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Ontology-driven Knowledge Graph for Android Malware
[article]
2021
arXiv
pre-print
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This ontology forms the basis for the malware threat intelligence knowledge graph, MalKG, which we exemplify using three different, non-overlapping demonstrations. ...
In the poster and demonstration, we discuss MalONT2.0, MalKG, as well as the dynamically growing knowledge graph, TINKER. ...
The latest knowledge graph is generated from CTI reports that focus exclusively on android malware threats. ...
arXiv:2109.01544v1
fatcat:xwmjst54xbhwbdfoeta4qdtx4i
MALOnt: An Ontology for Malware Threat Intelligence
[article]
2020
arXiv
pre-print
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In this paper, we introduce an open-source malware ontology - MALOnt that allows the structured extraction of information and knowledge graph generation, especially for threat intelligence. ...
A work in progress, this research is part of a larger effort towards auto-generation of knowledge graphs (KGs)for gathering malware threat intelligence from heterogeneous online resources. ...
Oshani Seneviratne for evaluating MALOnt and for ensuring that best practices are followed for ontology generation; and Destin Yee for putting together the ontology and knowledge graph figures, and instantiating ...
arXiv:2006.11446v1
fatcat:bohgjorhnbguzpidk6e2bpptxa
Knowledge Enrichment by Fusing Representations for Malware Threat Intelligence and Behavior
2020
2020 IEEE International Conference on Intelligence and Security Informatics (ISI)
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The tracked malware behavior is represented in our Cybersecurity Knowledge Graph (CKG), so that a security professional can reason with behavioral information present in the graph and draw parallels with ...
Security engineers and researchers use their disparate knowledge and discretion to identify malware present in a system. ...
KNOWLEDGE GRAPH REASONING The behavioral knowledge when represented in a knowledge graph, presents us with query and reasoning capabilities. ...
doi:10.1109/isi49825.2020.9280512
fatcat:g6vl3mypu5ht3brbsb5e4msxfq
AndroVault: Constructing Knowledge Graph from Millions of Android Apps for Automated Analysis
[article]
2017
arXiv
pre-print
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With the produced data of high quality, we have successfully conducted many research works including malware detection, code generation, and Android testing. ...
After that, we employ a knowledge graph to connect all these apps by computing their correlation in terms of attributes; Last, we leverage multiple technologies such as logical inference, machine learning ...
, and auto GUI code generation. ...
arXiv:1711.07451v2
fatcat:wjlhwcdepvelbmqk5gju3b5s2y
Graph Neural Network-based Android Malware Classification with Jumping Knowledge
[article]
2022
arXiv
pre-print
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This paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK). ...
Android function call graphs (FCGs) consist of a set of program functions and their inter-procedural calls. ...
Android Malware Detection based on Graph Representation Learning In [19] , the authors generated OpCode graphs from the execution files and used the Power Iteration method to embed the graph into a low ...
arXiv:2201.07537v8
fatcat:y3euesh7lventppy3y6zwu6vhu
A Study of Trojan Propagation in Online Social Networks
2012
2012 5th International Conference on New Technologies, Mobility and Security (NTMS)
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Online Social Networks (OSNs) are generally based on real social relations. Hence, malware writers are taking advantage of this fact to propagate their viral code into OSNs. ...
This effect raises the significance of giving security knowledge to avoid designated social engineered posts. ...
The generated graph, satisfies all three required conditions for being a social network graph [16, 17] . Firstly, the average shortest path of the graph is less than log /log . ...
doi:10.1109/ntms.2012.6208767
dblp:conf/ntms/FaghaniML12
fatcat:3msnemo7bjapzfnx2iz4cqbjjq
RelExt
2019
Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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We use the set of semantic triples generated through our system to assert in a cybersecurity knowledge graph. ...
Security Analysts can retrieve this data from the knowledge graph, and use this information to form a decision about a cyber-attack. ...
Semantic triple generation is a key component in the Knowledge Graph population(See Section II). ...
doi:10.1145/3341161.3343519
dblp:conf/asunam/PinglePMJHZ19
fatcat:356wbtek7zgzlbmvzgmvyht72q
RelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvement
[article]
2019
arXiv
pre-print
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We use the set of semantic triples generated through our system to assert in a cybersecurity knowledge graph. ...
Security Analysts can retrieve this data from the knowledge graph, and use this information to form a decision about a cyber-attack. ...
Semantic triple generation is a key component in the Knowledge Graph population(See Section II). ...
arXiv:1905.02497v2
fatcat:arnicmmyhrh7nasveurnt4vb4a
Creating Cybersecurity Knowledge Graphs from Malware After Action Reports
2020
IEEE Access
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.: Creating Cybersecurity Knowledge Graphs from Malware After Action Reports ...
Cybersecurity Knowledge Graph. ...
doi:10.1109/access.2020.3039234
fatcat:5ymamllt7fcf3eq5pwdle4vd5y
Research Methodology on Web Mining for Malware Detection
English
2014
International Journal of Computer Trends and Technology
English
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The proposed web mining methodology uses web structure mining, using graph mining for malware detection with a case study proposed on cloud mining. ...
In this review paper we want to discuss Research Methodology on Web mining for Malware detection. ...
analysis domain knowledge, namely the Interval Type-2 Fuzzy Malware Ontology (IT2FMO), for malware behavior analysis. ...
doi:10.14445/22312803/ijctt-v12p131
fatcat:tt4nfblmhfb43a5a5j7hrew2pm
Malware Visualization Techniques
2020
International Journal of Applied Mathematics Electronics and Computers
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Malware review and analysis requires an advanced level of programming knowledge, in-depth file systems knowledge, deep code inspection, and reverse engineering capability. ...
In this work, Systematic Literature Review (SLR) conducted to investigate the current state of knowledge about Malware detection techniques, data visualization and malware features. ...
It facilitates the process of generating link graphs.• Graphviz: Tool to generate a two-dimensional link graphs.• ChartDirector: Programming library to generate a wide variety of charts. • Cytoscape: Tool ...
doi:10.18100/ijamec.526813
fatcat:54lixfrqxrdrlnnqmbtfpvmdve
Detecting Malware Based on DNS Graph Mining
2015
International Journal of Distributed Sensor Networks
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Malware remains a major threat to nowadays Internet. In this paper, we propose a DNS graph mining-based malware detection approach. ...
After the graph construction, we next transform the problem of malware detection to the graph mining task of inferring graph nodes' reputation scores using the belief propagation algorithm. ...
Thus, it has a feature of good generality for detecting various types of malware. ...
doi:10.1155/2015/102687
fatcat:whbj2sdb5refznvfcvu5skvj5y
Malware Detection Based on Hybrid Signature Behaviour Application Programming Interface Call Graph
2012
American Journal of Applied Sciences
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Results: In addition, a new malware detection framework is proposed. Conclusion: The proposed framework combines Signature-Based with Behaviour-Based using API graph system. ...
Problem statement: A malware is a program that has malicious intent. Nowadays, malware authors apply several sophisticated techniques such as packing and obfuscation to avoid malware detection. ...
Malware detectors take two inputs: • Knowledge of the malware signature or behavior (learning) • The program under inspection Once the malware detector has the knowledge of what is considered malware behavior ...
doi:10.3844/ajassp.2012.283.288
fatcat:dq3zaa2lvbeb7ldutuevlk2wou
Malicious Threats Detection of Executable File
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Malware is a general problems faced in the present day. Malware is a file that may be on the client machine. ...
In this paper explain a malware threats detection using data mining and machine learning. Malware detection algorithms with machine learning approach and data file. ...
knowledge of algorithms. ...
doi:10.35940/ijitee.c8918.019320
fatcat:uzoomccdvbc2tccujr4yw53uqq
AI assisted Malware Analysis: A Course for Next Generation Cybersecurity Workforce
[article]
2020
arXiv
pre-print
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Topics include: (1) CTI and malware attack stages, (2) malware knowledge representation and CTI sharing, (3) malware data collection and feature identification, (4) AI-assisted malware detection, (5) malware ...
on critical systems, such as cloud infrastructures, government offices or hospitals, and the vast amounts of data they generate. ...
knowledge representations: Students will be given access to servers hosting various malware representations like databases, knowledge graphs, and vector models. ...
arXiv:2009.11101v1
fatcat:jdqtzpffdbbtxfmqpg5o654uma
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