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Ontology-driven Knowledge Graph for Android Malware [article]

Ryan Christian, Sharmishtha Dutta, Youngja Park, Nidhi Rastogi
2021 arXiv   pre-print
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]

Nidhi Rastogi, Sharmishtha Dutta, Mohammed J. Zaki, Alex Gittens, Charu Aggarwal
2020 arXiv   pre-print
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

Aritran Piplai, Sudip Mittal, Mahmoud Abdelsalam, Maanak Gupta, Anupam Joshi, Tim Finin
2020 2020 IEEE International Conference on Intelligence and Security Informatics (ISI)  
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]

Guozhu Meng, Yinxing Xue, Jing Kai Siow, Ting Su, Annamalai Narayanan, Yang Liu
2017 arXiv   pre-print
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]

Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan, Marcus Gallagher, Marius Portmann
2022 arXiv   pre-print
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

Mohammad R. Faghani, Ashraf Matrawy, Chung-Horng Lung
2012 2012 5th International Conference on New Technologies, Mobility and Security (NTMS)  
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


Aditya Pingle, Aritran Piplai, Sudip Mittal, Anupam Joshi, James Holt, Richard Zak
2019 Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining  
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]

Aditya Pingle, Aritran Piplai, Sudip Mittal, Anupam Joshi, James Holt, Richard Zak
2019 arXiv   pre-print
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

Aritran Piplai, Sudip Mittal, Anupam Joshi, Tim Finin, James Holt, Richard Zak
2020 IEEE Access  
.: 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

Shaik. Irfan Babu, Dr. M.V.P. Chandra Sekhara Rao, G.Nagi Reddy
2014 International Journal of Computer Trends and Technology  
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

Ahmet EFE, Saleh Hussin S. HUSSİN
2020 International Journal of Applied Mathematics Electronics and Computers  
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

Futai Zou, Siyu Zhang, Weixiong Rao, Ping Yi
2015 International Journal of Distributed Sensor Networks  
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  
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

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]

Maanak Gupta, Sudip Mittal, Mahmoud Abdelsalam
2020 arXiv   pre-print
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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