Paper Type	::	Research Paper
Title	::	A Distribute Publisher-Driven Secure Data Sharing Scheme for Information-Centric IoT
Country	::	India
Authors	::	Ms. Geeta Nikude || Prof.Pravin Kulurkar
Page No.	::	01-05

In Information-Centric Internet of Things (ICIoT), Internet of Things (IoT) data can be cached throughout a network for close data copy retrievals. Such a distributed data caching environment, however, poses a challenge to flexible authorization in the network. To address this challenge, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been identified as a promising approach. However, in the existing CP-ABE scheme, publishers need to retrieve attributes from a centralized server for encrypting data, which leads to high communication overhead. To solve this problem, we incorporate CP-ABE and propose a novel Distributed Publisher-Driven secure data sharing for ICIoT (DPD- ICIoT) to enable only authorized users to retrieve IoT data from distributed cache............

Keyword :IoT, ICN, Encryption, NICT, Cryptography, DPD-ICIoT, NFD, Information retrieval, Internet of things, Authorization, Cache storage

[1]. United Nation. (2014). World Urbanization Prospect. [Online].Available
[2]. http://dl.acm.org/citation.cfm?id=308574.308676
[3]. M. S. Hossain, ``Cloud-supported cyber-physical localization framework for patients monitoring,'' IEEE Syst. J., vol. 11, no. 1, pp. 118 127, Mar. 2017.
[4]. M. S. Hossain, G. Muhammad, W. Abdul, B. Song, and B.
[5]. B. Gupta, ``Cloud-assisted secure video transmission and sharing framework for smart cities,'' Future Generat. Comput. Syst. J., Elsevier 2017, to be pub- lished. [Online].

Paper Type	::	Research Paper
Title	::	A Review on Privacy Preservation and Public Auditing in Cloud Storage
Country	::	India
Authors	::	Divya Hadke || Rajesh Babu || Roshani Talmale
Page No.	::	06-11

Securing outsourced taking in cloud storage from degradation, adding adjustment to non-basic inability to cloud stockpiling close by data uprightness checking reparation winds up observably fundamental. Earlier impact codes to have quality by virtue of their lower information measure offering adjustment to nonbasic disappointment. Starting late remote checking courses for make coded adapting solely offer non-public auditing requiring data proprietor tenaciously keep on-line and handle auditing and repairing, that is unreasonable. Here maker propose a public auditing for the make code based for the most part cloud stockpiling. It's to decide the recuperation disadvantage of unsuccessful authenticators inside the nonattendance of data property holders, maker familiarize a proxy that is advantaged with recoup the authenticators into the standard public auditing system show.........

Keywords: Cloud storage, regenerating codes, public audit, privacy preserving, authenticator regeneration, proxy, privileged, provable secure

[1]. A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, and I. Stoica, "Above the clouds: A Berkeley view of cloud computing," Dept. Electrical Eng. and Comput. Sciences,University of California, Berkeley, Rep. UCB/EECS, vol. 28, p. 13, 2009.
[2]. G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, and D. Song, "Provable data possession at untrusted stores," in Proceedings of the 14th ACM Conference on Computer and Communications Security, ser. CCS '07. New York, NY, USA: ACM, 2007, pp. 598– 609.
[3]. A. Juels and B. S. Kaliski Jr, "Pors: Proofs of retrievability for large files," in Proceedings of the 14th ACM conference on Computer and communications security. ACM, 2007, pp. 584–597.
[4]. R. Curtmola, O. Khan, R. Burns, and G. Ateniese, "Mr-pdp: Multiplereplica provable data possession," in Distributed Computing Systems, 2008. ICDCS'08. The 28th International Conference on. IEEE, 2008, pp. 411–420.
[5]. K. D. Bowers, A. Juels, and A. Oprea, "Hail: a high-availability and integrity layer for cloud storage," in Proceedings of the 16th ACM conference on Computer and communications security. ACM, 2009, pp. 187–198.

Paper Type	::	Research Paper
Title	::	A Survey on Privacy-Preserving Mining of Association Rules from Transaction Databases
Country	::	India
Authors	::	Rani Mankar || Jayant Adhikari || Jiwan Dehankar
Page No.	::	12-18

Database Outsourcing is a promising data organization system in which Data-proprietor stores the private information at the untouchable organization supplier's site. The organization provider directs and deals with the database and benefits the readymade organizations to the data proprietor and their clients to make, update, eradicate and get to the database. Regardless of the way that database security is required in light of the way that more organization providers are not reliability. The genuine necessities for getting security in outsourced databases are mystery, assurance, trustworthiness, and freshness if there ought to be an event of component redesigns, get to control in multi-customer condition, availability and request approval and confirmation. To achieve these all necessities distinctive.......

Keywords: Access Control, Confidentiality, Freshness, Integrity, Outsourced Databases, Query Authentication, Security mechanisms

[1]. http://www.computerweekly.com/news/2240178104/Badoutsourcingdecisions-cause-63ofdatabreaches.
[2]. http://www.networkworld.com/news/2012/020712-data- breach-255782.html
[3]. www.oracle.com/technetwork/topics/.../oes-refarch-dbaas508111.pdf
[4]. http://dbaas.wordpress.com/2008/05/14/whatexactly-is-database-as-a-service/
[5]. https://451research.com/reportshort?entityId=78105&referrer=marketing

Paper Type	::	Research Paper
Title	::	A Survey on Sequential and Non-Overlapping Patterns for Classification
Country	::	India
Authors	::	Kanchan Ganvir || Rajesh Babu || Roshani Talmale
Page No.	::	19-22

Classification is the way toward finding a model or capacity that portrays and recognizes information classes or ideas, to be ready to utilize the model to foresee the class of items whose class mark is obscure. The objective of classification is to precisely foresee the object class for each case in the information. In sequence database having sequences, in which each sequence is a rundown of the exchanges requested by the exchange time. There is exchange time which is related to every exchange in the sequence database. The sequence classification can be characterized as appointing class marks to new sequences dependent on the learning picked up in the preparation organize.

Keywords:Sequence Classification, Interesting patterns,Classification Rules, Association Rules

[1]. Cheng Zhou, Boris Cule, and Bart Goethals, "Pattern Based Sequence Classification", IEEE Transaction on knowledge and data engineering, Vol 28, No. 5, May 2016.
[2]. T. C. Silva and L. Zhao, "Pattern-Based Classification via a High Level Approach Using Tourist Walks in Networks," 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, Ipojuca, 2013, pp. 284-289.
[3]. K. W. Chang, B. Deka, W. M. W. Hwu and D. Roth, "Efficient Pattern-Based Time Series Classification on GPU," 2012 IEEE 12th International Conference on Data Mining, Brussels, 2012, pp. 131-140.
[4]. E. Egho, D. Gay, M. Boull, N. Voisine and F. Clrot, "A Parameter-Free Approach for Mining Robust Sequential Classification Rules," 2015 IEEE International Conference on Data Mining, Atlantic City, NJ, 2015, pp. 745-750.
[5]. Wentao Mao, J.Wang and L.Wang, "Online sequential classification of imbalanced data by combining extreme learning machine and improved SMOTE algorithm," 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, 2015, pp. 1-8.

Paper Type	::	Research Paper
Title	::	A Review on Stress Detection Based on Social Media Interactions
Country	::	India
Authors	::	Shweta Meshram || Rajesh Babu || Roshani Talmale
Page No.	::	23-27

Psychological wellbeing conditions impact a significant dimension of the total populace each year. Mental stress is turning into a risk to individuals' well-being nowadays. With the fast pace of life, an everincreasing number of individuals are getting affected by increasing stress level. Distinguishing the user stress at initial stage is an important yet difficult task. With the notoriety of electronic social platform, people are accustomed to sharing their day to day events and exercises and connecting with companions by means of online networking media stages, making it conceivable to utilize online social network information for stress detection. In this paper, we will discuss a various study conducted by different researchers.

Keywords: Human Stress Detection, Social Media, Healthcare, Social Interaction, Data Mining.

[1]. Yuan Zhang, Jie Tang, Jimeng Sun, Yiran Chen, and Jinghai Rao. Moodcast: Emotion prediction via dynamic continuous factor graph model 2016 IEEE International Conference on Data Mining
[2]. Bridging the vocabulary gap between health seekers and healthcare knowledgeLiqiang Nie, Yi-Liang Zhao, Mohammad Akbari, Jialie Shen, and Tat-Seng Chua. 2013
[3]. Factor Graphs and the Sum-Product Algorithm Frank R. Kschischang, Senior Member, IEEE, Brendan J. Frey, IEEE TRANSACTIONS 2015
[4]. Xiao jun Chang, Yi Yang1, Alexander G. Hauptmann, Eric P. Xing and Yao-Liang Yu Semantic Concept Discovery for Large- Scale Zero-Shot Event Detection Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)
[5]. Jennifer Golbeck, Cristina Robles, Michon Edmondson, and Karen Turner. Predicting personality from twitter. In Passat/socialcom 2011, Privacy, Security, Risk and Trust, pages 149–156, 2011

Paper Type	::	Research Paper
Title	::	An Analytical Review of Web Page Recommendation System Based on Machine Learning
Country	::	India
Authors	::	Nikita Raut || Jayant Adhikari
Page No.	::	28-31

Recommendation System (RS) are generally used in e-commerce industry to solve the complication of information overloading. Large amount of information is generating now, days due to which user face the difficulty in finding the relevant information of product and services matching to their taste and preferences. Data mining (DM) is the process of mining and extracting useful knowledge from large datasets. The tasks of DM are to do description and prediction of data to retrieve the information. RS is a subfield of information retrieval (IR) and IR is subfield of DM. Recommendation engines basically are data filtering and IR tool that make use of algorithms and data to recommend the most relevant item to particular user........

Keywords-Web Page Recommendation, Web Mining, Sequential Patterns, Knowledge Representation, Domain Ontology

[1]. S. Agarwal, ―data mining: data mining concepts and techniques,‖ proc. - 2013 int. Conf. Mach. Intell. Res. Adv. Icmira 2013, pp. 203–207, 2014.
[2]. C. H. Moore, ―data mining based recommendation system using social websites,‖ ieee/wic/acm int. Conf. Web intell. Intell. Agent technol. Data, vol. 19, no. 1, pp. 12–13, 2015.
[3]. Y. Li, ―data mining: concepts, background and methods of integrating uncertainty in data mining,‖ pp. 2–7.
[4]. J. P. J.han, m. Kamber, data mining concepts and techniques, 3rd editio. Morgan kaufmann is an imprint of elsevier, 2011.
[5]. M. Dhanda and v. Verma, ―recommender system for academic literature with incremental dataset,‖ procedia comput. Sci., vol. 89, pp. 483–491, 2016.

Paper Type	::	Research Paper
Title	::	A Survey on Data Aggregation Mechanism in Wireless Sensor Networks
Country	::	India
Authors	::	Sayali Tembhurne || Rajesh babu || Roshani Talmale
Page No.	::	32-38

Data aggregation is exceptionally critical methods in wireless sensor network. Since with the assistance of data aggregation we lessen the vitality utilization by wiping out excess. At the point when wireless sensor network sent in remote regions or threatening condition. In the wireless sensor network have the most difficult assignment is an existence time so with help of data aggregation we can improve the lifetime of the network .In this paper we talk about the data aggregation approaches based on the directing conventions, the calculation in the wireless sensor network. And furthermore talk about the favourable circumstances and detriments or different execution measures of the data aggregation in the network

Keywords-Wireless sensor network, data aggregation, architecture, Network Lifetime, Routing, Tree, Cluster, Base Station

[1]. I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A Survey On Sensor Networks", IEEE Communications Magazine, Volume 40, Number 8, pp.102-114, 2002.
[2]. T. Arampatzis , J. Lygeros, and S. Manesis, "A Survey Of Applications Of Wireless Sensors And Wireless Sensor Networks", In Mediterranean Conference On Control And Automation MED05, Nicosia, Cyprus, 2005.
[3]. L. Gatani, G. Lo Re, and M. Ortolani, "Robust and Efficient Data Gathering for Wireless Sensor Networks", in Proceeding of the 39th Hawaii International Conference on System Sciences – 2006
[4]. K. Dasgupta, K. Kalpakis, and P. Namjoshi, "An Efficient Clustering-based Heuristic for Data Gathering and Aggregation in Sensor
Networks", IEEE 2003

Paper Type	::	Research Paper
Title	::	A Survey on Data Redundancy Minimization Based on Feature Extraction
Country	::	India
Authors	::	Ashwini Hanwate || Jayant Adhikari
Page No.	::	39-43

In high dimensional data mining feature selection is most important step. Feature selection is use to select most relevant,important and informative features from the high-dimensional dataset. It plays an important role in many scientific and practical applications, because itincreases the speed of learning process. So, it is very important to develop an efficient framework, which can improve the feature selection process. In literature, various supervised and unsupervised feature selection methods are developed. In order to utilize both local and global structures, existing system propose unsupervised local and global discriminative (LGD) feature selection criterion. Generally, supervised feature selection methods with supervision information are better than unsupervised ones without supervision information.........

Keywords-Data mining, feature selection, redundancy minimization, supervised and unsupervised feature learning

[1]. De Wang, Feiping Nie, and Heng Huang, "Feature Selection viaGlobal Redundancy Minimization", IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 27, NO. 10, OCTOBER 2015
[2]. D. Cai, C. Zhang, and X. He, "Unsupervised feature selection for multi-cluster data,"in Proc. 16th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2010, pp.333-342.
[3]. P. N. Belhumeur, J. P. Hespanha, and D. Kriegman, "Eigenfaces vs. fisherfaces:Recognition using class specific linear projection," IEEE Trans. Pattern Anal. Mach.Intell., vol. 19, no. 7, pp. 711-720, Jul. 1997.
[4]. X. Chang, F. Nie, Y. Yang, and H. Huang, "A convex formulation for semisupervisedmulti-label feature selection," in Proc. AAAI Conf. Artif. Intell., 2014,pp. 1171-1177.

Paper Type	::	Research Paper
Title	::	A Survey on Online Transaction Processing Security
Country	::	India
Authors	::	Aniket A. Ganvir
Page No.	::	44-50

Nowadays, online transactions are becoming popular in India since Demonetization occurred. Making online transactions having benefits for users like cashback, reward points, redeem coupons, etc. Besides, online transactions are easy and flexible, at other hand, this technology needs strong security method. This paper targets the security concept among the payments which are carried over the Internet. Online transaction processing security is nothing but the protection of online payments from illegal access, modification, usage or damage. Online payments are evolving as extremely helpful to end clients and business parties, however it additionally creates new risks and vulnerabilities, for example, security threats. For making effective and efficient transaction operations over the internet, Information security is essential constraint. In this paper a review of Online Transactions and Online Transaction Security, purpose of online transaction security, various security issues in online payments, and different security framework has being talked about.

Keywords: Online transaction; OLTP; Keystroke Logging; Secure Electronic Transaction; WebPin

[1]. Yi Yi Thaw ,Ahmad Kamil Mahmoodl, P.Dhanapal Durai Dominic A Study on the Factors That Inuence the Consumers Trust on Ecommerce AdoptionVol. 4, No. 1 2, 2009
[2]. Asaf Shabtai,Yuval Fledel, Uri Kanonov, Yuval Elovici, Shlomi Dolev (2010), "Google Android: A Comprehensive Security Assessment." IEEE security and Privacy.
[3]. Dilip Kumar, Yeonseung Ryu, "A Brief Introduction of Biometrics and fingerprint Payment Technology", Published by the IEEE Computer Society,2008.
[4]. Dr Suresh Sankaranarayanan, "Biometric Security Mechanism in mobile Payment ", Published by the IEEE Computer Society, 2010.
[5]. ]B Y Hiew, Andrew BJ Teoh and David CL Ngo, (2006) "Preprocessing of Fingerprint Images Captured with a Digital Camera", IEEE, ICARVC.

Paper Type	::	Research Paper
Title	::	A Survey paper on face authentication system
Country	::	India
Authors	::	Arshad Khan || UkeshKewat || SameerRamteke || Prof.Manisha
Page No.	::	51-53

Biometric system can be either an 'identification' system or a 'verification' (authentication) system. Biometrics can be used to determine a person's identity even without his knowledge or consent.In this survey paper, we have made an effort to study and analyze the approaches of one of the existing biometric systems i.e. face recognition system. A face authentication system can recognize static images and can be modified to work with dynamic images. In 2008, H. Bay invented SURF descriptor which is invariant to a scale and in-plane rotation features. It consists of two phases such as interest point detector and interest point descriptor. In the first phase, he located the interest point in the image and second phase, used the Hessian matrix to find the approximate detection............

Keywords: SURF Descriptor, Hessian matrix SOM, CAMSHIFT, Adaptive Kalman filter

[1]. Kavita,Ms. ManjeetKaur ,"A Survey paper for Face Recognition Technologies",International Journal of Scientific and Research Publications, Volume 6, Issue 7, July 2016,ISSN 2250-3153
[2]. Priyanka P. Raut , Namrata R. Borkar," Techniques and Implementation of Face Spoof Recognition: Perspectives and Prospects ", International Journal of Engineering Science and Computing, January 2018 , Volume 8 Issue No.1 10.1109/ICETT.2016.7873742.
[3]. Chung-Hua Chu* and Yu-Kai Feng," Study of Eye Blinking to Improve Face Recognition for Screen Unlock on Mobile Devices ", J ElectrEng Technol.2017, the Ministry of Science and Technology, R.O.C., under Contracts MOST 106-2221-E-025 -001.
[4]. Xiwei Dong, Fei Wu and Xiao-Yuan Jing," Generic Training Set based Multimanifold Discriminant Learning for Single Sample Face Recognition", KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 12, NO. 1, January 2018.
[5]. Priya Gupta, NidhiSaxena, Meetika Sharma, JagritiTripathi,"Deep Neural Network for Human Face Recognition", International Journal of Engineering and Manufacturing(IJEM), Vol.8, No.1, pp.63-71, 2018.DOI: 10.5815/ijem.2018.01.06

Paper Type	::	Research Paper
Title	::	Affordable Mobile Application to Monitor Residential Society's Vehicle Activity
Country	::	India
Authors	::	Bhavana R. Chandankar || Rani S. Choudhari || Shivani M. Nagrale || Pallavi D. Morey || Pranali G. Nikhade || Prof. Vishakha Nagrale
Page No.	::	54-57

The process involves the system has still images as the input, and extracts a string corresponding to the plate number, which is used to obtain the output user data from a suitable database. The system extracts data from a license plate and compare it with the user database if it matches the it will store the details of that vehicle and if it is not matched means that vehicle does not belong to that society then it will generated a message and send it to the administrator. License plate extraction is based on plate features, such as texture, and all characters segmented from the plate are passed individually to a character recognition stage for reading..

Keywords: License plate Extraction, Character Segmentation, Character Recognition

[1]. Nitin Sharma and Nidhi:TEXT EXTRACTION AND RECOGNITION FROM THE NORMAL IMAGES USING MSER FEATURE EXTRACTION AND TEXT SEGMENTATION METHODS. Indian Journal of Science and Technology, Vol 10(17), DOI: 10.17485/ijst/2017/v10i17/114415, May 2017
[2]. SaquibNadeemHashmi, Kaushtubh Kumar, SiddhantKhandelwal, DravitLochan, Sangeeta Mittal :REAL TIME LICENSE PLATE RECOGNITION FROM VIDEO STREAMS USING DEEP LEARNING, International Journal of Information Retrieval Research Volume 9 ,Issue, January-March 2019
[3]. Sneha G. Patel:VEHICLE LICENSE PLATE RECOGNITION USINGMORPHOLOGY AND NEURAL NETWORK,International Journal on Cybernetics & Informatics (IJCI) Vol.2, No.1, February 2013 DOI : 10.5121/ijci.2013.2101
[4]. RaginiBhat, BijenderMehandia: "RECOGNITION OF VEHICLE NUMBERPLATE USING MATLAB"International journal of innovative research in electrical, electronics, instrumentation and control engineering Vol. 2,Issue 8, August 2014

Paper Type	::	Research Paper
Title	::	An Enhanced Approach for Classifying Twitter Emotions Using Machine Learning
Country	::	India
Authors	::	Bireshwar Ganguly || Devashri Raich
Page No.	::	58-63

In this modern age, social media apps like Twitter, Facebook, Tumbler, and etc carries a significant role in everyone's life. Twitter is a worldwide blogging platform that has a lot of information that can be utilized for different assessment & analysis of views & opinions of people all across the globe. The amount of data accumulated on Twitter is very huge. This data is unstructured & raw as it is written by common man. Twitter Sentimental Analysis is the process of accessing tweets for a particular topic and predicts the sentiment of these tweets as positive, negative or neutral with the help of different machine learning algorithm. Analysis such as prediction, forecasts, evaluations, elections, marketing etc using emotion analysis is one such procedure of extracting vital information from this data and views..............

Keywords: Sentiment Analysis, Twitter, Machine Learning

[1]. Pak, Alexander, and Patrick Paroubek. "Twitter as a Corpus for Sentiment Analysis and Opinion Mining." LREc. Vol. 10. 2010.
[2]. Alec Go, Richa Bhayani, and Lei Huang. Twitter sentiment classification using distant supervision. Processing, pages 1-6, 2009.
[3]. Niek Sanders. Twitter sentiment corpus. http://www.sananalytics.com/lab/twitter-sentiment/. Sanders Analytics.
[4]. Alexander Pak and Patrick Paroubek. Twitter as a corpus for sentiment analysis and opinion mining. volume 2010, pages 1320- 1326, 2010.
[5]. Efthymios Kouloumpis, Theresa Wilson, and Johanna Moore. Twitter sentiment analysis: The good the bad and the omg! ICWSM, 11:pages 538-541, 2011.

Paper Type	::	Research Paper
Title	::	Survey Paper on Detection of Diseases using Medical imagery and Machine learning
Country	::	India
Authors	::	Mr.Ramiz Sheikh || Mr. Mohanish Mishra || Mr. Mohd Siddique Sheikh || Mr. Vishwakarma Hekad || Prof. Manisha
Page No.	::	64-65

Diseases with medical imaginary such as Tuberculosis'And Pneumonia Etc ,which is detected by using chest x-rays etc are rapidly spread disease in the world. When left undiagnosed and thus untreated, mortality rates of patients with such diseases are high. Standard diagnostics still rely on methods developed in the last century. They are slow and often unreliable. So, Computer aided diagnosis (CAD) has been popular and many researchers are interested in this research areas and different approaches have been proposed for the disease detection and lung decease classification. In our paper we have surveyed different method for detection of different diseases in X-Ray image by using Machine learning which includes Pre Processing of Image, classification and Feature extraction from that image.

Keywords: Computer aided diseases(CAD), X-ray

[1]. Anderson, L. et al. WHO Global tuberculosis report 2015. WHO Libr. Cat. Data 1, 1689–1699 (2015).
[2]. Van't Hoog, A. H. et al. High sensitivity of chest radiograph reading by clinical officers. Int. J. Tuberc. Lung Dis. 15, 1308–1314 (2011).
[3]. Melendez, J. et al. An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information. Sci. Rep. 6, 1–8 (2016).
[4]. Jaeger, S. et al. Automatic tuberculosis screening using chest radiographs. IEEE Trans. Med. Imaging 33, 233–245 (2014).
[5]. Hwang, S., Kim, H., Jeong, J. & Kim, H. A Novel Approach for Tuberculosis Screening Based on Deep Convolutional Neural Networks. Proc. SPIE 9785, 1–8 (2016).

Paper Type	::	Research Paper
Title	::	Survey Architecture for Network Intrusion Detection and Prevention
Country	::	India
Authors	::	monali Bodkhe || Gurudev Sawarkar
Page No.	::	66-70

This paper presents an investigation, involving experiments, which shows that current network intrusion, detection, and prevention systems (NIDPSs) have several shortcomings in detecting or preventing rising unwanted traffic and have several threats in high-speed environments. It shows that the NIDPS performance can be weak in the face of high-speed and high-load malicious traffic in terms of packet drops, outstanding packets without analysis, and failing to detect/prevent unwanted traffic. Since new threats are potentially more lethal, a number of pro-active designs have been proposed, which can detect new security events such as propagation of a new and unknown virus or worm.......

Keywords: NIDS, Anomaly Detection, Network Security, Security Signature, Pattern Matching

[1]. R. Lippmann, D. Fried, I. Graf, J. Haines, K. Kendall, D. McClung, D. Weber, S. Webster, D. Wyschogrod, R. Cunninghan, and M. Zissman, "Evaluating Intrusion Detection Systems: The 1998 DARPA Off-line Intrusion Detection Evaluation," DARPA Information Survivability Conference and Exposition, Jan 2000.
[2]. R. Lippmann, J. Haines, D. Fried, J. Korba, and K. Das., "Analysis and Results of the 1999 DARPA Off-line Intrusion Detection Evaluation," Recent Advances in Intrusion Detection (RAID 2000), Oct 2000.
[3]. J. Allen, A. Christie, W. Fithen, J. McHugh, J. Pickel, and E. Stoner, "State of the Practice of Intrusion Detection Technologies," Technical Report CMU/SEI-99-TR-028, CMU/SEI, 2000.
[4]. T. H. Ptacek and T. N. Newsham, "Insertion, Evasion, and Denial of Service: Eluding Network Intrusion Detection," Technical report, Secure Networks Inc., Jan 1998.
[5]. V. Paxson, "Bro: A System for Detecting Network Intruders in Real-time," Computer Networks, 31(23-24), Dec 1999

Paper Type	::	Research Paper
Title	::	Follow But No Track: Privacy Preserved Profile Publishing in Cyber-Physical Social Systems
Country	::	India
Authors	::	Miss Neha Suryawanshi || Asst.prof.vaishali agre
Page No.	::	71-76

Due to the close correlation with individual's physical features and status, the adoption of Cyber-Physical Social Systems (CPSSs) has been inevitably hindered by users' privacy concerns. Such concerns keep growing as our bile devices have more embedded sensors, while the existing countermeasures only provide incapable and limited privacy preservation for sensitive physical information. Therefore, we propose a novel privacy preservation framework for CPSSs. We formulate both the privacy concerns and user expectations in CPSSs based on real-world knowledge. We also design a corresponding data publishing mechanism for users. It regulates the publishing behaviors to hide sensitive physical profiles. Meanwhile, the published data retain comprehensive social profiles for users. Our analysis demonstrates that the mechanism achieves a local maximized performance on the aspect published data size. The experiment results towards real datasets reveal that the performance is comparable to the global optimal one.

[1]. A. Sheth, P. Anantharam, and C. Henson, "Physicalcyber-social computing: An early 21st century approach," IEEE Intelligent Systems, vol. 28, no. 1, pp. 78–82, 2013.
[2]. F. Li, C. Tian, T. Li, and Y. Wang, "Energy efficient social routing framework for mobile social sensing networks," Tsinghua Science and Technology, vol. 21, no. 4, pp. 363–373, 2016.
[3]. T. Qiu, D. Luo, F. Xia, N. Deonauth, W. Si, and A. Tolba, "A greedy model with small world for improving the robustness of heterogeneous internet of things," Computer Networks, vol. 101, pp. 127–143, 2016.
[4]. L. Hu, A. Sun, and Y. Liu, "Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction," in Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pp. 345–354,
ACM, 2014.
[5]. T. Qiu, A. Zhao, R. Ma, V. Chang, F. Liu, and Z. Fu, "A task-efficient sink node based on embedded multicore soc for internet of things," Future Generation Computer Systems, 2016.