Article
Computer Science, Information Systems
Ali Jazayeri, Christopher C. Yang
Summary: Networks are considered perfect tools for modeling various systems. Frequent subgraph mining is seen as the essence of mining network data. Due to the complexities of the mining process, algorithms in the literature utilize various heuristics. This survey provides a classification of proposed algorithms and focuses on static and temporal network algorithms.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Article
Computer Science, Theory & Methods
Cuiying Gao, Minghui Cai, Shuijun Yin, Gaozhun Huang, Heng Li, Wei Yuan, Xiapu Luo
Summary: This study proposes an obfuscation-resilient Android malware analysis method called CorDroid, based on the combination of Enhanced Sensitive Function Call Graph (E-SFCG) and Opcode-based Markov transition Matrix (OMM). CorDroid shows higher detection performance compared to state-of-the-art methods and exhibits high execution efficiency.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Review
Computer Science, Information Systems
Vikas Sihag, Manu Vardhan, Pradeep Singh
Summary: With the increasing quantity and complexity of malware, Android users are facing severe security threats. Malware authors employ various techniques to evade detection, making it more challenging to detect. Strengthening security mechanisms has become increasingly important in application development.
COMPUTER SCIENCE REVIEW
(2021)
Article
Computer Science, Information Systems
Zubair Ali Ansari, Jahiruddin, Muhammad Abulaish
Summary: For the subgraph isomorphism finding problem, various solvers exist in the literature, but computational efficiency remains a central issue. This paper introduces an efficient solver, SubGlw, which decomposes the data graph into small-size candidate subgraphs and utilizes a ranking function to improve search efficiency and reduce computing costs.
Article
Chemistry, Multidisciplinary
Wenhua Guo, Wenqian Feng, Yiyan Qi, Pinghui Wang, Jing Tao
Summary: This paper proposes a novel motif sampling method, Mosar, to estimate motif frequencies. By sampling frequent and rare motifs with different probabilities and tending to sample motifs with low frequencies, the method greatly reduces the estimation errors of rare motifs.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Daniel Gibert, Carles Mateu, Jordi Planes, Joao Marques-Silva
Summary: Malicious software poses a serious threat on the internet, with traditional detection methods struggling to keep up. Machine learning and deep learning engines have shown promise in handling complex malware and new variants effectively. Further research is needed to improve classification performance and vulnerabilities to adversarial examples.
COMPUTERS & SECURITY
(2021)
Review
Computer Science, Information Systems
Qing Wu, Xueling Zhu, Bo Liu
Summary: The rapid growth of Android devices and applications has led to increased security threats in the Android environment. Researchers have proposed machine learning-based methods with static features of apps as input vectors to detect Android malware, which have advantages in code coverage, operational efficiency, and massive sample detection. This paper investigates the structure of Android applications, analyzes sources of static features, reviews machine learning methods for detecting Android malware, studies the advantages and limitations of these methods, and discusses future directions in this field.
MOBILE INFORMATION SYSTEMS
(2021)
Article
Computer Science, Information Systems
Fan Ou, Jian Xu
Summary: This paper proposes a novel static sensitive subgraph-based feature for Android malware detection, named S(3)Feature. By mining sensitive subgraphs and neighbor subgraphs to characterize suspicious behaviors of applications, and encoding them into feature vectors, S(3)Feature achieves a 97.04% F1 score in malware detection, outperforming other well-studied features.
COMPUTERS & SECURITY
(2022)
Article
Computer Science, Information Systems
Andrea Pasini, Flavio Giobergia, Eliana Pastor, Elena Baralis
Summary: This research proposes an image collection summarization technique based on frequent subgraph mining, utilizing a novel representation of scene graphs for images. The resulting summary demonstrates non-redundant and diverse patterns extracted from the image dataset.
Article
Computer Science, Information Systems
Pagnchakneat C. Ouk, Wooguil Pak
Summary: In this paper, a new static analysis technique is proposed to address the challenges of obfuscating and native malware applications on the Android operating system. The proposed system achieves rapid and high detection rate by extracting features and using a selection algorithm.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Artificial Intelligence
Ngoc-Thao Le, Bay Vo, Unil Yun, Bac Le
Summary: Mining a weighted single large graph has become a popular research topic. This paper introduces a novel algorithm called AWeGraMi, which solves the problem of calculating average value in a domain with the same role for all values. AWeGraMi calculates the weight based on the average value and applies the MaxMin measure as an upper-bound to prune the search space. Experimental results show that AWeGraMi outperforms post-processing GraMi in terms of search space, running time, and memory consumption.
APPLIED INTELLIGENCE
(2023)
Article
Chemistry, Multidisciplinary
Haemin Jung, Heesung Park, Kwangyon Lee
Summary: Recommender systems are essential for personalizing online user experiences. However, the semantics and explainability of knowledge graphs are often lost in deep learning-based recommendation algorithms. To address this issue, we propose a novel user profiling method that preserves the semantics of knowledge graphs while encapsulating user preferences using frequent subgraph mining.
APPLIED SCIENCES-BASEL
(2023)
Article
Chemistry, Multidisciplinary
Kwangyon Lee, Haemin Jung, June Seok Hong, Wooju Kim
Summary: This study proposes a novel method for efficiently learning frequent subgraphs from ontology-based graph data, demonstrating its superiority in movie rating prediction over other recommendation methods through experiments.
APPLIED SCIENCES-BASEL
(2021)
Article
Neurosciences
Yao Li, Zihao Zhou, Qifan Li, Tao Li, Ibegbu Nnamdi Julian, Hao Guo, Junjie Chen
Summary: This study proposed an approximate frequent subgraph mining algorithm and a discriminative feature selection method for uncertain brain networks to improve the classification accuracy of brain diseases. The results showed that the new methods achieved higher classification accuracy, especially within a certain sparse range, the performance of uncertain networks was superior to that of certain networks.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Computer Science, Information Systems
Daniel Gibert, Matt Fredrikson, Carles Mateu, Jordi Planes, Quan Le
Summary: The current research on malware detection and classification focuses on using machine learning systems due to their ability to handle unfamiliar malware variants. However, it has been found that machine learning models, especially deep neural networks, are vulnerable to carefully crafted inputs. This study investigates the vulnerability of a state-of-the-art shallow convolutional neural network malware classifier to the technique of dead code insertion and proposes a general framework that induces misclassification of malware families.
COMPUTERS & SECURITY
(2022)
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)