A
Arzu Gorgulu Kakisim
Researcher at Istanbul Commerce University
Publications - 12
Citations - 82
Arzu Gorgulu Kakisim is an academic researcher from Istanbul Commerce University. The author has contributed to research in topics: Malware & Computer science. The author has an hindex of 4, co-authored 10 publications receiving 36 citations. Previous affiliations of Arzu Gorgulu Kakisim include Gebze Institute of Technology.
Papers
More filters
Book ChapterDOI
Analysis and Evaluation of Dynamic Feature-Based Malware Detection Methods
TL;DR: The main objective is to find more discriminative dynamic features to detect malware executables by analyzing different dynamic features with common malware detection approaches by evaluating some dynamic feature-based malware detection and classification approaches.
Journal ArticleDOI
Metamorphic malware identification using engine-specific patterns based on co-opcode graphs
TL;DR: This work proposes a novel metamorphic malware identification method, named HLES-MMI (Higher-level Engine Signature based Metamorphic Malware Identification), which firstly constructs a unique graph structure, called as co-opcode graph, for each meetamorphic family, then extracts engine-specific opcode patterns from the graphs.
Proceedings ArticleDOI
Analysis and Comparison of Disassemblers for OpCode Based Malware Analysis
TL;DR: The experimental results presented which disassembler is more suitable with the analysis method for the best performance will help researchers to be guided with the results obtained in this work for their static opcode based PE file analysis.
Journal ArticleDOI
Sequential opcode embedding-based malware detection method
TL;DR: Wang et al. as discussed by the authors proposed a new malware detection approach, called Sequential Opcode Embedding-based Malware Detection (SOEMD), which aims at capturing common malicious patterns in sequential opcodes.
Journal ArticleDOI
Unsupervised binary feature construction method for networked data
TL;DR: A new unsupervised binary feature construction method (NetBFC) for networked data that reconstructs attributes for each object by exploiting link information and applies an attribute elimination phase to eliminate irrelevant and redundant attributes which decrease the performance of clustering algorithms.