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RESEARCH INTERESTS

Big Data mining: Cloud-based mining of Big Data, Big Data analytics for healthcare, healthcare decision support systems and data mining and machine learning applications.

Data Stream mining: Data stream classification and clustering, coping with concept-drift in data streams, classification and novel class detection in concept-drifting data streams, text stream classification, classification and clustering of data streams with dynamic feature space, active mining of data streams, and semi-supervised learning in data streams.

Cyber Security:  malware and intrusion detection using data mining, network security solutions with data mining.

 

PUBLICATIONS

Links to DBLP, Google Scholar

Book:

 

 

 

 

 

 

 

 

 

Book: Bhavani Thuraisingham, Mehedy Masud, Pallavi Parveen, Latifur Khan, “Big Data Analytics with Applications in Insider Threat Detection”. In preparation.

 

Patent:

Principal inventor of US Patent pending “Systems and Methods for Detecting a Novel Data Class”. Applied by UT Dallas, Ref. no UTD-10-017, Application number 61376427, Approved Sep 2015.

 

Research Grants

UAEU Startup Grant

[G1] Co-PI, “Privacy protection of biomedical data in the cloud”. UAEU startup grant, amount 395,000 AED, Award date: January 2014 (running).

UAEU Interdisciplinary Grant

[G2] PI,Building the Foundation of an Intelligent Healthcare Information System for UAE”.  UAEU Inter-disciplinary grant (externally evaluated). Amount 498,000 AED, Award date: January 2015 (running).

External Grants

[G3] PI, “Biometrics based highly Secured Networks (Bio-Networks)”. UAEU-NRF grant (externally evaluated).  Award amount AED 320,000, Award date: February 2013 (completed).

[G4] Co-PI, “Health Monitoring System”, ICT fund project in collaboration with the College of Medicine and Health Sciences (CMHS) and several US Universities. Award amount AED 2,728,948, Award date: July 2014 (running).

Total grant Awarded in running and completed projects = AED 3,941,948 ~ $US 1,074,100

 

Articles in Refereed Journals

[J14] Mohammad M. Masud, Mohamed Adel Serhani, Alramzana Nujum Navaz. Resource-Aware Mobile-Based Health Monitoring. IEEE Journal of Biomedical and Health Informatics. Accepted, Jan 2016. (SJR: 2.864, ISI IF: 1.44).

[J13] Noura AlNuaimi, Mohammad M Masud and Farhan Mohammed. Examining The Effect Of Feature Selection On Improving Patient Deterioration Prediction. International Journal of Data Mining & Knowledge Management Process (IJDKP), Vol.5, No.6, Nov 2015.

[J12] Tahseen Al-Khateeb, Mohammad M. Masud, Khaled Al-Naami, Sadi Evren Seker, Latifur Khan, Charu Aggarwal, Jiawei Han. Recurring and Novel Class Detection using Class-Based Ensemble for Evolving Data Stream. IEEE Transactions on Knowledge and Data Engineering. Accepted, Nov 2015. (SJR: 3.023, ISI IF: 2.067)

[J11] Zouheir Trabelsi, Safaa zeidan , Mohammad M. Masud , Kilani Ghoudi. Statistical dynamic splay tree filters towards multilevel firewall packet filtering enhancement. Computers & Security. Volume 53, 109–131 (2015). (SJR: 1.051, ISI IF: 1.931).

[J10] Zouheir Trabelsi, Mohamed Al Hemairy, Mohammad M. Masud. On Investigating the Effectiveness of Biometric Readers in Thwarting Network Attacks: A Secure Architecture Design Proposal. Journal of Intelligent Systems 24 (2), 199-213 (2015). (SJR: 0.158, h-index: 10)

[J9] Mohammad M. Masud, Q. Chen, L. Khan, C. C. Aggarwal, J. Gao, J. Han, A. Srivastava, N. C. Oza. Classification and Novel Class Detection of Feature-Evolving Data Streams. IEEE Trans. on Knowledge and Data Engineering, 25(7):1484-1497 (2013). (SJR: 3.023, ISI IF: 1.815, non-self-citations: 27)

[J8] Bhavani M. Thuraisingham, Tahseen Al-Khateeb, Latifur Khan, Mehedy Masud, Kevin W. Hamlen, Vaibhav Khadilkar, Satyen Abrol: Design and Implementation of a Data Mining System for Malware Detection. Journal of Integrated Design & Process Science 16(2): 33-49 (2012). (SJR 0.123, h-index: 8).

[J7] Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, and Bhavani M. Thuraisingham. Facing the reality of data stream classification: Coping with scarcity of labeled data. Journal of Knowledge and Information Systems (KAIS), 33(1), pp. 213-244 (2012). (SJR: 2.812, ISI IF: 2.225, non-self-citations: 33).

[J6] Mohammad M. Masud, Tahseen M. Al-Khateeb,  Kevin W. Hamlen, Jing Gao, Latifur Khan, and Jiawei Han. Cloud based Malware Detection for Evolving Data Streams. ACM Transactions on Management Information Systems 2(3), pp. 16 (2011). (SJR: 0.7, h-index: 10, non-self-citations: 17)

[J5] Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, and Bhavani M. Thuraisingham. Classification and novel class detection in concept-drifting data streams under time constraints. IEEE Transactions on Knowledge and Data Engineering (TKDE) 23(6), pp. 859-874 (2011). (SJR: 2.455, ISI IF: 1.657, non-self-citations: 125)

[J4] Mohammad Husain, James McGothlin, Mohammad M. Masud, Latifur Khan, and Bhavani Thuraisingham. Heuristics-based Query Processing for Large RDF Graphs Using Cloud Computing. IEEE Transactions on Knowledge and Data Engineering (TKDE) 23(9),pp. 1312-1327 (2011). (SJR: 2.455, ISI IF: 1.657, non-self-citations: 117).

[J3] Kevin W. Hamlen, Vishwath Mohan, Mohammad M. Masud, Latifur Khan, and Bhavani M. Thuraisingham. Exploiting an antivirus interface. Computer Standards and Interfaces 31(6), pp. 1182-1189 (2009). (SJR: 0.744, ISI IF: 1.373, non-self-citations: 13).

[J2] Mohammad M. Masud, Latifur Khan and Bhavani Thuraisingham. A Scalable Multi-level Feature Extraction Technique to Detect Malicious Executables. Information System Frontiers 10(1): 33-45, 2008. (SJR: 0.68, ISI IF: 0.7, non-self-citations: 16)

[J1] Mohammad M. Masud, Latifur Khan and Bhavani Thuraisingham. Efficient Auto-Detection of Novel Email Worms using Feature based Techniques. International Journal of Information Security and Privacy 1(4):47-61, 2007. (SJR: 0.118, h-index: 4)

Under review

Ahsanul Haque, Latifur Khan, Michael Baron, Mohammad Masud, Sadi Evren Seker. Adaptive Framework for Classification and Novel Class Detection over Evolving Data Streams with Limited Labeled Data". IEEE Transactions on Knowledge and Data Engineering. Submitted. (SJR: 3.023, ISI IF: 2.067).

 

Note:

ISI IF and SJR are provided for the year the paper was published in.

Non-self-citations are as per Google Scholar

Journal h-index is as per Scopus

 

Refereed Conference Proceedings

[C29] Ahsanul Haque, Latifur Khan, Michael Baron, Mohammad M. Masud, and Charu Aggarwal. Efficient Semi-supervised Adaptive Classification and Novel Class Detection over Data Stream. To appear in Proceedings of the 32nd IEEE International Conference on Data Engineering (ICDE ‘2016). (Class A* Conferencej).

[C28] Noura AlNuaimi, Mohammad M. Masud and Farhan Mohammed. ICU Patient Deterioration prediction: a Data-Mining Approach. Fourth Intl. Conf. on Data Mining and Knowledge Management Process, Dubai, UAE Nov 6-7, CoRR abs/1511.06910 (2015).

[C27] Mohammad M. Masud, Ameera Al-Shehhi, Eiman Al-Shamsi, Shamma Al-Hassani, Asmaa Al-Hamoudi, Latifur Khan. Online Prediction of Chess Match Result. In PAKDD '15: Proceedings of The 19th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2015), pages 525-537, Ho-Chi-Minh City, Vietnam, 19-22 May 2015. (Acceptance rate: 22%, Class A Conferencej).

[C26] Hind Al Falasi, Mohammad M. Masud, Nader Mohamed. Trusting the Same: Using Similarity to Establish Trust among Vehicles. In CTS '15: Proceedings of 2015 International Conference on Collaboration Technologies and Systems (CTS 2015), pages 64-69, June 01-05, 2015, Atlanta, Georgia, USA. (Class C Conference). (non-self-citations: 2)

[C25] L. Ismail, Mohammad M. Masud, Latifur Khan. FSBD: A Framework for Scheduling of Big Data Mining in Cloud Computing. In Proceedings of 2014 IEEE International Congress on Big Data (Big Data Congress 2014), Anchorage, Alaska, July 2014, pp 514-521. (non-self-citations: 2).

[C24] Mohammad M. Masud, Zouheir Trabelsi, and Umniya Mustafa. A Data Driven FireWall for Faster Packet Filtering, In COMNET ’14: Proceedings of the fourth International Conference on Communications and Networking (COMNET 2014), Hammamet, Tunisia, March 19-22, 2014.

[C23] Zouheir Trabelsi, Mohammed Al Hemairy, and Mohammad M. Masud. Resilience of Fingerprint and Iris Readers against Common Denial of Service Attacks, In WCCAIS ’14: Proceedings of the World Congress On Computer Applications and Information Systems (WCCAIS 2014), Hammamet, Tunisia, January 17-19, 2014.

[C22] Umniya Mustafa, Mohammad M. Masud, Zouheir Trabelsi, Timothy Wood and Zainab Al Harthi. Firewall Performance Optimization using Data Mining Techniques, In IWCMC '13: Proceedings of The 9th Intl Wireless Communications and Mobile Computing Conference (IWCMC 2013), Sardinia, Italy, July 1-5, pp 934-940, 2013. (Class B Conference, non-self-citations: 5).

[C21] Tahseen Al-Khateeb, Mohammad M. Masud, Latifur Khan, Charu C. Aggarwal, Jiawei Han, Bhavani M. Thuraisingham. Detecting Recurring and Novel Classes in Concept-Drifting Data Streams, In ICDM '12: Proceedings of the IEEE International Conference on Data Mining (IEEE ICDM 2012), pp. 31-40, Brussels, Belgium, Dec 2012. (Acceptance rate: 19.9%, Class A* Conference, non-self-citation: 20).

[C20] Tahseen Al-Khateeb, Mohammad M. Masud, Latifur Khan, Bhavani M. Thuraisingham, Cloud Guided Stream Classification Using Class-Based Ensemble. In CLOUD '12: Proceedings of the 5th International Conference on Cloud Computing (IEEE CLOUD 2012), pp. 694-701, Hawaii, USA, June 2012. (Class B conference, non-self-citations: 20)

[C19] Mohammad M. Masud, Tahseen Al-Khateeb, Latifur Khan, Charu C. Aggarwal, Jing Gao, Jiawei Han, Bhavani M. Thuraisingham. Detecting Recurring and Novel Classes in Concept-Drifting Data Streams. In ICDM '11: Proceedings of the IEEE International Conf. on Data Mining (ICDM 2011), pp. 1176-1181, Vancouver, Canada, Dec 2011. (Acceptance rate: 18%, Class A* Conference, non-self-citations: 21).

[C18] Altaher, A., Ramadass, S., Thuraisingham, B., Mehedy, M. On-line anomaly detection based on relative entropy. In IC-BNMT' 11: Proceedings of the 4th IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT), pp. 33-36, Oct 26-28, 2011. (non-self-citations: 1).

[C17] Mohammad M. Masud, Qing Chen, Jing Gao, Latifur Khan, Charu Aggarwal, Jiawei Han, and Bhavani Thuraisingham. Addressing Concept-Evolution in Concept-Drifting Data Streams. In ICDM '10: Proceedings of the IEEE International Conference on Data Mining (ICDM 2010), pages 929-934, Sydney, Australia, December 14-17, 2010. (Acceptance rate: 19.4%, Class A* Conference, non-self-citations: 54).

[C16] Mohammad M. Masud, Qing Chen, Jing Gao, Latifur Khan, Jiawei Han, and Bhavani Thuraisingham. Classification and Novel Class Detection of Data Streams in A Dynamic Feature Space. In ECML PKDD '10: Proceedings of the 2010 European Conference on Machine Learning and Principles and Practice in Knowledge Discovery in Databases (ECML/PKDD ’10), vol 2, pages 337-352, Barcelona, Spain, Sep 20-24, 2010. (Acceptance rate: 18.2%, Class A Conference, non-self-citations: 36).

[C15] Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, and Bhavani M. Thuraisingham. Classification and Novel Class Detection in Data Streams with Active Mining. In PAKDD '10: Proceedings of The 14th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2010), pages 311-324, Hyderabad, India, 21-24 June 2010. (Acceptance rate: 23.3%, Class A Conference, non-self-citations: 18).

[C14] Tahseen Al-Khateeb, Mohammad Salim Ahmed, Mohammad Masud and Latifur Khan. A Data Intensive Multi-chunk Ensemble Technique to Classify Stream Data Using Map-Reduce Framework. Workshop on High Performance Analytics - Algorithms, Implementations, and Applications. Columbus, Ohio, USA, May 1st, 2010.

[C13] ClayWoolam, Mohammad M. Masud, and Latifur Khan. Lacking labels in the stream: Classifying evolving stream data with few labels. In ISMIS '09: Proceedings of the 18th International Symposium on Methodologies for Intelligent Systems (ISMIS 2009), pages 552-562, Prague, Czech Republic, 14-17 Sep 2009. (non-self-citations: 19).

[C12] Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, and Bhavani M. Thuraisingham. Integrating novel class detection with classification for concept-drifting data streams. In ECML PKDD '09: Proceedings of the 2009 European Conference on Machine Learning and Principles and Practice in Knowledge Discovery in Databases (ECML/PKDD 2009), vol 2, pages 79-94, Bled, Slovenia, 7-11 Sep 2009. (Acceptance rate: 24.9%, Class A Conference, non-self-citations: 47)

[C11] Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, and Bhavani M. Thuraisingham. A multi-partition multi-chunk ensemble technique to classify concept-drifting data streams. In PAKDD '09: Proceedings of the 13th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009), pages 363-375, Bangkok, Thailand, 27-30 Apr 2009. (Acceptance rate: 21.3%, Class A Conference, non-self-citations: 37).

[C10] Mohammad M. Masud, Latifur Khan, Bhavani M. Thuraisingham, Xinran Wang, Peng Liu, and Sencun Zhu. Detecting remote exploits using data mining. In Sujeet Shenoi Indrajit Ray, ed., IFIP Intl. Federation for Info. Processing, vol 285; Advances in Digital Forensics IV, pp 177-189, 2008. (non-self-citations: 5).

[C9] Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, Bhavani Thuraisingham. Peer to peer botnet detection for cyber-security: a data mining approach. In CSIIRW '08: Proceedings of the 4th annual workshop on Cyber security and information intelligence research (CSIIRW): developing strategies to meet the cyber security and information intelligence challenges ahead. Article No. 39. Oak Ridge, Tennessee, USA, May 12-14, 2008. (non-self-citations: 17).

[C8] Bhavani M. Thuraisingham, Latifur Khan, Mohammad M. Masud, Kevin W. Hamlen. Data Mining for Security Applications. In EUC '08: Proceedings of the International Conference On Embedded and Ubiquitous Computing (EUC 2008), vol 2, pages 585-589, Shanghai, China, 17-20 Dec 2008. (Class C Conference, non-self-citations: 21).

[C7] Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, and Bhavani M. Thuraisingham. A practical approach to classify evolving data streams: Training with limited amount of labeled data. In ICDM '08: Proceedings of the 2008 International Conference on Data Mining (ICDM 2008), pages 929-934, Pisa, Italy, 15-19 Dec 2008. (Acceptance rate: 19.9%, Class A* Conference, non-self-citations: 85)

[C6] Mohammad M. Masud, Tahseen Al-khateeb, Latifur Khan, Bhavani M. Thuraisingham, and Kevin W. Hamlen. Flow-based identification of botnet traffic by mining multiple log files. In DFMA '08: Proceedings of the 2008 International Conference on Distributed Frameworks and Multimedia Applications (DFMA 2008), pages 200-206, Penang, Malaysia, 21-22 October 2008. (Class C conference, non-self-citations: 51).

[C5] Mohammad M. Masud, Latifur Khan, and Bhavani M. Thuraisingham. Feature based techniques for auto-detection of novel email worms. In PAKDD '07: Proceedings of the 11th Pacific-Asia Conf. on Knowl. Discovery and Data Mining (PAKDD 2007), pages 205-216, Nanjing, China, 22-25 May 2007. (Acceptance rate: 17.7%, Class A Conference, non-self-citations: 14).

[C4] Mohammad M. Masud, Latifur Khan, and Bhavani M. Thuraisingham. A hybrid model to detect malicious executables. In ICC '07: Proceedings of the 2007 IEEE International Conference on Communications (ICC 2007), pages 1443-1448, Glasgow, Scottland, 24-28 June 2007. (Class B Conference, non-self-citations: 17)

[C3] Mohammad M. Masud, Latifur Khan, and Ehab Al-Shaer. Email worm detection using naive bayes and support vector machine. In ISI '06: Proceedings of the 2006 IEEE Intelligence and Security Informatics Conference (ISI 2006), pages 733-734, San Diego, California, 23-24 May 2006. (Class C Conference, non-self-citations: 1)

[C2] Mohammad M Masud. Combined Evolution Model: Integrating Machine Learning, Simulated Annealing and Evolutionary Computing. In RASC'04: Proceedings of the 5th International Conf. on Recent Advances in Soft Computing (RASC 2004), Nottingham, U.K, pp. 428-435, 16-18 December, 2004.

[C1] Mohammad M. Masud, Md. Monirul Islam & K. Murase. Empirical Study on Learnable Evolution Model for PMedian and Capacitated P-Median Problems. In HART '04: Proceedings of the 4th International Symposium on Human and Artificial Intelligence Systems (HART 2004): From Control to Autonomy, Fukui, Japan, pp. 265-270, 5-6 December, 2004.

jNote: conference ratings are obtained from following sources
1) CORE
(http://portal.core.edu.au/conf-ranks/)

2) Google scholar rankings: (https://scholar.google.ae/citations?view_op=top_venues&hl=en&vq=eng)
(following the link and choosing the sub-discipline “Data mining and analysis”, the top 20 journals and conferences can be seen )
 

Publication Links

Google Scholar: (https://scholar.google.com/citations?user=HBK9pbsAAAAJ&hl=en)
DBLP:
(http://dblp.uni-trier.de/pers/hd/m/Masud:Mohammad_M=)
Scopus: (http://www.scopus.com/authid/detail.uri?authorId=14037743900)

 

 

Public Presentations

Conference presentations (oral)

M. M. Masud, Online Prediction of Chess Match Result. The 19th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2015), Ho-Chi-Minh City, Vietnam, 19-22 May 2015.

M. M. Masud, Data Stream Mining for Malware Detection and Network Security, Workshop on Emerging Data Center and Cloud Computing Technologies, Al Ain, UAE, Dec 2013.

M. M. Masud, Detecting Recurring and Novel Classes in Concept-Drifting Data Streams.  IEEE International Conference on Data Mining (IEEE ICDM 2012), Brussels, Belgium, Dec 2012.

M. M. Masud, Detecting Recurring and Novel Classes in Concept-Drifting Data Streams. IEEE International Conference on Data Mining (ICDM 2011), Vancouver, Canada, Dec 2011.

M. M. Masud, Peer to Peer Botnet Detection for Cyber-Security: A Data Mining Approach. Cyber Security and Information Intelligence Research Workshop (CSIIRW), Oak Ridge National Laboratory, May 2008.

Conference presentations (poster)

M. M. Masud, MineClass:  A Synergy of Data Stream Classification and Novel  Class Detection. Doctoral Student Forum, SIAM International Conference on Data Mining (SDM), Sparks, NV, May 2009.

M. M. Masud, Novel Class Detection in Concept-Drifting Data Streams in a Shared Environment. Multidisciplinary University Research Initiative (MURI) meeting, University of Maryland Baltimore County, Sep 2009.

M. M. Masud, A Knowledge-based Approach to detect new Malicious Executables. Second Secure Knowledge Management Workshop (SKM06), Brooklyn, NY, USA, Sep 2006.

Invited Talks

M. M. Masud, Big Data Analytics and its Potentials for Oil and Gas Industry. The 2nd Data and Information Management in Oil and Gas Conference, Abu Dhabi, UAE, Sep 2015

M. M Masud, Active Defense with Data Mining. Information Sciences Institute (ISI), Los Angeles, CA, USA, Jan 2011.

M. M. Masud, Data Stream Mining for Cyber Security. Society for Design Process Science (SDPS) Conference, Dallas, TX, Jun 2010.

M. M. Masud, Malware Detection using Data Mining. Secure World Expo, Dallas, TX, USA, Nov 2009

Keynote Speeches

M M Masud, Challenges, Tools, and Applications of Data Stream Mining. 14th International Conference on Computer and Information Technology (ICCIT 2011), Dhaka, Bangladesh, Dec 2011.

M. M. Masud, Big Data Management and Analytics. Workshop on Advances in Data Management (WADM), Dhaka, Bangladesh, June 2013

Tutorials

L. Khan, W. Fan, J. Han, J. Gao and M. M. Masud, Data Stream Mining: Challenges and Techniques. Pacific-Asia Conf. on Knowl. Discovery and Data Mining (PAKDD '11), Shenzhen, China, May 2011.

M. M. Masud, Data  Mining  for Security  Applications. IEEE Conference on Intelligence and Security Informatics (ISI), Dallas, TX, Jun, 2009.

 

Text Box: SERVICES AND PROFESSIONAL ACTIVITIES

Professional Membership

Member of IEEE (2010-Present)

Member of ACM (2010-Present)

Editorial Membership

Lead Guest Editor, Special issue on “Mining Big Data in Social Networks and Healthcare”.

Journal of Electrical and Computer Engineering (http://www.hindawi.com/journals/jece/si/465468/cfp/)

Conference Organization

Member, Organizing committee, Student Poster Track, International Conference on Innovations in IT (IIT) – 2014.  (http://www.it-innovations.ae/iit2014/Ocommittees.html)

 

Technical Program Committee Member

IEEE Conf. on Connected Health: Applications, Systems and Engg. Tech. (CHASE) - 2016

Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) - 2016

International Conference on Innovations in IT (IIT) - 2015

IEEE Intelligence and Security Informatics Conference (ISI) – 2013

ACM International Conference on Information and Knowledge Management – 2013 (CIKM ’13)

ACM International Conference on Information and Knowledge Management – 2012 (CIKM ’12)

International Symposium on Pervasive Systems, Algorithms and Network (ISPAN)- 2012

World Wide Web (WWW)  Poster Track - 2011

ACM SIGKDD Workshop on Novel Data Stream Pattern Mining Techniques (StreamKDD ‘10) -2010. (https://lyle.smu.edu/IDA/StreamKDD2010/index.html#organizers)

 

Manuscript Reviewing (Invited / external)

Conferences

International Conference on Innovations in IT – 2015 (IIT ‘15)

International Conference on Innovations in IT – 2014 (IIT ‘14)

UAE Forum on Telecommunication Research – 2014 (ICTRF ‘14)

ACM International Conf. on Information and Knowledge Management – 2013 (CIKM ’13)

International Conference on Innovations in IT – 2013 (IIT ‘13)

IEEE International Conference on Info. And Comm. Tech.– 2013 (EICT ’13)

ACM International Conf. on Information and Knowledge Management – 2012 (CIKM ’12)

International Symposium on Pervasive Systems – 2012 (ISPAN ‘12)

ACM International Conf. on Knowledge Discovery and Data Mining – 2011 (SIGKDD ‘11)

Euro. Conf. on ML and Pr. and Prac. in Knowl. Disc.  in Data.  - 2011 (ECML/PKDD ’11)

ACM International Conf. on Knowledge Discovery and Data Mining – 2010 (SIGKDD ‘10)

International Conference on Data Mining – 2010 (ICDM ‘10)

Pacific Asia Conference on Knowledge Discovery and Data Mining – 2010 (PAKDD ’10)

ACM International Conf. on Advances in Geographic Information Systems – 2010 (GIS ‘10)

IFIP WG 11.9 International Conference on Digital Forensics – 2009

ACM International Conf. on Knowledge Discovery and Data Mining – 2009 (SIGKDD ‘09)

Siam International Conference on Data Mining -  2009 (SDM ‘09)

International Conference on Data Mining – 2009 (ICDM ‘09)

Siam International Conference on Data Mining -  2008 (SDM ‘08)

International Conference on Communications – 2007 (ICC ‘07)

International Conference on Dependable Systems and Networks – 2007 (DSN ‘07)

 

Journals

IEEE Trans on Knowl. and Data Engineering (TKDE) - 2015, 2014, 2013, 2012, 2011

IEEE Transactions on Database Systems (ToDS) – 2015

Journal of Innovation in Digital Ecosystems (JIDE), Elsevier – 2015

IEEE Trans. on Neural Net. and Learning Systems (TNNLS) – 2014, 2013, 2012, 2011

IEEE Transactions on Signal Processing (TSP) – 2014, 2013

Journal of Knowledge and Information Systems (KAIS) – 2014, 2013, 2012

Data Mining and Knowledge Discovery Journal (DAMI) – 2014, 2013

IEEE Internet of Things (IOT) Journal – 2014

Journal of Intelligent Information Systems (JIIS) – 2014

Journal of Machine Vision and Applications (MVAP) – 2014

Journal of Security and Communication Networks (SCN) – 2014, 2012

ACM Transactions on Knowledge Discovery from Data (TKDD) – 2013

ACM Transactions on Management Information Systems (TMIS) – 2013

Journal of Electronic Commerce Research (ECR) – 2013

Journal of Artificial Intelligence (ARTINT), Elsevier – 2011

Information Systems Frontiers (ISF) Journal – 2011

Journal of Zhejiang University Science (JZUS) – 2011, 2010

IEEE Transactions on Dependable and Secure Computing (TDSC) – 2009

 

"Data Mining Tools for Malware Detection". Mehedy Masud, Latifur Khan, Bhavani Thuraisingham. CRC Press.

Summary: Although the use of data mining for security and malware detection is quickly on the rise, most books on the subject provide high-level theoretical discussions to the near exclusion of the practical aspects. Breaking the mold,Data Mining Tools for Malware Detection provides a step-by-step breakdown of how to develop data mining tools for malware detection. Integrating theory with practical techniques and experimental results, it focuses on malware detection applications for email worms, malicious code, remote exploits, and botnets.

 

Amazon link:

http://www.amazon.com/Data-Mining-Tools-Malware-Detection/dp/1439854548

Dr. Mohammad Mehedy Masud
Associate Professor
Enterprise Systems
College of Information Technology