TL;DR
This paper investigates a unified behavioral analysis framework that combines temporal and structural representations of Windows API call sequences: a sequential view based on learned API n-gram representations and a Bidirectional LSTM (BiLSTM) encoder, a structural view derived from the API transition graph, and a multi-head attention fusion of the two.
MetaGNN-Sec, a graph-augmented neural framework that detects metamorphic malware from program structure rather than surface bytes, provides a heterogeneous PDG representation with a conditional score-shift bound under graph-edit-bounded mutations, a WGAN hardening loop that delivers measurable adversarial robustness, a...
The study introduces an innovative approach of deep learning-based hybrid system to classify malware based on its real-time detection using a novel architecture called “Gated Convolutional Embedded Convolutional Network - Bidirectional Long Short-Term Memory (GCE-CNN-BiLSTM)” that integrates both static and dynamic se...
Recent rapid increases in sophisticated malware have severely challenged traditional signature-based security systems, particularly in their ability to recognize zero-day and polymorphic threats. This paper introduces “DeepGuard,” an AI/Machine-Learning-powered behavioral malware detection framework built around the an...
HYDRA (Hybrid Drift Adaptation), a proactive adaptation framework that learns drift-invariant representations from hierarchically structured data, unifies feature learning and domain alignment, eliminating the need for complex adversarial objectives.
Signature-based malware detection is undermined by obfuscation and packing, motivating dynamic analysis of Application Programming Interface (API) call sequences. Existing image-based classifiers reach high accuracy but rarely explain why a sample belongs to a given family. In this paper, our goal is not to maximize cl...
A Transformer-based malware detection approach named MalBERT-Temporal that reshapes the 2,381-dimensional BODMAS PE feature vector into 16 contiguous feature-group tokens and then processes these tokens with Transformer encoder layers employing multi-head self-attention to model interactions among all tokens is introdu...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
