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Network Security and
Its Applications
Top Read Articles in
October 2020
International Journal of Network Security &
Its Applications (IJNSA)
http://airccse.org/journal/ijnsa.html
ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)
SECURITY & PRIVACY THREATS, ATTACKS AND COUNTERMEASURES IN
INTERNET OF THINGS
Faheem Masoodi1
Shadab Alam2
and Shams Tabrez Siddiqui2
1
Department of Computer Science, University of Kashmir, J&k, India 2
Department of Computer
Science, Jazan University, KSA
ABSTRACT
The idea to connect everything to anything and at any point of time is what vaguely defines the
concept of the Internet of Things (IoT). The IoT is not only about providing connectivity but also
facilitating interaction among these connected things. Though the term IoT was introduced in
1999 but has drawn significant attention during the past few years, the pace at which new
devices are being integrated into the system will profoundly impact the world in a good way but
also poses some severe queries about security and privacy. IoT in its current form is susceptible
to a multitudinous set of attacks. One of the most significant concerns of IoT is to provide
security assurance for the data exchange because data is vulnerable to some attacks by the
attackers at each layer of IoT. The IoT has a layered structure where each layer provides a
service. The security needs vary from layer to layer as each layer serves a different purpose. This
paper aims to analyze the various security and privacy threats related to IoT. Some attacks have
been discussed along with some existing and proposed countermeasures.
KEYWORDS
Internet of Things, privacy, attacks, security, threats, protocols.
For More Details : http://aircconline.com/ijnsa/V11N2/11219ijnsa05.pdf
Volume Link : http://airccse.org/journal/jnsa19_current.html
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A CONCEPTUAL SECURE BLOCKCHAIN- BASED ELECTRONIC
VOTING SYSTEM
Ahmed Ben Ayed
Department of Engineering and Computer Science, Colorado Technical University, Colorado
Springs, Colorado, USA
ABSTRACT
Blockchain is offering new opportunities to develop new types of digital services. While research
on the topic is still emerging, it has mostly focused on the technical and legal issues instead of
taking advantage of this novel concept and creating advanced digital services. In this paper, we
are going to leverage the open source Blockchain technology to propose a design for a new
electronic voting system that could be used in local or national elections. The Blockchain-based
system will be secure, reliable, and anonymous, and will help increase the number of voters as
well as the trust of people in their governments.
KEYWORDS
Blockchain, Electronic Voting System, e-Voting, I-Voting, iVote
For More Details : https://aircconline.com/ijnsa/V9N3/9317ijnsa01.pdf
Volume Link : http://airccse.org/journal/jnsa17_current.html
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AUTHORS
Ahmed Ben Ayed, has received his Bachelor of Science in Computer Information Systems,
Master of Science in Cyber Security and Information Assurance, and currently a doctoral student
at Colorado Technical University, and an Adjunct Professor at California Takshila University.
His research interests are Android Security, Pattern Recognition of Malicious Applications,
Machine Learning, Cryptography, Information & System Security and Cyber Security.
MINING PATTERNS OF SEQUENTIAL MALICIOUS APIS TO DETECT MALWARE
Abdurrahman Pektaş1
, Elif Nurdan Pektaş2
and Tankut Acarman1
1
Department of Computer Engineering, Galatasaray University, İstanbul, Turkey 2
Siemens
Turkey, Yakack Caddesi No: 111, 34870 Kartal, Istanbul, Turkey
ABSTRACT
In the era of information technology and connected world, detecting malware has been a major
security concern for individuals, companies and even for states. The New generation of malware
samples upgraded with advanced protection mechanism such as packing, and obfuscation
frustrate anti-virus solutions. API call analysis is used to identify suspicious malicious behavior
thanks to its description capability of a software functionality. In this paper, we propose an
effective and efficient malware detection method that uses sequential pattern mining algorithm to
discover representative and discriminative API call patterns. Then, we apply three machine
learning algorithms to classify malware samples. Based on the experimental results, the proposed
method assures favorable results with 0.999 F-measure on a dataset including 8152 malware
samples belonging to 16 families and 523 benign samples.
KEYWORDS
Android, Malware, Frequent Sequence Mining, Behavioural Pattern, API Calls, Dynamic
Analysis
For More Details : http://aircconline.com/ijnsa/V10N4/10418ijnsa01.pdf
Volume Link : http://airccse.org/journal/jnsa18_current.html
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AUTHORS
Abdurrahman Pektaş received his B.Sc. and M Sc. at Galatasaray University
and his PhD at the University of Joseph Fourier, all in computer engineering, in
2009, 2012 and 2015, respectively. He is a senior researcher at Galatasaray
University. His research interests are analysis, detection and classification of
malicious software, machine learning and security analysis tool development.
Elif Nurdan Pektaş received his B.Sc. and M Sc. at Galatasaray University all
in computer engineering, in 2010, and 2014, respectively. She is leading
software developer at Siemens Turkey. Her research interests are developing
IoT based applications, deep learning, cloud based application and automated
testing.
Tankut Acarman received his Ph.D. degree in Electrical and Computer
engineering from the Ohio State University in 2002. He is professor and head of
computer engineering department at Galatasaray University in Istanbul, Turkey.
His research interests lie along all aspects of autonomous s ystems, intelligent
vehicle technologies and security. He is the co-author of the book entitled
“Autonomous Ground.
PLEDGE: A POLICY-BASED SECURITY PROTOCOL FOR PROTECTING
CONTENT ADDRESSABLE STORAGE ARCHITECTURES
Wassim Itani Ayman Kayssi Ali Chehab
Department of Electrical and Computer Engineering
American University of Beirut
Beirut 1107 2020, Lebanon
ABSTRACT
In this paper we present PLEDGE, an efficient and scalable security ProtocoL for protecting
fixedcontent objects in contEnt aDdressable storaGe (CAS) architEctures. PLEDGE follows an
end-to-end policy-driven security approach to secure the confidentiality, integrity, and
authenticity of fixed-content entities over the enterprise network links and in the nodes of the
CAS device. It utilizes a customizable and configurable extensible mark-up language (XML)
security policy to provide flexible, multi-level, and fine-grained encryption and hashing
methodologies to fixed content CAS entities. PLEDGE secures data objects based on their
content and sensitivity and highly overcomes the performance of bulk and raw encryption
protocols such as the Secure Socket Layer (SSL) and the Transport Layer Security (TLS)
protocols. Moreover, PLEDGE transparently stores sensitive objects encrypted (partially or
totally) in the CAS storage nodes without affecting the CAS storage system operation or
performance and takes into consideration the processing load, computing power, and memory
capabilities of the client devices which may be constrained by limited processing power, memory
resources, or network connectivity. PLEDGE complies with regulations such as the Health
Insurance Portability and Accountability Act (HIPAA) requirements and the SEC Rule 17a-4
financial standards. The protocol is implemented in a real CAS network using an EMC Centera
backend storage device. The application secured by PLEDGE in the sample implementation is an
X-Ray radiography scanning system in a healthcare network environment. The experimental test
bed implementation conducted shows a speedup factor of three over raw encryption security
mechanisms.
KEYWORDS
Security, Content-addressable storage security, Policy-driven security, Customizable security.
For More Details : http://airccse.org/journal/nsa/1010s8.pdf
Volume Link : http://airccse.org/journal/jnsa10_current.html
REFERENCES
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AN INTELLECT LEARNING ON E-MAIL SECURITY AND FRAUD, SPAM AND
PHISHING
Dr.P.S.Jagadeesh Kumar1
, Dr.S.Meenakshi Sundaram2
, Mr.Ranjeet kumar3
1, 2, 3
Department of Computer Science and Engineering, Don Bosco Institute of Technology,
Kumbalagodu, Bangalore, India – 560074.
ABSTRACT
Cybercrime has grown voluminous pleats with veneration to the development of first-hand
technology. The flout towards cybercrime has become todays prime centric with developing
countries frugality as well. Nonetheless hefty figure of security and privacy available with
modern expertise; phishing, spam and email fraud are more equally exasperating. In this intellect
learning, the authors’ primary interest is to make a healthy charge on phishing, spam and email
fraud towards the wealthy personal information and realm. Official and business related
information needs added exhaustive sanctuary and discretion from the hackers to be on the top in
their one-to-one arena.
KEYWORDS
Cybercrime, Phishing, Spam, Email fraud, Security and Privacy, Intellect learning.
For More Details : http://airccse.org/journal/nsa/7515nsa03.pdf
Volume Link : http://airccse.org/journal/jnsa15_current.html
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AUTHORS
Dr.P.S.Jagadesh Kumar, Professor in the Department of Computer Science
and Engineering, Don Bosco Institute of Technology, Bengaluru has 16 years
of teaching experience, including 6 year of research experience in the field of
received his B.E. degree from University of Madras in Electrical and
Electronics Engineering discipline in the year 1999. He obtained his M.E
degree in 2004 with specialization in Computer Science and Engineering from
Annamalai University Ph.D. from Anna University, Chennai.
Dr.S.Meenakshi Sundaram is working as Professor and Head in the
Department of Computer Science and Engineering at India. He obtained
Bachelor Degree in Computer Science and Engineering from Bharathidasan
University in 1989. He obtained his M.Tech from National Institute of
Technology, Tiruchirappalli in 2006 and Ph.D. in Computer Science &
Engineering from Anna University Chennai in 2014. He has presented 3 papers
in International Conferences and published 17 papers in International Journals.
Mr.Ranjeet Kumar is working as an Associate Professor in the Department of
Computer Science & Engineering at Don Bosco Institute of Technology,
Bengaluru 560074. He has completed Bachelor of engineering in electrical &
electronics engineering from Kuvempu University, Shimoga, Karnataka in
2001. He has also completed his Master of Technology in Computer Science &
Engineering from Visveswaraya Technological University, Belgaum,
Karnataka in 2010.
ARCHITECTURE FOR INTRUSION DETECTION SYSTEM WITH FAULT TOLERANCE
USING MOBILE AGENT
Chintan Bhatt1
, Asha Koshti2
,Hemant Agrawal3
, Zakiya Malek4
, Dr Bhushan Trivedi5
MCA Dept.,GLS Institute of Computer Technology, Ahmedabad, India
ABSTRACT
This paper is a survey of the work, done for making an IDS fault tolerant. Architecture of IDS
that uses mobile Agent provides higher scalability. Mobile Agent uses Platform for detecting
Intrusions using filter Agent, co-relater agent, Interpreter agent and rule database. When server
(IDS Monitor) goes down, other hosts based on priority takes Ownership. This architecture uses
decentralized collection and analysis for identifying Intrusion. Rule sets are fed based on user-
behaviour or application behaviour. This paper suggests that intrusion detection system (IDS)
must be fault tolerant; otherwise, the intruder may first subvert the IDS then attack the target
system at will.
KEYWORDS
Fault tolerance, Mobile Agent, Intrusion Detection System
For More Details : http://airccse.org/journal/nsa/0911nsa13.pdf
Volume Link : http://airccse.org/journal/jnsa11_current.html
REFERENCES
[1] Lange, D., Oshima, M. 1998. Programming and Deploying Java Mobile Agents with Aglets .
Addison-Wesley.
[2] Rothermel, K., Schwehm, M. 1998. Mobile Agents . In Kent, A., Williams, J. (Editors)
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[3] Jansen, W. 1999. Mobile agents and security. In Proceedings of the 1999 Canadian
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[4] Jansen, W. 2002. Intrusion detection with mobile agents. Computer Communications ,
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World Academy of Science, Engineering and Technology 6 2005
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AGENTS International Journal of Network Security & Its Applications (IJNSA), Vol 1, No 2,
July 2009
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Wireless Networks (SNPD/SAWN’05) 0-7695-2294-7/05 © 2005 IEEE
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Mohamad Eid American University of Beirut, Department of Electrical and Computer
Engineering
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AUTHORS
Chintan Bhatt
MCA, SEM-5 Student GLSICT, Ahmedabad , India
Research Area :- Network Security
Asha Koshti
MCA, SEM-5 Student GLSICT, Ahmedabad , India
Research Area :- Network Security
Hemant Agrwal
MCA, SEM-5 Student GLSICT, Ahmedabad , India
Research Area :- Network Security
Zakiya Malek
Assistant Professor, GLSICT, Ahmedabad , India
Research Area :- Network Security
Dr. Bhushan Trivedi
Ditrector, GLSICT, Ahmedabad , India
Research Area :- Network Security
IOT AND SECURITY-PRIVACY CONCERNS: A SYSTEMATIC MAPPING STUDY
Moussa WITTI and Dimitri KONSTANTAS
Information Science Institute University of Geneva Route de Drize 7, 1227 Carouge, Switzerland
ABSTRACT
The increase of smart devices has accelerated sensitive data exchange on the Internet using most
of the time unsecured channels. Since a massive use of RFID (Radio-frequency Identification)
tags in the transportation and construction industries from 1980 to 1990, with the expanded use
of the Internet with 2G/3G or 4G since 2000, we are witnessing a new era of connected objects.
A huge number of heterogeneous sensors may collect and dispatch sensitive data from an
endpoint to worldwide network on the Internet. Privacy concerns in IOT remain important issues
in the research. In this paper, we aim to evaluate current research state related to privacy and
security in IOT by identifying existing approaches and publications trends. Therefore, we have
conducted a systematic mapping study using automated searches from selected relevant
academics databases. The result of this mapping highlights research type and contribution in
different facets and research activities trends in the topic of “security and privacy” in IoT edge,
cloud and fog environment.
KEYWORDS
Internet of Thing, privacy, security, the mapping study
For More Details : http://aircconline.com/ijnsa/V10N6/10618ijnsa03.pdf
Volume Link : http://airccse.org/journal/jnsa18_current.html
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’05). ACM,New York, NY, USA, (pp. 334-343).
AUTHORS
Moussa WITTI is a consulting engineer and IT architect in the R&D. He is
advising bank and insurance firms in content and data management. He has
more than 13 years of IT application development and deployment experience.
He has obtained an MBA from Toulouse Business School and master
Research in Computer Science from university of Franche-Comté in Besançon
(FRANCE).
Dimitri KONSTANTAS is Professor at the University of Geneva (CH) and
director of the . He has been active since 1987 in research in the areas of
Object Oriented systems, agent technologies, and mobile health systems, with
numerous publications in international conferences and journals. His current
interests are Mobile Services and Applications with special focus in the well-
being services for elderly and information security. Professor D. Konstantas
has a long participation in European research and industrial projects and is
consultant and expert to several European companies and governments.

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October 2020 - Top Read Articles in Network Security & Its Applications

  • 1. Network Security and Its Applications Top Read Articles in October 2020 International Journal of Network Security & Its Applications (IJNSA) http://airccse.org/journal/ijnsa.html ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)
  • 2. SECURITY & PRIVACY THREATS, ATTACKS AND COUNTERMEASURES IN INTERNET OF THINGS Faheem Masoodi1 Shadab Alam2 and Shams Tabrez Siddiqui2 1 Department of Computer Science, University of Kashmir, J&k, India 2 Department of Computer Science, Jazan University, KSA ABSTRACT The idea to connect everything to anything and at any point of time is what vaguely defines the concept of the Internet of Things (IoT). The IoT is not only about providing connectivity but also facilitating interaction among these connected things. Though the term IoT was introduced in 1999 but has drawn significant attention during the past few years, the pace at which new devices are being integrated into the system will profoundly impact the world in a good way but also poses some severe queries about security and privacy. IoT in its current form is susceptible to a multitudinous set of attacks. One of the most significant concerns of IoT is to provide security assurance for the data exchange because data is vulnerable to some attacks by the attackers at each layer of IoT. The IoT has a layered structure where each layer provides a service. The security needs vary from layer to layer as each layer serves a different purpose. This paper aims to analyze the various security and privacy threats related to IoT. Some attacks have been discussed along with some existing and proposed countermeasures. KEYWORDS Internet of Things, privacy, attacks, security, threats, protocols. For More Details : http://aircconline.com/ijnsa/V11N2/11219ijnsa05.pdf Volume Link : http://airccse.org/journal/jnsa19_current.html
  • 3. REFERENCES [1] J. Gubbi, R. Buyya, S. Marusic, M. Palaniswami, Internet of things (IoT): a vision, architectural elements, and future directions, Future Gener. Comput. Syst. 29 (7) (2013) 1645– 1660. [2] Roman, R., Najera, P., Lopez, J., 2011. Securing the internet of things. Computer 44 (9), 51_58. [3] Horrow, S., and Anjali, S. (2012). Identity Management Framework for Cloud-Based Internet of Things. SecurIT ’12 Proceedings of the First International Conference on Security of Internet of Things, 200– 203. 2012 [4] Whitmore, A., Agarwal, A., and Da Xu, L. (2014). The Internet of Things: A survey of topics and trends. Information Systems Frontiers, 17(2), 261– 274. [5] Aazam, M., St-Hilaire, M., Lung, C.-H., and Lambadaris, I. (2016). PRE-Fog: IoT trace based probabilistic resource estimation at Fog. 2016 13th IEEE Annual Consumer Communications and Networking Conference (CCNC), 12– 17. [6] Jiang, H., Shen, F., Chen, S., Li, K. C., and Jeong, Y. S. (2015). A secure and scalable storage system for aggregate data in IoT. Future Generation Computer Systems, 49, 133– 141. [7] Li, S., Tryfonas, T., and Li, H. (2016). The Internet of Things: a security point of view. Internet Research, 26(2), 337– 359. [8] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys Tutorials, 17(4):2347–2376, Fourth quarter 2015. [9] Pongle, P., and Chavan, G. (2015). A survey: Attacks on RPL and 6LoWPAN in IoT. 2015 International Conference on Pervasive Computing: Advance Communication Technology and Application for Society, ICPC 2015, 0(c), 0–5. [10] Tsai, C.-W., Lai, C.-F., and Vasilakos, A. V. (2014). Future Internet of Things: open issues and challenges. Wireless Networks, 20(8), 2201–2217. [11] V. Karagiannis, P. Chatzimisios, F. Vazquez-Gallego, and J. Alonso-Zarate, "A survey on application layer protocols for the internet of things," Transaction on IoT and Cloud Computing, vol. 3, no. 1, pp. 11-17, 2015 [12] D. Locke, "MQ telemetry transport (MQTT) v3. 1 protocol specification," IBM Developer WorksTechnicalLibrary,2010, http://www.ibm.com/developerworks/webservices/library/wsmqtt/index.html
  • 4. [13] M. Singh, M. Rajan, V. Shivraj, and P. Balamuralidhar, "Secure MQTT for the Internet of Things (IoT)," in Fifth International Conference on Communication Systems and Network Technologies (CSNT 2015), April 2015, pp. 746-751. [14] OASIS, "OASIS Advanced Message Queuing Protocol (AMQP) Version 1.0," 2012, http://docs.oasis-open.org/amqp/core/v1.0/os/amqp-core-complete-v1.0-os.pdf [15] T. Winter, et al., "RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks," IETF RFC 6550, Mar. 2012, http://www.ietf.org/rfc/rfc6550.txt [16] A. Aijaz and A. Aghvami, "Cognitive machine-to-machine communications for internet-of- things: A protocol stack perspective," IEEE Internet of Things Journal, vol. 2, no. 2, pp. 103-112, April 2015, [17] http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7006643 [18] Z. Zhou, B. Yao, R. Xing, L. Shu, and S. Bu, "E-CARP: An energy-efficient routing protocol for UWSNs on the internet of underwater things," IEEE Sensors Journal, vol. PP, no. 99, 2015, http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7113774 [19] D. Dujovne, T. Watteyne, X. Vilajosana, and P. Thubert, "6TiSCH: Deterministic IP- enabled industrial internet (of things)," IEEE Communications Magazine, vol. 52, no.12, pp. 36- 41, December 2014, http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6979984 [20] M. Hasan, E. Hossain, D. Niyato, "Random access for machine-to-machine communication in LTEadvanced networks: issues and approaches," in IEEE Communications Magazine, vol. 51, no. 6, pp.86-93, June 2013, http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6525600 [21] Z-Wave, "Z-Wave Protocol Overview," v. 4, May 2007, https://wiki.ase.tut.fi/courseWiki/imges/9/94/SDS10243_2_Z_Wave_Protocol_Overview.pdf [22] ZigBee Standards Organization, “ZigBee Specification,” Document 053474r17, Jan 2008, 604 pp., http://home.deib.polimi.it/cesana/teaching/IoT/papers/ZigBee/ZigBeeSpec.pdf [23] O. Cetinkaya and O. Akan, "A dash7-based power metering system," in 12th Annual IEEE Consumer Communications and Networking Conference (CCNC), Jan 2015, pp. 406-411, http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7158010 [24] Zhang, Zhi-Kai, et al. ”IoT security: ongoing challenges and research opportunities.” ServiceOriented Computing and Applications (SOCA), 2014 IEEE 7th International Conference on. IEEE, 2014. [28] D. Migault, D. Palomares, E. Herbert, W. You, G. Ganne, G. Arfaoui, and M. Laurent, “E2E: An Optimized IPsec Architecture for Secure And Fast Offload,” in Seventh International Conference on Availability, Reliability and Security E2E: 2012.
  • 5. [26] Abomhara, Mohamed, and Geir M. Køien. ”Security and privacy in the Internet of Things: Current status and open issues.” Privacy and Security in Mobile Systems (PRISMS), 2014 International Conference on. IEEE, 2014. [27] B. L. Suto, “Analyzing the Accuracy and Time Costs of Web Application Security Scanners,” San Fr., no. October 2007, 2010. [28] O. El Mouaatamid, M. LahmerInternet of Things security: layered classification of attacks and possible countermeasures Electron J (9) (2016). [29] Seda F. Gürses/Bettina Berendt/Thomas Santen, Multilateral Security Requirements Analysis for Preserving Privacy in Ubiquitous Environments, in Bettina Berendt/Ernestina Menasalvas (eds), Workshop on Ubiquitous Knowledge Discovery for Users (UKDU '06), at 51– 64; [30] Stankovic, J. (2014). Research directions for the internet of things. IEEE Internet of Things Journal, 1(1), 3–9 [31] Sicari, Sabrina, et al. "Security, privacy and trust in the Internet of Things: The road ahead." Computer Networks76 (2015): 146-164. [32]https://www.cso.com.au/article/575407/internet-things-iot-threats-countermeasures/ Accessed on 15-03-2019 [33] Bokhari, Mohammad Ubaidullah, and Faheem Masoodi. "Comparative analysis of structures and attacks on various stream ciphers." Proceedings of the 4th National Conference. 2010.
  • 6. A CONCEPTUAL SECURE BLOCKCHAIN- BASED ELECTRONIC VOTING SYSTEM Ahmed Ben Ayed Department of Engineering and Computer Science, Colorado Technical University, Colorado Springs, Colorado, USA ABSTRACT Blockchain is offering new opportunities to develop new types of digital services. While research on the topic is still emerging, it has mostly focused on the technical and legal issues instead of taking advantage of this novel concept and creating advanced digital services. In this paper, we are going to leverage the open source Blockchain technology to propose a design for a new electronic voting system that could be used in local or national elections. The Blockchain-based system will be secure, reliable, and anonymous, and will help increase the number of voters as well as the trust of people in their governments. KEYWORDS Blockchain, Electronic Voting System, e-Voting, I-Voting, iVote For More Details : https://aircconline.com/ijnsa/V9N3/9317ijnsa01.pdf Volume Link : http://airccse.org/journal/jnsa17_current.html
  • 7. REFERENCES [1] Madise, Ü. Madise and T. Martens, “E-voting in Estonia 2005. The first practice of country- wide binding Internet voting in the world.”,Electronic voting, 2nd International Workshop, Bregenz, Austria,(2006) August 2-4. [2] J. Gerlach and U. Grasser, “Three Case Studies from Switzerland: E-voting”, Berkman Center Research Publication, (2009). [3] I. S. G. Stenerud and C. Bull, “When reality comes knocking Norwegian experiences with verifiable electronic voting”, Electronic Voting. Vol. 205. (2012), pp. 21-33. [4] C. Meter and A. Schneider and M. Mauve, “Tor is not enough: Coercion in Remote Electronic Voting Systems. arXiv preprint. (2017). [5] D. L. Chaum, “Untraceable Electronic Mail, Return Addresses, and Digital Pseudonyms”, Communication of the ACM. Vol. 24(2). (1981), pp. 84-90. [6] T. ElGamal, “A public Key Cryptosystem and a Signature Scheme Based on Discrete Logarithms”, IEEE Trans. Info. Theory. Vol. 31. (1985), pp. 469-472. [7] S. Ibrahim and M. Kamat and M. Salleh and S. R. A. Aziz, “Secure E-Voting with Blind Signature”, Proceeding of the 4th National Conference of Communication Technology, Johor, Malaysia, (2003) January 14-15. [8] J. Jan and Y. Chen and Y. Lin, “The Design of Protocol for e-Voting on the Internet”, Proceedings IEEE 35th Annual 2001 International Carnahan Conference on Security Technology, London, England, (2001) October 16-19. [9] D. L. Dill and A.D. Rubin, “E-Voting Security”, Security and Privacy Magazine, Vol. 2(1). (2004), pp. 22-23. [10] D. Evans and N. Paul, “Election Security: Perception and Reality”. IEEE Privacy Magazine, vol. 2(1). (2004), pp. 2-9. [11] Trueb Baltic, “Estonian Electronic ID – Card Application Specification Prerequisites to the Smart Card Differentiation to previous Version of EstEID Card Application.” http://www.id.ee/public/TBSPEC-EstEID-Chip-App-v3_5-20140327.pdf [12] Cybernetica. “Internet Voting Solution.” https://cyber.ee/uploads/2013/03/cyber_ivoting_NEW2_A4_web.pdf. [13] D. Springall, T. Finkenauer, Z. Durumeric, J. Kitcat, H. Hursti, M. MacAlpine, and J. A. Halderman, “Security Analysis of the Estonian Internet Voting System.” Proceedings of the
  • 8. 2014 ACM SIGSAC Conference on Computer and Communications Security. (2014), pp. 703-715. [14] Ministry of Local Government and Modernisation. “Internet Voting Pilot to be Discontinued.” https://www.regjeringen.no/en/aktuelt/Internet-voting-pilot-to-be- discontinued/id764300/ [15] J. A. Halderman, and V. Teague, “The New South Wales iVote System: Security Failures and Verifications Flaws in a Live Online Election.” International Conference on E-Voting and Identity. (2015), pp. 35-53. [16] S. Wolchok, E. Wustrow, D. Isabel, J. A. Halderman, “Attacking the Washington, DC Internet Voting System.” International Conference on Financial Cryptography and Data Security (2012), pp. 114-128. [17] National Institute of Standards and Technology, “Federal Information Processing Standards Publication”, (2012). [18] S. Nakamoto, “A Peer-to-Peer Electronic Cash System”, (2008). [19] F. Reid and M. Harrigan, “An Analysis of Anonymity in the Bitcoin System”, Security and Privacy in Social Networks. (2013), pp. 1-27. [20] S. Raval, “Decentralized Applications: Harnessing Bitcoin’s Blockchain Technology.” O’Reilly Media, Inc. Sebastopol, California (2016). [21] J. R. Douceur, “The Sybil Attack”, International Workshop on Peer-to-Peer Systems, (2002), pp. 251-260. AUTHORS Ahmed Ben Ayed, has received his Bachelor of Science in Computer Information Systems, Master of Science in Cyber Security and Information Assurance, and currently a doctoral student at Colorado Technical University, and an Adjunct Professor at California Takshila University. His research interests are Android Security, Pattern Recognition of Malicious Applications, Machine Learning, Cryptography, Information & System Security and Cyber Security.
  • 9. MINING PATTERNS OF SEQUENTIAL MALICIOUS APIS TO DETECT MALWARE Abdurrahman Pektaş1 , Elif Nurdan Pektaş2 and Tankut Acarman1 1 Department of Computer Engineering, Galatasaray University, İstanbul, Turkey 2 Siemens Turkey, Yakack Caddesi No: 111, 34870 Kartal, Istanbul, Turkey ABSTRACT In the era of information technology and connected world, detecting malware has been a major security concern for individuals, companies and even for states. The New generation of malware samples upgraded with advanced protection mechanism such as packing, and obfuscation frustrate anti-virus solutions. API call analysis is used to identify suspicious malicious behavior thanks to its description capability of a software functionality. In this paper, we propose an effective and efficient malware detection method that uses sequential pattern mining algorithm to discover representative and discriminative API call patterns. Then, we apply three machine learning algorithms to classify malware samples. Based on the experimental results, the proposed method assures favorable results with 0.999 F-measure on a dataset including 8152 malware samples belonging to 16 families and 523 benign samples. KEYWORDS Android, Malware, Frequent Sequence Mining, Behavioural Pattern, API Calls, Dynamic Analysis For More Details : http://aircconline.com/ijnsa/V10N4/10418ijnsa01.pdf Volume Link : http://airccse.org/journal/jnsa18_current.html
  • 10. REFERENCES [1] Statcounter: Operating system market share worldwide, (2018). http://gs.statcounter.com/os- marketshare#monthly-201801-201801-bar. [Online; accessed 7-October-2017]. [2] Ilsun You & Kangbin Yim (2010) “Malware obfuscation techniques: A brief survey”, Broadband, Wireless Computing, Communication and Applications (BWCCA), 2010 International Conference on, pp297– 300. [3] 2016 Symantec Security Report, Internet: https://www.symantec.com/content/dam/symantec/docs/reports/istr-21-2016-en.pdf, 29.06.2018. [4] Abdurrahman Pektas & Tankut Acarman (2018) “Malware classification based on api calls and behavior analysis”, IET Information Security, Vol. 12, No.2, pp 107-117. [5] Abdurrahman Pektas & Tankut Acarman (2014) “A dynamic malware analyzer against virtual machine aware malicious software”, Security and Communication Networks, Vol. 7, No.12, pp2245–2257. [6] Nizar R Mabroukeh & Christie I Ezeife (2010) “A taxonomy of sequential pattern mining algorithms”, ACM Computing Surveys (CSUR), Vol. 43, No.1:3. [7] Philippe Fournier-Viger & Jerry Chun-Wei Lin & Rage Uday Kiran & Yun Sing Koh & Rincy Thomas (2017) “A survey of sequential pattern mining”, Data Science and Pattern Recognition, Vol.1, No.1, pp54–77. [8] Yong Qiao & Jie He & Yuexiang Yang & Lin Ji (2013) “Analyzing malware by abstracting the frequent itemsets in api call sequences”,Trust, Security and Privacy in Computing and Communications (TrustCom), 2013 12th IEEE International Conference on, pp.265–270. [9] Youngjoon Ki & Eunjin Kim & Huy Kang Kim (2015) “A novel approach to detect malware based on api call sequence analysis”, International Journal of Distributed Sensor Networks, Vol. 11, No.6,pp:95-10. [10] In Kyeom Cho & Eul Gyu Im (2015), “Extracting representative api patterns of malware families using multiple sequence alignments”, In Proceedings of the 2015 Conference on research in adaptive and convergent systems, pp.308–313. [11] Winfried Just (2001) “Computational complexity of multiple sequence alignment with sp- score”, Journal of computational biology, Vol. 8, No. 6. pp. 615–623. [12] Lusheng Wang & Tao Jiang (1994), “On the complexity of multiple sequence alignment”, Journal of computational biology, Vol. 1, No.4, p.337–348. [13] Yujie Fan &Yanfang Ye & Lifei Chen (2016), “Malicious sequential pattern mining for automatic malware detection”, Expert Systems with Applications, Vol.52, pp.16–25.
  • 11. [14] Iltaek Kwon & Eul Gyu Im (2017), “Extracting the representative api call patterns of malware families using recurrent neural network”, In Proceedings of the International Conference on Research in Adaptive and Convergent Systems, pp.202–207. [15] Canfora, G., Mercaldo, F., & Visaggio, C. A. (2016). An hmm and structural entropy based detector for android malware: An empirical study. Computers & Security, 61, 1-18. [16] Salehi, Z., Sami, A., & Ghiasi, M. (2017). MAAR: Robust features to detect malicious activity based on API calls, their arguments and return values. Engineering Applications of Artificial Intelligence, 59, 93-102. [17] Shijo, P. V., & Salim, A. (2015). Integrated static and dynamic analysis for malware detection. Procedia Computer Science, 46, 804-811. [18] Cuckoo Sandbox, Internet: https://cuckoosandbox.org/, 29.06.2018. [19] Virustotal, Internet: https://www.virustotal.com/, 29.06.2018. [20] Payam Refaeilzadeh & Lei Tang & Huan Liu (2009) “Cross-validation”, In Encyclopedia of database systems, pp.532–538, Springer. [21] A. Barthels, Behavior-based Malware Detection, Faculty of Informatics, The Technical University of Munich, Master Thesis, 2009. [22] Chand, C., Thakkar, A., & Ganatra, A. (2012). Sequential pattern mining: Survey and current research challenges. International Journal of Soft Computing and Engineering, 2(1), 185- 193. [23] Parikh, M., Chaudhari, B., & Chand, C. (2013). A comparative study of sequential pattern mining algorithms. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 2(2). [24] Mooney, C. H., & Roddick, J. F. (2013). Sequential pattern mining--approaches and algorithms. ACM Computing Surveys (CSUR), 45(2), 19. [25] Ramakrishnan Srikant & Rakesh Agrawal (1996), “Mining sequential patterns: Generalizations and performance improvements”, In International Conference on Extending Database Technology, pp.1–17, Springer. [26] Jay Ayres & Jason Flannick & Johannes Gehrke & Tomi Yiu (2002) “Sequential pattern mining using a bitmap representation”, In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.429–435. [27] Mohammed J Zaki. Spade (2001) “An efficient algorithm for mining frequent sequences. Machine learning”, Vol.42, No.1-2, pp.31–60.
  • 12. [28] Philippe Fournier-Viger &Antonio Gomariz & Ted Gueniche &Azadeh Soltani & Cheng- Wei Wu & Vincent S Tseng (2014) “Spmf: a java open-source pattern mining library”, The Journal of Machine Learning Research, Vol.15, No.1, pp.3389–3393. [29] SPMF library, Internet: http://www.philippe-fournier-viger.com/spmf/, 29.06.2018. [30] Philippe Fournier-Viger & Antonio Gomariz & Manuel Campos & Rincy Thomas (2014) “Fast vertical mining of sequential patterns using co-occurrence information”, In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp.40–52, Springer. [31] Gandotra, E., Bansal, D., & Sofat, S. (2014). Malware analysis and classification: A survey. Journal of Information Security, 5(02), 56. [32] Leo Breiman (2001) “Random forests”, Machine learning, Vol.45, No.1, pp.5–32. [33] Padraig Cunningham & Sarah Jane Delany (2007) “k-nearest neighbour classifiers”, Multiple Classifier Systems, Vol.34, pp.1–17. [34] Marti A. Hearst & Susan T Dumais & Edgar Osuna & John Platt & Bernhard Scholkopf (1998), “Support vector machines”, IEEE Intelligent Systems and their applications, Vol. 13, No.4, pp.18–28. [35] Fabian Pedregosa & Gaël Varoquaux &Alexandre Gramfort & Vincent Michel & Bertrand Thirion & Olivier Grisel & Mathieu Blondel & Peter Prettenhofer &Ron Weiss &Vincent Dubourg (2011) “Scikit-learn: Machine learning in python”, Journal of machine learning research, Vol. 12, pp.2825–2830. [36] Hossin, M., & Sulaiman, M. N. (2015). A review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2), 1. [37] Yiming Yang (1999) “An evaluation of statistical approaches to text categorization”, Information retrieval, Vol.1, No. 1-2, pp.69–90. [38] Thomas G Dietterich (1998), “Approximate statistical tests for comparing supervised classification learning algorithms”, Neural computation, Vol.10, No.7, pp.1895–1923. AUTHORS Abdurrahman Pektaş received his B.Sc. and M Sc. at Galatasaray University and his PhD at the University of Joseph Fourier, all in computer engineering, in 2009, 2012 and 2015, respectively. He is a senior researcher at Galatasaray University. His research interests are analysis, detection and classification of malicious software, machine learning and security analysis tool development.
  • 13. Elif Nurdan Pektaş received his B.Sc. and M Sc. at Galatasaray University all in computer engineering, in 2010, and 2014, respectively. She is leading software developer at Siemens Turkey. Her research interests are developing IoT based applications, deep learning, cloud based application and automated testing. Tankut Acarman received his Ph.D. degree in Electrical and Computer engineering from the Ohio State University in 2002. He is professor and head of computer engineering department at Galatasaray University in Istanbul, Turkey. His research interests lie along all aspects of autonomous s ystems, intelligent vehicle technologies and security. He is the co-author of the book entitled “Autonomous Ground.
  • 14. PLEDGE: A POLICY-BASED SECURITY PROTOCOL FOR PROTECTING CONTENT ADDRESSABLE STORAGE ARCHITECTURES Wassim Itani Ayman Kayssi Ali Chehab Department of Electrical and Computer Engineering American University of Beirut Beirut 1107 2020, Lebanon ABSTRACT In this paper we present PLEDGE, an efficient and scalable security ProtocoL for protecting fixedcontent objects in contEnt aDdressable storaGe (CAS) architEctures. PLEDGE follows an end-to-end policy-driven security approach to secure the confidentiality, integrity, and authenticity of fixed-content entities over the enterprise network links and in the nodes of the CAS device. It utilizes a customizable and configurable extensible mark-up language (XML) security policy to provide flexible, multi-level, and fine-grained encryption and hashing methodologies to fixed content CAS entities. PLEDGE secures data objects based on their content and sensitivity and highly overcomes the performance of bulk and raw encryption protocols such as the Secure Socket Layer (SSL) and the Transport Layer Security (TLS) protocols. Moreover, PLEDGE transparently stores sensitive objects encrypted (partially or totally) in the CAS storage nodes without affecting the CAS storage system operation or performance and takes into consideration the processing load, computing power, and memory capabilities of the client devices which may be constrained by limited processing power, memory resources, or network connectivity. PLEDGE complies with regulations such as the Health Insurance Portability and Accountability Act (HIPAA) requirements and the SEC Rule 17a-4 financial standards. The protocol is implemented in a real CAS network using an EMC Centera backend storage device. The application secured by PLEDGE in the sample implementation is an X-Ray radiography scanning system in a healthcare network environment. The experimental test bed implementation conducted shows a speedup factor of three over raw encryption security mechanisms. KEYWORDS Security, Content-addressable storage security, Policy-driven security, Customizable security. For More Details : http://airccse.org/journal/nsa/1010s8.pdf Volume Link : http://airccse.org/journal/jnsa10_current.html
  • 15. REFERENCES [1] W. Itani, A. Kayssi, A. Chehab, “An efficient and scalable Security ProtocoL for protecting fixedContent Objects in ContEnt aDdressable StoraGe architectures”, in Proc. of the Third International Conference on Security and Privacy in Communication Networks, Nice, France, Sept, 2007. [2] A. Freier, P. Karlton, P. Kocher, “The SSL Protocol Version 3.0, ” Internet-Draft, 1996. [3] T. Dierks, C. Allen, “The TLS Protocol – Version 1.0, ” RFC 2246, 1999. [4] EMC Centera homepage: http://www.emc.com/products/family/emc-centera-family.htm, accessed Jan. 14, 2008. [5] Health Insurance Portability & Accountability Act homepage: http://www.hipaa.org, accessed Jan. 14, 2008. [6] SEC 17 CFR Part 240, Release No. 34-38245, “Reporting Requirements for Broker Dealers Under the Security Exchange Act of 1934,” January 1997, http://www.sec.gov/rules/final/34- 38245.txt, accessed Jan. 14, 2008. [7] J. Daemen and V. Rijmen, “Rijndael, the advanced encryption standard,” Dr. Dobb's Journal, vol. 26, no. 3, March 2001, pp. 137 - 139. [8] National Institute of Standards and Technology, August 2002, Secure Hash Standard, Federal Information Processing Standards, Publication 180-2, http://csrc.nist.gov/publications/fips/fips180-2/fips180-2withchangenotice.pdf, accessed Jan. 14, 2008. [9] Y. Zheng, J. Pieprzyk, J. Seberry, “HAVAL--A One-Way Hashing Algorithm with Variable Length of Output,” in Proc. Workshop on the Theory and Application of Cryptographic Techniques: Advances in Cryptology, pp. 83-104, 1992. [10] R. Rivest, “The MD5 Message-Digest Algorithm,” RFC 1321, 1992. [11] W. Itani, A. Kayssi, “J2ME End-to-End Security for M-Commerce,” in Proc IEEE Wireless Communications and Networking Conference, 2003. [12] W. Itani, A. Kayssi, “SPECSA: a Scalable, Policy-driven, Extensible, and Customizable Security Architecture for Wireless Enterprise Applications,” Computer Communications, vol. 27, no. 18, December 2004, pp. 1825 - 1839. [13] W. Itani, A. Kayssi, A. Chehab, “PATRIOT – a Policy-Based, Multi-level Security Protocol for Safekeeping Audit Logs on Wireless Devices,” in Proc. IEEE/CreateNet First
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  • 18. AN INTELLECT LEARNING ON E-MAIL SECURITY AND FRAUD, SPAM AND PHISHING Dr.P.S.Jagadeesh Kumar1 , Dr.S.Meenakshi Sundaram2 , Mr.Ranjeet kumar3 1, 2, 3 Department of Computer Science and Engineering, Don Bosco Institute of Technology, Kumbalagodu, Bangalore, India – 560074. ABSTRACT Cybercrime has grown voluminous pleats with veneration to the development of first-hand technology. The flout towards cybercrime has become todays prime centric with developing countries frugality as well. Nonetheless hefty figure of security and privacy available with modern expertise; phishing, spam and email fraud are more equally exasperating. In this intellect learning, the authors’ primary interest is to make a healthy charge on phishing, spam and email fraud towards the wealthy personal information and realm. Official and business related information needs added exhaustive sanctuary and discretion from the hackers to be on the top in their one-to-one arena. KEYWORDS Cybercrime, Phishing, Spam, Email fraud, Security and Privacy, Intellect learning. For More Details : http://airccse.org/journal/nsa/7515nsa03.pdf Volume Link : http://airccse.org/journal/jnsa15_current.html
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  • 22. Engineering and Electronic Business, 2015, Vol.2, pp.60-65, Published Online March 2015 in MECS (http://www.mecs-press.org/)DOI: 10.5815/ijieeb.2015.02.08 [33] Ram B.Basnet, Andrew H.Sung, Quingzhong Liu (2014) “Learning to detect phishing URLs”, International Journal of Research in Engineering and Technology, Vol.3 Issue.6, pp.11-24, Available @ http://www.ijret.org [34] R. Dhanalakshmi, C. Chellappan, Quingzhong Liu (2012) “Mitigating E-Mail Threats - A Web Content BasedApplication”, Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol.1, IMECS’12, Hong Kong. [35] R. Dhanalakshmi, C. Chellappan, Quingzhong Liu (2012) “Evolving Fuzzy Neural Network for Phishing Emails Detection”, Journal of Computer Science 8 (7): pp.1099-1107, ISSN 1549-3636©Science Publications. [36] Goverdhan Reddy Jidiga, Dr.P Sammulal, (2013) “Machine learning approach to anomaly detection incyber security with a case study of spamming attack”, International Journal of Computer Engineering& Technology, Vol.4, Issue.3, May-June (2013), pp. 113- 122, © IAEME: www.iaeme.com/ijcet.asp [37] Kurt Thomas, Chris Grier, Justin Ma, Vern Paxson, Dawn Song (2011) “Design and Evaluation of a Real-Time URL Spam Filtering Service”, IEEE Symposium on Security and Privacy, pp.447-462, 1081-6011/11© 2011 IEEE, DOI: 10.1109/SP.2011.25 [38] Steve Sheng, Mandy Holbrook, Ponnurangam Kumaraguru, Lorrie Cranor, Julie Downs (2010) “Who Falls for Phish? A Demographic Analysis of PhishingSusceptibility and Effectiveness of Interventions”, Atlanta, Georgia, USA.Copyright 2010 ACM 978-1-60558- 929-9/10/04. [39] Yue Zhang, Jason Hong, Lorrie Cranor (2007) “CANTINA: A Content-Based Approach toDetecting Phishing Web Sites”, International World Wide Web ConferenceCommittee, May 8–12, 2007, Banff, Alberta, Canada, ACM 978-1-59593-654-7/07/0005 [40] Ram B.Basnet,Andrew H.Sung (2010) “Classifying Phishing Emails Using Confidence- Weighted Linear Classifiers”, International Conference on Information Security and Artificial Intelligence (ISAI 2010),978-1-4244-8870-4 /10 C IEEE, pp.108-112. [41] Michalis Polychronakis, Panayiotis Mavrommatis, Niels Provos (2010) “Ghost turns Zombie: Exploring the Life Cycle of Web-based Malware”, https://www.usenix.org/legacy/event/leet08/tech/full_papers/polychronakis/polychronakis.pd f [42] JaeSeung Song and Andreas Kunz (2013) “Towards Standardized Prevention ofUnsolicited Communications and PhishingAttacks”, Journal of ICT Standardization, Vol. 1, PP.109–122, River Publishers, DOI: 10.13052/jicts2245-800X .126.
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  • 24. AUTHORS Dr.P.S.Jagadesh Kumar, Professor in the Department of Computer Science and Engineering, Don Bosco Institute of Technology, Bengaluru has 16 years of teaching experience, including 6 year of research experience in the field of received his B.E. degree from University of Madras in Electrical and Electronics Engineering discipline in the year 1999. He obtained his M.E degree in 2004 with specialization in Computer Science and Engineering from Annamalai University Ph.D. from Anna University, Chennai. Dr.S.Meenakshi Sundaram is working as Professor and Head in the Department of Computer Science and Engineering at India. He obtained Bachelor Degree in Computer Science and Engineering from Bharathidasan University in 1989. He obtained his M.Tech from National Institute of Technology, Tiruchirappalli in 2006 and Ph.D. in Computer Science & Engineering from Anna University Chennai in 2014. He has presented 3 papers in International Conferences and published 17 papers in International Journals. Mr.Ranjeet Kumar is working as an Associate Professor in the Department of Computer Science & Engineering at Don Bosco Institute of Technology, Bengaluru 560074. He has completed Bachelor of engineering in electrical & electronics engineering from Kuvempu University, Shimoga, Karnataka in 2001. He has also completed his Master of Technology in Computer Science & Engineering from Visveswaraya Technological University, Belgaum, Karnataka in 2010.
  • 25. ARCHITECTURE FOR INTRUSION DETECTION SYSTEM WITH FAULT TOLERANCE USING MOBILE AGENT Chintan Bhatt1 , Asha Koshti2 ,Hemant Agrawal3 , Zakiya Malek4 , Dr Bhushan Trivedi5 MCA Dept.,GLS Institute of Computer Technology, Ahmedabad, India ABSTRACT This paper is a survey of the work, done for making an IDS fault tolerant. Architecture of IDS that uses mobile Agent provides higher scalability. Mobile Agent uses Platform for detecting Intrusions using filter Agent, co-relater agent, Interpreter agent and rule database. When server (IDS Monitor) goes down, other hosts based on priority takes Ownership. This architecture uses decentralized collection and analysis for identifying Intrusion. Rule sets are fed based on user- behaviour or application behaviour. This paper suggests that intrusion detection system (IDS) must be fault tolerant; otherwise, the intruder may first subvert the IDS then attack the target system at will. KEYWORDS Fault tolerance, Mobile Agent, Intrusion Detection System For More Details : http://airccse.org/journal/nsa/0911nsa13.pdf Volume Link : http://airccse.org/journal/jnsa11_current.html
  • 26. REFERENCES [1] Lange, D., Oshima, M. 1998. Programming and Deploying Java Mobile Agents with Aglets . Addison-Wesley. [2] Rothermel, K., Schwehm, M. 1998. Mobile Agents . In Kent, A., Williams, J. (Editors) Encyclopedia for Computer Science and Technology . M. Dekker Inc. New York, USA [3] Jansen, W. 1999. Mobile agents and security. In Proceedings of the 1999 Canadian Information Technology Security Symposium . [4] Jansen, W. 2002. Intrusion detection with mobile agents. Computer Communications , 25(15): 1392-1401. [5] Jansen, W., Karygiannis, T. 1999. Mobile agent security. Special Publication 800 19, National Institute of Standards and Technology (NIST). [6] Jansen, W., Mell, P., Karygiannis, T., Marks, D. 1999. Applying mobile agents to intrusion detection and response. Interim Report 6416, National Institute of Standards and Technology (NIST) [7] Intelligent Agents for Distributed Intrusion Detection System M. Benattou, and K. Tamine World Academy of Science, Engineering and Technology 6 2005 [8] Intrusion Detection & Prevention by Carl Endorf, Eugene Schultz and Jim Mellander ISBN:0072229543 TMH pub. [9] INTELLIGENT INTRUSION DETECTION SYSTEM FRAMEWORK USING MOBILE AGENTS International Journal of Network Security & Its Applications (IJNSA), Vol 1, No 2, July 2009 [10] DIDMA: A Distributed Intrusion Detection System Using Mobile Agents Pradeep Kannadiga and Mohammad Zulkernine School of Computing Queen’s University Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (SNPD/SAWN’05) 0-7695-2294-7/05 © 2005 IEEE [11] Applying Mobile Agents to Intrusion Detection and Response Wayne Jansen, Peter Mell, Tom Karygiannis, Don Marks National Institute of Standards and Technology Computer Security Division NIST Interim Report (IR) – 6416 October 1999 [12] An Architecture for Intrusion Detection using Autonomous Agents Jai Sundar Balasubramaniyan, Jose Omar Garcia-Fernandez,DavidIsaco, Eugene Spafford, Diego Zamboniy Center for Education and Research in Information Assurance and Security Purdue University CERIAS Technical Report 98/05 June 11, 1998
  • 27. [13] A New Mobile Agent-Based Intrusion Detection System Using Distributed Sensors Mohamad Eid American University of Beirut, Department of Electrical and Computer Engineering [14] Snort website: www.snort.org AUTHORS Chintan Bhatt MCA, SEM-5 Student GLSICT, Ahmedabad , India Research Area :- Network Security Asha Koshti MCA, SEM-5 Student GLSICT, Ahmedabad , India Research Area :- Network Security Hemant Agrwal MCA, SEM-5 Student GLSICT, Ahmedabad , India Research Area :- Network Security Zakiya Malek Assistant Professor, GLSICT, Ahmedabad , India Research Area :- Network Security Dr. Bhushan Trivedi Ditrector, GLSICT, Ahmedabad , India Research Area :- Network Security
  • 28. IOT AND SECURITY-PRIVACY CONCERNS: A SYSTEMATIC MAPPING STUDY Moussa WITTI and Dimitri KONSTANTAS Information Science Institute University of Geneva Route de Drize 7, 1227 Carouge, Switzerland ABSTRACT The increase of smart devices has accelerated sensitive data exchange on the Internet using most of the time unsecured channels. Since a massive use of RFID (Radio-frequency Identification) tags in the transportation and construction industries from 1980 to 1990, with the expanded use of the Internet with 2G/3G or 4G since 2000, we are witnessing a new era of connected objects. A huge number of heterogeneous sensors may collect and dispatch sensitive data from an endpoint to worldwide network on the Internet. Privacy concerns in IOT remain important issues in the research. In this paper, we aim to evaluate current research state related to privacy and security in IOT by identifying existing approaches and publications trends. Therefore, we have conducted a systematic mapping study using automated searches from selected relevant academics databases. The result of this mapping highlights research type and contribution in different facets and research activities trends in the topic of “security and privacy” in IoT edge, cloud and fog environment. KEYWORDS Internet of Thing, privacy, security, the mapping study For More Details : http://aircconline.com/ijnsa/V10N6/10618ijnsa03.pdf Volume Link : http://airccse.org/journal/jnsa18_current.html
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