A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
Contextual Weisfeiler-Lehman Graph Kernel For Malware Detection
[article]
2016
arXiv
pre-print
Preserved Fulltext
We observe that state-of-the-art graph kernels, such as Weisfeiler-Lehman kernel (WLK) capture the structural information well but fail to capture contextual information. ...
To address this, we develop the Contextual Weisfeiler-Lehman kernel (CWLK) which is capable of capturing both these types of information. ...
We apply this featureenrichment idea on a state-of-the-art graph kernel, namely, Weisfeiler-Lehman kernel (WLK) [11] to obtain the Contextual Weisfeiler-Lehman kernel (CWLK). ...
arXiv:1606.06369v1
fatcat:kmhrihgbxrch5mthmejdgoyerm
Context-aware, Adaptive and Scalable Android Malware Detection through Online Learning (extended version)
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In order to perform accurate detection, a novel graph kernel that facilitates capturing apps' security-sensitive behaviors along with their context information from dependency graphs is proposed. ...
It is well-known that Android malware constantly evolves so as to evade detection. This causes the entire malware population to be non-stationary. ...
Weisfeiler-Lehman Kernel. ...
arXiv:1706.00947v2
fatcat:sduouh6iovhkxficotkgpwaycq
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
MKLDroid uses a graph kernel to capture structural and contextual information from apps' dependency graphs and identify malice code patterns in each view. ...
Addressing this limitation, we propose MKLDroid, a unified framework that systematically integrates multiple views of apps for performing comprehensive malware detection and malicious code localisation ...
To address C1, we leverage on our previous work [20] and use the Contextual Weisfeiler-Lehman Kernel (CWLK) that is specifically designed to perform accurate malware detection by capturing both structural ...
arXiv:1704.01759v2
fatcat:msutizn5uneovma7mb36vaeqzm
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs
[article]
2016
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Also, we show that the subgraph vectors could be used for building a deep learning variant of Weisfeiler-Lehman graph kernel. ...
Specifically, on two realworld program analysis tasks, namely, code clone and malware detection, subgraph2vec outperforms state-of-the-art kernels by more than 17% and 4%, respectively. ...
To illustrate this, lets consider the Weisfeiler-Lehman (WL) kernel [6] which decomposes graphs into rooted subgraphs 1 . ...
arXiv:1606.08928v1
fatcat:ltirl3etv5e5xhffj5m4k4ahum
Android-COCO: Android Malware Detection with Graph Neural Network for Byte- and Native-Code
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Recently, various approaches have been introduced to detect Android malware, the majority of these are either based on the Manifest File features or the structural information, such as control flow graph ...
After that, we design an ensemble algorithm to get the final result of malware detection system. ...
graph kernel, namely, Weisfeiler-Lehman Kernel(WLK) to obtain the Contextual Weisfeiler-Lehman Kernel(CWLK). ...
arXiv:2112.10038v2
fatcat:5wbiq52wp5hsfo2jlcaxawcpjq
Adaptive and Scalable Android Malware Detection through Online Learning
[article]
2016
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In order to perform accurate detection, security-sensitive behaviors are captured from apps in the form of inter-procedural control-flow sub-graph features using a state-of-the-art graph kernel. ...
Our experimental findings strongly indicate that online learning based approaches are highly suitable for real-world malware detection. ...
ACKNOWLEDGMENT We thank the authors of [4] and [5] , for their suggestions and discussions that helped us re-implement their methods. We thank Kevin Allix for sharing the dataset used in [23] . ...
arXiv:1606.07150v2
fatcat:klf5uaukzreidojvdx53egs57e
Algorithm selection for software validation based on graph kernels
2020
Automated Software Engineering : An International Journal
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Our kernel operates on a graph representation of source code mixing elements of control-flow and program-dependence graphs with abstract syntax trees. ...
The evaluation, which is based on data sets from the annual software verification competition SV-COMP, demonstrates our kernel to generalize well and to achieve rather high prediction accuracy, both for ...
Weisfeiler-Lehman subtree kernels (on CFGs only) are also employed for malware detection in Android apps (Wagner et al. 2009; Sahs and Khan 2012) . ...
doi:10.1007/s10515-020-00270-x
fatcat:4b62vpw6izg3xce26ysdhovtp4
Predicting Rankings of Software Verification Competitions
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. ...
The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for verification tasks. ...
., types for program variables [16] or malware in Android apps [17] ). Just like our approach, the la er also uses Weisfeiler-Lehman subtree kernels (on CFGs only). ...
arXiv:1703.00757v1
fatcat:rk4hob6lhjdmtfzdjf4ce4bdxe
Order Matters: Semantic-Aware Neural Networks for Binary Code Similarity Detection
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Moreover, we find that the order of the CFG's nodes is important for graph similarity detection, so we adopt convolutional neural network (CNN) on adjacency matrices to extract the order information. ...
Binary code similarity detection, whose goal is to detect similar binary functions without having access to the source code, is an essential task in computer security. ...
To capture the semantic feature, we propose BERT pre-training for the blocks of CFGs with two original tasks MLM & ANP, and two additional graph-level tasks BIG & GC. ...
doi:10.1609/aaai.v34i01.5466
fatcat:yqvu4sondrb5zo4avshuk4fvky
UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
2020
Proceedings 2020 Network and Distributed System Security Symposium
unpublished
doi:10.14722/ndss.2020.24046 fatcat:uov5xctdezcchb2biqkyivsqqq
doi:10.14722/ndss.2020.24046 fatcat:uov5xctdezcchb2biqkyivsqqq
From modeling to detection, UNICORN tailors its design specifically for the unique characteristics of APTs. ...
Through extensive yet time-efficient graph analysis, UNICORN explores provenance graphs that provide rich contextual and historical information to identify stealthy anomalous activities without pre-defined ...
We adapt a linear-time, fast Weisfeiler-Lehman (WL) subtree graph kernel algorithm based on one dimensional WL test of isomorphism [126] . ...
arXiv:2001.01525v1
fatcat:cljlsnrtsfamhlhtnzptydwd6i
Bayesian Deep Learning for Graphs
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Two real-world applications demonstrate the efficacy of deep learning for graphs. ...
In this thesis, we take a different route and develop a Bayesian Deep Learning framework for graph learning. ...
such as the 1-dim Weisfeiler-Lehman (WL) test [34] . ...
arXiv:2202.12348v1
fatcat:ayrl5zr6q5dfjhqspecg4umsxm
Complex Data: Learning Trustworthily, Automatically, and with Guarantees
2021
ESANN 2021 proceedings
unpublished
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
This demands for improving both ML technical aspects (e.g., design and automation) and human-related metrics (e.g., fairness, robustness, privacy, and explainability), with performance guarantees at both ...
The aforementioned scenario posed three main challenges: (i) Learning from Complex Data (i.e., sequence, tree, and graph data), (ii) Learning Trustworthily, and (iii) Learning Automatically with Guarantees ...
Another research direction focuses on networks for graphs extending two existing theories (i.e., Weisfeiler-Lehman test and unfolding equivalence), which separately provide only few suggestions about the ...
doi:10.14428/esann/2021.es2021-6
fatcat:pahz7bwkqzcllnpy5ckl427kg4
A Systematic Survey on Deep Generative Models for Graph Generation
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. ...
This article provides an extensive overview of the literature in the field of deep generative models for the graph generation. ...
Hamming and Ipsen-Mikhailov distances(HIM) [61] ; (3) spectral entropies of the density matrices; (4) eigenvector centrality distance [12] ; (5) closeness centrality distance [37] ; (6) Weisfeiler Lehman ...
arXiv:2007.06686v2
fatcat:xox7apwdvbfhlgnsgrr3w3rv5m
Learning to Find Bugs in Programs and their Documentation
2021
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Second, we hope that our work will open the door for more research on automatically utilizing natural language in software development. ...
First, we provide developers with novel bug detection techniques that complement traditional ones. ...
Given two graphs and their WL sequences, we compute the graph kernel as follows: Definition 3. 4
.1 (Weisfeiler-Lehman kernel) The graph kernel of g and g is k(g, g ) = k sub (g 0 , g 0 ) + k sub (g ...
doi:10.26083/tuprints-00017377
fatcat:o47olqxg5rhrzoqg4j5tazo3ni
Graph mining on static, multiplex and attributed networks
[article]
2021
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Graph structured data is pervasive and generated by online human interactions at an unprece- dented velocity. ...
Relational data poses challenges for information extraction and knowledge discovery due to its web scale size, extreme sparsity, multimodality, the presence of spatial autocorrelation and heterogeneity ...
We extracted the Weisfeiler-Lehman features which appeared in at least 5 graphs in the datasets. ...
doi:10.7488/era/1498
fatcat:kmqs4lnzmzam7a7q53aic7nf3y