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Automatic Malware Classification via PRICoLBP

Chinese Journal of Electronics 2018年第4期27卷 852-859页

作者： YAN Hanbing ZHOU Hao ZHANG Honggang National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing University of Posts and Telecommunications

Creating effective features is a critical issue in malware analysis. It requires a proper tradeoff between discriminative power and invariance. Previous studies have shown that it is fairly effective to design features based on the binary code. However, the current existing binary-based features seldom take into consideration the problem of obfuscation, such as relocated sections, incomplete code and redundant operations. In this paper, we propose a novel Pairwise rotation invariant co-occurrence local binary pattern(PRICo LBP) feature, and further extend it to incorporate the term frequency-inverse document frequency(TFIDF) transform. Different from other static analysis techniques, our method not only achieves better linear separability, but also appears to be more resilient to obfuscation. In addition, we evaluate PRICo LBPTFIDF comprehensively on three datasets from different perspectives, e.g., classification performance, classifier selection and performance against obfuscation. What’s more,we compare our PRICoLBP-TFIDF method with other techniques, and demonstrate that PRICo LBP-TFIDF is quite an efficient and effective tradeoff between discriminative power and invariance.

关键词： Malware classification Pairwise rotation invariant co-occurrence local binary pattern term frequency-inverse document frequency Texture classification Resilience to obfuscation

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Enhanced Topic-Aware Summarization Using Statistical Graph Neural Networks

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Computers, Materials & Continua 2024年第8期80卷 3221-3242页

作者： Ayesha Khaliq Salman Afsar Awan Fahad Ahmad Muhammad Azam Zia Muhammad Zafar Iqbal Department of Computer Science University of Agriculture FaisalabadFaisalabadPunjab37300Pakistan School of Computing Faculty of TechnologyUniversity of PortsmouthSouthseaPortsmouthPO12UPUK Department of Mathematics and Statistics University of Agriculture FaisalabadFaisalabadPunjab37300Pakistan

The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original *** approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent *** study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by term frequency-inverse document frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical *** approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text *** model is stretched to Multi-document Summarization(MDS)from Single-document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and *** results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document *** research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.

关键词： Summarization graph attention network bidirectional encoder representations from transformers Latent Dirichlet Allocation term frequency-inverse document frequency

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