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Research Methodology on Web Mining for Malware Detection
English

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.  ...  It proposes a novel soft-computing mechanism based on the ontology model for malware behavioral analysis: Malware Analysis Network in Taiwan (MAN in Taiwan, MiT).  ... 
doi:10.14445/22312803/ijctt-v12p131 fatcat:tt4nfblmhfb43a5a5j7hrew2pm

Comprehensive Analysis of IoT Malware Evasion Techniques

A. Al-Marghilani
2021 Engineering, Technology & Applied Science Research  
Malware detection in Internet of Things (IoT) devices is a great challenge, as these devices lack certain characteristics such as homogeneity and security.  ...  Many security researchers have studied the IoT malware detection domain. Many studies proposed the static or dynamic analysis on IoT malware detection.  ...  Malware detection can be performed on three bases: Behavior-based, specification -based, and signature-based ( Figure 1 ).  ... 
doi:10.48084/etasr.4296 fatcat:hyfkdspwizce3cyeu6erygpqai

Peer-to-Peer Architecture for Collaborative Intrusion and Malware Detection on a Large Scale [chapter]

Mirco Marchetti, Michele Messori, Michele Colajanni
2009 Lecture Notes in Computer Science  
The complexity of modern network architectures and the epidemic diffusion of malware require collaborative approaches for defense.  ...  We present a novel distributed system where each component collaborates to the intrusion and malware detection and to the dissemination of the local analyses.  ...  Moreover, it disseminates network activity reports on the basis of a behavioral analysis of the captured payload, thus being able to provide a description of the malware behavior.  ... 
doi:10.1007/978-3-642-04474-8_37 fatcat:2ytrbjpxsfehte25fevnarjkkq

SCREDENT: Scalable Real-time Anomalies Detection and Notification of Targeted Malware in Mobile Devices

Paul McNeil, Sachin Shetty, Divya Guntu, Gauree Barve
2016 Procedia Computer Science  
We propose SCREDENT: Scalable Real-time Anomalies Detection and Notification of Targeted Malware in Mobile Devices, to provide a scalable system to classify, detect, and predict targeted malware in real-time  ...  SCREDENT uses adaptive, location-based notification principles to create a geographical fence which warn users of malicious attacks.  ...  The logging component consists of a native Android application which logs replicable contextual and user behavioral data temporarily on the device until a Wi-Fi connection is established (see Table I  ... 
doi:10.1016/j.procs.2016.04.254 fatcat:4rugawxfqjaxdiazpg7brnjwhq

Triton: A Carrier-based Approach for Detecting and Mitigating Mobile Malware

Arati Baliga, Jeffrey Bickford, Neil Daswani
2014 Journal of Cyber Security and Mobility  
In this paper, we describe Triton, a new, network-based architecture, and a prototype implementation of it, for detecting and mitigating mobile malware.  ...  Our implementation of Triton for both Android and Linux environments was built in our 3G UMTS lab network, and was found to efficiently detect and neutralize mobile malware when tested using real malware  ...  Acknowledgements We thank Tufan Demir, Gerry Eisenhaur and Mike Gagnon for their help with mobile malware analysis.  ... 
doi:10.13052/jcsm2245-1439.324 fatcat:qt22obp6jndpfibojlf4dovcnq

Detection of Threats to IoT Devices using Scalable VPN-forwarded Honeypots

Amit Tambe, Yan Lin Aung, Ragav Sridharan, Martín Ochoa, Nils Ole Tippenhauer, Asaf Shabtai, Yuval Elovici
2019 Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy - CODASPY '19  
For detection and examination of potentially malicious traffic, we devise two analysis strategies: (1) given an outbound connection from honeypot, backtrack into network traffic to detect the corresponding  ...  attack command that caused the malicious connection and use it to download malware, (2) perform live detection of unseen URLs from HTTP requests using adaptive clustering.  ...  IMPLEMENTATION OF VPN-FORWARDED IOT HONEYPOT In this section, we describe the implementation of a high interaction IoT honeypot based on our proposed design.  ... 
doi:10.1145/3292006.3300024 dblp:conf/codaspy/TambeASOTSE19 fatcat:cnkc6mqnxrdj7oowy7cmk6pmxy

Scalable and Secure Architecture for Distributed IoT Systems [article]

Najmeddine Dhieb, Hakim Ghazzai, Hichem Besbes, Yehia Massoud
2020 arXiv   pre-print
We propose a novel architecture based on permissioned blockchain technology in order to build a scalable and decentralized end-to-end secure IoT system.  ...  Internet-of-things (IoT) is perpetually revolutionizing our daily life and rapidly transforming physical objects into an ubiquitous connected ecosystem.  ...  Also, we implement a deep learningbased solution to detect malware and suspected traffic.  ... 
arXiv:2005.02456v1 fatcat:culse67ylzeqlnkedwepjshq5e

The Nepenthes Platform: An Efficient Approach to Collect Malware [chapter]

Paul Baecher, Markus Koetter, Thorsten Holz, Maximillian Dornseif, Felix Freiling
2006 Lecture Notes in Computer Science  
We present the nepenthes platform, a framework for large-scale collection of information on self-replicating malware in the wild.  ...  This hampers research in these topics because many counter-strategies against malware, e.g., network-and host-based intrusion detection systems, need hard empirical data to take full effect.  ...  We are currently in the process of deploying a network intrusion detection system (NIDS) based on nepenthes.  ... 
doi:10.1007/11856214_9 fatcat:qaydx546ezhp5gi2o5f2fcc2lq

A Hybrid Real-time Zero-day Attack Detection and Analysis System

Ratinder Kaur, Maninder Singh
2015 International Journal of Computer Network and Information Security  
This paper presents a novel hybrid system that integrates anomaly, behavior and signature based techniques for detecting and analyzing zero-day attacks in real-time.  ...  Present research exhibits various issues and is not able to provide complete solution for the detection and analysis of zero-day attacks.  ...  The authors are highly obliged to the Computer Science and Engineering Department of Thapar University, Patiala for rendering their incessant help in providing best infrastructure and work-environment.  ... 
doi:10.5815/ijcnis.2015.09.03 fatcat:fac5u42jpfffhh53jpjzg7yzgq

PreCrime to the rescue

Cheng Tan, Haibo Li, Yubin Xia, Binyu Zang, Cheng-Kang Chu, Tieyan Li
2014 Proceedings of 5th Asia-Pacific Workshop on Systems - APSys '14  
PreCrime is a proactive malware detection scheme that detects and stops malware activities from happening.  ...  PreCrime creates mirrors of a mobile device in a resource-rich and trusted cloud, which speculatively executes multiple likely user operations concurrently to detect potential tampering and information  ...  . • The implementation of a scalable detection system on cloud cluster that can make full use of computing resources of the cloud to achieve low latency of malware detection.  ... 
doi:10.1145/2637166.2637224 dblp:conf/apsys/TanLXZCL14 fatcat:3fwkgejos5fjzngqswksbpttwe

Scalable fine-grained behavioral clustering of HTTP-based malware

Roberto Perdisci, Davide Ariu, Giorgio Giacinto
2013 Computer Networks  
In this paper, we present a new scalable system for network-level behavioral clustering of HTTP-based malware that aims to efficiently group newly collected malware samples into malware family clusters  ...  We implemented a proof-of-concept version of our new scalable malware clustering system and performed experiments with about 65,000 distinct malware samples.  ...  This material is partially based upon work supported by the National Science Foundation under Grant No. CNS-1149051.  ... 
doi:10.1016/j.comnet.2012.06.022 fatcat:3772btbrjvf3zdm2osh4rx5xna

Scalable malware clustering through coarse-grained behavior modeling

Mahinthan Chandramohan, Hee Beng Kuan Tan, Lwin Khin Shar
2012 Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering - FSE '12  
Due to large volume of malware samples, it has become extremely important to group them based on their malicious characteristics.  ...  In this paper, we propose a scalable malware behavior modeling technique that models the interactions between malware and sensitive system resources in a coarse-grained manner.  ...  From a set of malware samples, prototypes are selected based on a threshold value to represent the entire malware samples.  ... 
doi:10.1145/2393596.2393627 dblp:conf/sigsoft/ChandramohanTS12 fatcat:pyqjtbwia5cwzdsjx3b2yfkuta

Internet of Things Malware : A Survey

Evanson Mwangi Karanja, Shedden Masupe, Jeffrey Mandu
2017 International Journal of Computer Science & Engineering Survey  
In this paper we review literature on internet of things malware categories, support technologies, propagation and tools KEYWORDS Internet of Things (IoT), Malware, Malware synthesis, Machine to Machine  ...  Internet of Things environments poses unique challenges such as device latency, scalability, lack of antimalware tools and heterogeneity of device architectures that makes malware synthesis complex.  ...  ACKNOWLEDGEMENTS This work was supported by EU-Intra-ACP Mobility under Mobility to Enhance Training of Engineering Graduates in Africa (METEGA) grant.  ... 
doi:10.5121/ijcses.2017.8301 fatcat:dqsotxtph5cm5gmagwdxd6rmju

Improving IoT Botnet Investigation Using an Adaptive Network Layer

João Ceron, Klaus Steding-Jessen, Cristine Hoepers, Lisandro Granville, Cíntia Margi
2019 Sensors  
The proposed solution can modify the traffic at the network layer based on the actions performed by the malware.  ...  Current malware analysis solutions, when faced with IoT, present limitations in regard to the network access containment and network traffic manipulation.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s19030727 fatcat:sxqihxpo4nhsxcappheq755ydq

Robust Intelligent Malware Detection Using Deep Learning

Vinayakumar R, Mamoun Alazab, Soman KP, Prabaharan Poornachandran, Sitalakshmi Venkatraman
2019 IEEE Access  
Overall, this paper paves way for an effective visual detection of malware using a scalable and hybrid deep learning framework for real-time deployments.  ...  Recent malwares use polymorphic, metamorphic, and other evasive techniques to change the malware behaviors quickly and to generate a large number of new malwares.  ...  They would also like to thank Computational Engineering and Networking (CEN) department for encouraging the research.  ... 
doi:10.1109/access.2019.2906934 fatcat:hr4vctlh55cbhamkvh5fq2hubu
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