Detection approaches for android malware: Taxonomy and review analysis
HHR Manzil, SM Naik - Expert Systems with Applications, 2023 - Elsevier
The main objective of this review is to present an in-depth study of Android malware
detection approaches. This article provides a comprehensive survey of 150 studies on …
detection approaches. This article provides a comprehensive survey of 150 studies on …
A generalized machine learning model for DDoS attacks detection using hybrid feature selection and hyperparameter tuning
In the digital era, the usage of network-connected devices is rapidly growing which leads to
an increase in cyberattacks. Among them, Distributed Denial of Service (DDoS) attacks are …
an increase in cyberattacks. Among them, Distributed Denial of Service (DDoS) attacks are …
Android malware category detection using a novel feature vector-based machine learning model
HHR Manzil, S Manohar Naik - Cybersecurity, 2023 - Springer
Malware attacks on the Android platform are rapidly increasing due to the high consumer
adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to …
adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to …
Towards a fair comparison and realistic evaluation framework of android malware detectors based on static analysis and machine learning
As in other cybersecurity areas, machine learning (ML) techniques have emerged as a
promising solution to detect Android malware. In this sense, many proposals employing a …
promising solution to detect Android malware. In this sense, many proposals employing a …
Do gradient-based explanations tell anything about adversarial robustness to android malware?
While machine-learning algorithms have demonstrated a strong ability in detecting Android
malware, they can be evaded by sparse evasion attacks crafted by injecting a small set of …
malware, they can be evaded by sparse evasion attacks crafted by injecting a small set of …
On improving the performance of DDoS attack detection system
Abstract A DDoS (Distributed Denial of Service) attack is a harmful way of preventing regular
access to a targeted machine, resources, or any network by flooding the target or its …
access to a targeted machine, resources, or any network by flooding the target or its …
Improving the accuracy of network intrusion detection with causal machine learning
Z Zeng, W Peng, B Zhao - Security and Communication …, 2021 - Wiley Online Library
In recent years, machine learning (ML) algorithms have been approved effective in the
intrusion detection. However, as the ML algorithms are mainly applied to evaluate the …
intrusion detection. However, as the ML algorithms are mainly applied to evaluate the …
Importance-aware contrastive learning via semantically augmented instances for unsupervised sentence embeddings
X Ma, H Li, J Shi, Y Zhang, Z Long - International Journal of Machine …, 2023 - Springer
Attaining better sentence embeddings benefits a wide range of natural language processing
tasks. SimCSE applied a simple contrastive learning framework to train BERT models and …
tasks. SimCSE applied a simple contrastive learning framework to train BERT models and …
Revisiting android app categorization
Numerous tools rely on automatic categorization of Android apps as part of their
methodology. However, incorrect categorization can lead to inaccurate outcomes, such as a …
methodology. However, incorrect categorization can lead to inaccurate outcomes, such as a …
KGA: integrating KPCA and GAN for microbial data augmentation
The data used for microbial-based disease diagnosis are characterized by small sample
sizes, imbalanced categories, high dimensionality, and strong sparsity. They pose …
sizes, imbalanced categories, high dimensionality, and strong sparsity. They pose …