wallet supporting Stealth Address (HDWSA), and proposes a specific construction that is proven to be secure in the random oracle model.
COMPUTER SECURITY - ESORICS 2022, PT I
(2022)
Proceedings Paper
Computer Science, Information Systems
Aoting Hu, Renjie Xie, Zhigang Lu, Aiqun Hu, Minhui Xue
Summary: This paper introduces a novel Membership Collision Attack against GANs (TableGAN-MCA) that successfully recovers partial GAN training data. Our experimental evaluations reveal five main findings, including the recovery rate on real-world datasets, the size of GAN training data, GAN training epochs, and the number of synthetic samples available to the adversary, all positively correlated to the success of TableGAN-MCA.
CCS '21: PROCEEDINGS OF THE 2021 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY
(2021)
Proceedings Paper
Computer Science, Information Systems
Zhushou Tang, Ke Tang, Minhui Xue, Yuan Tian, Sen Chen, Muhammad Ikram, Tielei Wang, Haojin Zhu
PROCEEDINGS OF THE 29TH USENIX SECURITY SYMPOSIUM
(2020)
Proceedings Paper
Computer Science, Hardware & Architecture
Lei Zhang, Yan Meng, Jiahao Yu, Chong Xiang, Brandon Falk, Haojin Zhu
IEEE INFOCOM 2020 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS
(2020)
Article
Computer Science, Information Systems
Kashan Ahmed, Syed Khaldoon Khurshid, Sadaf Hina
Summary: This paper mainly introduces the construction of the cyber threat intelligence knowledge graph and the information extraction technique. By using joint extraction technique, it solves the problem of traditional techniques becoming ineffective due to the increasing size of CTI data. Experimental results show that this technique outperforms state-of-the-art models in knowledge triple extraction on CTI data and improves the F1 score.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Xinlong He, Yang Xu, Sicong Zhang, Weida Xu, Jiale Yan
Summary: This paper proposes a new membership inference attack method in federated learning, which utilizes data poisoning and sequence prediction confidence. The attack is effective and results in minimal overall model performance degradation.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Tieming Chen, Huan Zeng, Mingqi Lv, Tiantian Zhu
Summary: In this paper, the authors propose a deep learning based dynamic malware detection method called CTIMD, which integrates threat knowledge from CTIs into the learning process of API call sequences with runtime parameters. Experimental results show that CTIMD outperforms existing methods in terms of performance.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Wonwoo Choi, Minjae Seo, Seongman Lee, Brent Byunghoon Kang
Summary: This paper proposes SUM, a backward-edge control flow protection scheme for ARM Cortex-M processors. It combines MPU and the overlooked hardware feature FaultMask to achieve efficient and robust protection. The empirical evaluation shows minimal runtime overhead for the proposed solution.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Liliana Ribeiro, Ines Sousa Guedes, Carla Sofia Cardoso
Summary: Phishing susceptibility is influenced by individual and contextual factors. The study found that individuals who perceive themselves as capable of detecting phishing and those who use online services more frequently are more susceptible to phishing. However, technology competencies and other individual variables do not predict phishing susceptibility.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Wenjie Wang, Yuanhai Shao, Yiju Wang
Summary: In this paper, we investigate the adversarial perturbations of twin support vector machines (TWSVMs) and propose an optimization framework, which provides explicit solutions to increase the interpretability of the conclusion and convenience for calculation.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Snofy D. Dunston, V. Mary Anita Rajam
Summary: This paper proposes a novel adversarial attack technique that can synthesize adversarial images to mislead deep learning models, and also studies interpretability plots. The research findings show that the proposed attack technique influences the interpretability plots, regardless of the success of the attack.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Junchen Li, Guang Cheng, Zongyao Chen, Peng Zhao
Summary: Protocol Reverse Engineering (PRE) is a direct approach for analyzing unknown traffic. This paper proposes a method for clustering unknown traffic based on private protocol labels, and the experimental results demonstrate its advantages on real-world network traffic.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Rafal Kozik, Massimo Ficco, Aleksandra Pawlicka, Marek Pawlicki, Francesco Palmieri, Michal Choras
Summary: The inclusion of Explainability of Artificial Intelligence (xAI) has become a mandatory requirement for designing and implementing reliable, interpretable, and ethical AI solutions. However, it has been shown that xAI can enable successful adversarial attacks in the domain of fake news detection, leading to a decrease in AI security. This paper presents an attack scheme that uses an explainable solution to reshape the structure of the original message, allowing the adversary to manipulate the model's prediction while keeping the message's meaning intact.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Benyuan Yang, Lili Luo, Zhimeng Wang
Summary: Interoperation is widely used in practical industrial applications, but merging local access control policies may lead to security violations. Dealing with these issues in a multidomain environment is critical, but finding the maximum secure interoperation among individual systems poses a challenge due to the large number of entities and access involved.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Binghui Zou, Chunjie Cao, Longjuan Wang, Sizheng Fu, Tonghua Qiao, Jingzhang Sun
Summary: The ongoing struggle between security researchers and malware has led to the exploration of using convolutional neural networks and capsule networks for classification and identification of malware. However, training these networks requires a significant amount of data and parameters, and the research on capsule networks is still in its early stages, posing challenges.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Hongsong Chen, Xingyu Li, Wenmao Liu
Summary: Multivariate time-series anomaly detection is crucial for maintaining normal operation of physical equipment. Recent advances have been made in this field, but two challenges have limited the model's ability to generalize. To address these challenges, a multivariate time-series anomaly detection model consisting of a characterization network and a forecasting network is proposed. Experimental results demonstrate that this method outperforms baseline methods in terms of detection performance and robustness.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Roberto Doriguzzi-Corin, Domenico Siracusa
Summary: This paper discusses the application of federated learning in the field of cybersecurity and proposes an adaptive mechanism-based federated learning solution for DDoS attack detection in dynamic cybersecurity scenarios. Through experiments, it is demonstrated that the proposed solution outperforms state-of-the-art federated learning algorithms in terms of convergence time and accuracy.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Antonio Giovanni Schiavone
Summary: The usage of HTTPS protocol is crucial for secure communication with websites, ensuring the confidentiality, integrity, and authenticity of online data transmissions. The Municipality2HTTPS research project analyzed the implementation of HTTPS in Italian municipalities' websites and identified areas for improvement.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Domna Bilika, Nikoletta Michopoulou, Efthimios Alepis, Constantinos Patsakis
Summary: Voice Assistants (VAs) are widely used in smart devices, but are vulnerable to attacks, as shown by experiments with popular VAs revealing successful attack rates exceeding 30% and statistical variations among vendors, calling for additional countermeasures to protect user information.
COMPUTERS & SECURITY
(2024)