A Real-Time and Adaptive-Learning Malware Detection Method Based on API-Pair Graph

Yang, Shaojie; Li, Shanxi; Chen, Wenbo; Liu, Yuhong

doi:10.1109/access.2020.3038453

Cited by 16 publications

(14 citation statements)

References 36 publications

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“…The adaptive learning system is divided into a macro adaptive learning system and a micro adaptive learning system. The former is adaptive based on predetermined criteria, that is, after the required adaptation is determined, the adaptive learning system is mainly composed of five parts: the domain model (displaying the content that students need to learn), the learning model (presenting the content) and learning strategy, the evaluation model (evaluating students’ basic knowledge), the teaching model (encouraging students’ to learn something more), and the adaptation model (determining what students need to learn next) ( Yang S. et al, 2020 ). Figure 1 shows the structure of adaptive learning.…”

Section: Theoretical Analysismentioning

confidence: 99%

Current Situation and Strategy Formulation of College Sports Psychology Teaching Following Adaptive Learning and Deep Learning Under Information Education

Mou

Yuan²,

Zhang³

et al. 2022

Front. Psychol.

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This study aims to explore the current situation and strategy formulation of sports psychology teaching in colleges and universities following adaptive learning and deep learning under information education. The informatization in physical education, teaching methods, and teaching processes make psychological education more scientific and efficient. First, the relevant theories of adaptive learning and deep learning are introduced, and an adaptive learning analysis model is implemented. Second, based on the deep learning automatic encoder, college students’ sports psychology is investigated and the test results are predicted. Finally, the current situation and development strategy of physical education in colleges and universities are analyzed. The results show that when the learning rate is 1, 0.1, and 0.01, there is no significant change in the analysis factors of recall, ndcg, item_coverage, and sps. When the learning rate is 1, their analysis factors change obviously, and it is calculated that the optimal learning rate of the model is 1. And the difficulty of the recommended test questions by using the sports psychology teaching method based on adaptive learning and deep learning is relatively stable. The test questions include various language points of sports psychology. Compared with others methods, adaptive learning and deep learning can provide comprehensive test questions for sports psychology teaching. This study provides technical support for the reform of sports psychology teaching in colleges and universities and contributes to optimizing the information-based teaching mode.

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Section: Theoretical Analysismentioning

confidence: 99%

Current Situation and Strategy Formulation of College Sports Psychology Teaching Following Adaptive Learning and Deep Learning Under Information Education

Mou

Yuan²,

Zhang³

et al. 2022

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…The idea behind that the behavior of malware variant has similarities with the behavior of its origin. In addition, as the executable malware cannot hide its malicious behavior once it is in the attack mode, a malware variant can be detected using real-time malware detection such that proposed in [43,48,49].…”

Section: Limitations and Future Workmentioning

confidence: 99%

An Adaptive Behavioral-Based Incremental Batch Learning Malware Variants Detection Model Using Concept Drift Detection and Sequential Deep Learning

et al. 2021

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Malware variants are the major emerging threats that face cybersecurity due to the potential damage to computer systems. Many solutions have been proposed for detecting malware variants. However, accurate detection is challenging due to the constantly evolving nature of the malware variants that cause concept drift. Existing malware detection solutions assume that the mapping learned from historical malware features will be valid for new and future malware. The relationship between input features and the class label has been considered stationary, which doesn't hold for the ever-evolving nature of malware variants. Malware features change dynamically due to code obfuscations, mutations, and the modification made by malware authors to change the features' distribution and thus evade the detection rendering the detection model obsolete and ineffective. This study presents an Adaptive behavioral-based Incremental Batch Learning Malware Variants Detection model using concept drift detection and sequential deep learning (AIBL-MVD) to accommodate the new malware variants. Malware behavior were extracted using dynamic analysis by running the malware files in a sandbox environment and collecting their Application Programming Interface (API) traces. According to the malware first-time appearance, the malware samples were sorted to capture the malware variants' change characteristics. The base classifier was then trained based on a subset of historical malware samples using a sequential deep learning model. The new malware samples were mixed with a subset of old data and gradually introduced to the learning model in an adaptive batch size incremental learning manner to address the catastrophic forgetting dilemma of the incremental learning. The statistical process control technique has been used to detect the concept drift as indication for incrementally updating the model as well as reducing the frequency of model updates. Results from extensive experiments show that the proposed model is superior in terms of detection rate and the efficiency compared with the static model, periodic retraining approaches, and the fixed batch size incremental learning approach. The model maintains an average of 99.41% detection accuracy of new and variants malware with a low updating frequency of 1.35 times per month.

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“…Other researchers have relied on dynamic features for detecting malware [198], [202], [210], [215], [221]. For instance, the authors of [210], [215] have used dynamic features such as behavior features, API calls and opcode sequences for detecting malware. However, if malware is able to disguise its behaviors and contents, these approaches may fail to detect it.…”

Section: E Machine Learning-based Malware Detectionmentioning

confidence: 99%

“…However, in addition to the high detection time and complex hardware modifications required to detect malware, this approach is also incapable of detecting malware in real-time, making it unsuitable for modern cars. A large portion of existing machine learning-based malware detection techniques relied on hybrid features to detect malware [199], [200], [203], [205], [206], [208], [210], [211], [213], [215], [219]. For example, the authors of [199], [200], [203], [205], [206], [208], [210], [213], [215], [219] have used system calls, instructions, image features, API calls, data flow, network flow, API call sequences and permissions as features for detecting malware.…”

Section: E Machine Learning-based Malware Detectionmentioning

confidence: 99%

“…A large portion of existing machine learning-based malware detection techniques relied on hybrid features to detect malware [199], [200], [203], [205], [206], [208], [210], [211], [213], [215], [219]. For example, the authors of [199], [200], [203], [205], [206], [208], [210], [213], [215], [219] have used system calls, instructions, image features, API calls, data flow, network flow, API call sequences and permissions as features for detecting malware. However, these methods may be ineffective, if malware is able to conceal its harmful actions and contents, making them inappropriate for modern vehicles.…”

Section: E Machine Learning-based Malware Detectionmentioning

confidence: 99%

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Vehicle Security: A Survey of Security Issues and Vulnerabilities, Malware Attacks and Defenses

et al. 2021

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A Real-Time and Adaptive-Learning Malware Detection Method Based on API-Pair Graph

Cited by 16 publications

References 36 publications

Current Situation and Strategy Formulation of College Sports Psychology Teaching Following Adaptive Learning and Deep Learning Under Information Education

Current Situation and Strategy Formulation of College Sports Psychology Teaching Following Adaptive Learning and Deep Learning Under Information Education

An Adaptive Behavioral-Based Incremental Batch Learning Malware Variants Detection Model Using Concept Drift Detection and Sequential Deep Learning

Vehicle Security: A Survey of Security Issues and Vulnerabilities, Malware Attacks and Defenses

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