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Mostrando 121 - 140 Resultados de 290 Para Buscar '"malware"', tiempo de consulta: 0.14s Limitar resultados
  1. 121
    “…In the first dataset, Drebin was used for malware applications, and in the second dataset, CICMalDroid 2020 dataset was used for malware applications. …”
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    Android malware detection using hybrid ANFIS architecture with low computational cost convolutional layers
  2. 122
    por Aurangzeb, Sana, Aleem, Muhammad
    Publicado 2023
    “…With the rise in popularity and usage of Android operating systems, malicious applications are targeted by applying innovative ways and techniques. Today, malware becomes intelligent that uses several ways of obfuscation techniques to hide its functionality and evade anti-malware engines. …”
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    Evaluation and classification of obfuscated Android malware through deep learning using ensemble voting mechanism
  3. 123
    “…Federated-learning-based Android malware classifiers have attracted much attention due to their privacy-preserving and multi-party joint modeling. …”
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    FedDroidMeter: A Privacy Risk Evaluator for FL-Based Android Malware Classification Systems
  4. 124
    “…To reduce such threat, many researchers have proposed new methods to detect Android malware. Currently, most malware detection products on the market are based on malware signatures, which have a fast detection speed and normally a low false alarm rate for known malware families. …”
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    OpCode-Level Function Call Graph Based Android Malware Classification Using Deep Learning
  5. 125
    “…Stealth malware is a representative tool of advanced persistent threat (APT) attacks, which poses an increased threat to cyber-physical systems (CPS) today. …”
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    Dynamic Defense against Stealth Malware Propagation in Cyber-Physical Systems: A Game-Theoretical Framework †
  6. 126
    por Xu, Biao, Lu, Minyan, Zhang, Hong, Pan, Cong
    Publicado 2021
    “…However, existing approaches primarily focus on component reliability and malware propagation, while the robustness and security of cascading failures between the physical domain and the information domain are usually ignored. …”
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    A Novel Multi-Agent Model for Robustness with Component Failure and Malware Propagation in Wireless Sensor Networks
  7. 127
    “…However, unethical authors tend to develop malware in the devices for wealth, fame, or private purposes. …”
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    Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation)
  8. 128
    por Gunduz, Hakan
    Publicado 2022
    “…Malware harms the confidentiality and integrity of the information that causes material and moral damages to institutions or individuals. …”
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    Online Artículo Texto
    Malware detection framework based on graph variational autoencoder extracted embeddings from API-call graphs
  9. 129
    “…Genetic analysis of the sample’s DNA will decode the address that is used by the software Trojan malware to activate and trigger a remote connection. …”
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    Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacks
  10. 130
    “…This situation needs the immediate implementation of an effective malware detection system. In this study, an explainable malware detection system was proposed using transfer learning and malware visual features. …”
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    Explainable Malware Detection System Using Transformers-Based Transfer Learning and Multi-Model Visual Representation
  11. 131
    “…Self-propagating malware has been infecting thousands of IoT devices and causing security breaches worldwide. …”
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    Diffusion of White-Hat Botnet Using Lifespan with Controllable Ripple Effect for Malware Removal in IoT Networks
  12. 132
    “…Among the most latent challenges are the detection, classification and eradication of malware and Denial of Service Cyber-Attacks (DoS). …”
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    ReinforSec: An Automatic Generator of Synthetic Malware Samples and Denial-of-Service Attacks through Reinforcement Learning
  13. 133
    “…This makes Android smartphones a prime target for malware. In order to address threats posed by malware, many researchers have proposed different malware detection approaches, including using a function call graph (FCG). …”
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    An Android Malware Detection Approach to Enhance Node Feature Differences in a Function Call Graph Based on GCNs
  14. 134
    “…The transport layer security (TLS) protocol is widely adopted by apps as well as malware. With the geometric growth of TLS traffic, accurate and efficient detection of malicious TLS flows is becoming an imperative. …”
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    Two-layer detection framework with a high accuracy and efficiency for a malware family over the TLS protocol
  15. 135
    por Mahindru, Arvind, Sangal, A.L.
    Publicado 2021
    “…The most critical problem is to detect malware-infected apps and keep them out of Google play store. …”
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    Online Artículo Texto
    FSDroid:- A feature selection technique to detect malware from Android using Machine Learning Techniques: FSDroid
  16. 136
    por Alotaibi, Abdullah Shawan
    Publicado 2021
    “…Detection of unknown malware and its variants remains both an operational and a research challenge in the Internet of Things (IoT). …”
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    Biserial Miyaguchi–Preneel Blockchain-Based Ruzicka-Indexed Deep Perceptive Learning for Malware Detection in IoMT
  17. 137
    “…Due to the widespread usage of Android smartphones in the present era, Android malware has become a grave security concern. The research community relies on publicly available datasets to keep pace with evolving malware. …”
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    AndroMalPack: enhancing the ML-based malware classification by detection and removal of repacked apps for Android systems
  18. 138
    por Datt, Samir
    Publicado 2016
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    Learning network forensics: identify and safeguard your network against both internal and external threats, hackers, and malware attacks
  19. 139
    por Tevault, Donald A
    Publicado 2018
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    Mastering Linux security and hardening: secure your Linux server and protect it from intruders, malware attacks, and other external threats
  20. 140
    “…Deep learning methods are being increasingly widely used in static malware detection field because they can summarize the feature of malware and its variants that have never appeared before. …”
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    DeepDetectNet vs RLAttackNet: An adversarial method to improve deep learning-based static malware detection model
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