Approximate TF–IDF based on topic extraction from massive message stream using the GPU

Recently, some graph-based methods have been proposed for malware detection. However, current malware is generally characterized by sophisticated behaviors, which makes graph-based malware detection extremely challenging. To address this issue, we propose a graph repartition algorithm by transforming API call graphs into fragment behaviors based on programs’ dynamic execution traces. The proposed algorithm relies on the N-order subgraph (NSG) for constructing the appropriate fragment behavior. Moreover, we improve the term frequency-inverse document frequency- (TF-IDF-) like measure and information gain (IG) to extract the crucial N-order subgraph (CNSG). This novel behavioral representation and improved extraction method can accurately represent crucial behaviors of malware. Experiments on 4,400 samples demonstrate that the proposed method achieves a high accuracy of 99.75% in malware detection and promising performance of 95.27% in malware classification.

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“…TF refers to the number of times a given word appears in a document. IDF measures the general importance of words [42].…”

Section: Basic Notationmentioning

confidence: 99%

A Novel Malware Classification Method Based on Crucial Behavior

Xiao

Sun

et al. 2020

Mathematical Problems in Engineering

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“…(1) if only contains a single path P (2) ifcov(Head ∪ , MFI) > cov min (3) combine MFI-tree to this path; (4) else (5) insertHead∪ into MFIT; (6) else for each in Header-table of T (7) append to Head; (8) construct the Head-pattern base; (9) Tail={frequent items in base}; (10) subset_checking (Head ∪ Tail); (11) if Head ∪ Tail is not in MFI-tree (12) constructtheFP-tree Head ; (13) call FPMAX-RS( Head ); (14) remove from Head. in TongYiCi CiLin.…”

Section: The Construction Of Edges In the Vocabulary Networkmentioning

confidence: 99%

“…The most representative method based on term frequency is term frequency-inverse document frequency (tf-idf) algorithm. For its simplicity and high efficiency, researchers have proposed many improved tfidf algorithms [11,12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Text Clustering Approach Using Deep-Learning Vocabulary Network

Zhang

Zhao

et al. 2017

Mathematical Problems in Engineering

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Text clustering is an effective approach to collect and organize text documents into meaningful groups for mining valuable information on the Internet. However, there exist some issues to tackle such as feature extraction and data dimension reduction. To overcome these problems, we present a novel approach named deep-learning vocabulary network. The vocabulary network is constructed based on related-word set, which contains the “cooccurrence” relations of words or terms. We replace term frequency in feature vectors with the “importance” of words in terms of vocabulary network and PageRank, which can generate more precise feature vectors to represent the meaning of text clustering. Furthermore, sparse-group deep belief network is proposed to reduce the dimensionality of feature vectors, and we introduce coverage rate for similarity measure in Single-Pass clustering. To verify the effectiveness of our work, we compare the approach to the representative algorithms, and experimental results show that feature vectors in terms of deep-learning vocabulary network have better clustering performance.

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“…Erra et al [4] present a GPU implementation of an approximate streaming version of TF-IDF. The TF-IDF metric is approximated by counting occurrences of a pre-set number of terms only in order to meet the memory limitations of GPUs.…”

Section: Related Workmentioning

confidence: 99%