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Nowadays, delivering sensitive digital multimedia contents confidentially over vulnerable public networks is a matter of high importance and encryption is a technique which is widely used for secure communication. An improved and extended version of a lossless encryption technique used for digital images is quaternion based encryption. The quaternion based encryption scheme, significantly improves speed of images encryption in comparison with those originally embedded advanced encryption standard (AES) and triple data encryption standard (3-DES) algorithms. It utilizes extraordinarily properties of quaternion’s to perform rotations of information in 3D space for each of the cipher rounds. In this paper it has been surveyed about existing works on quaternion based encryption technique for both gray toned and color images as well as its application for secure transmission of medical images.

Quaternion rotation, lossless scheme, security, DICOM, image processing, key sensitivity.

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Garima Mathur, Anjana Pandey, "Survey Paper on Quaternion-Based Encryption," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.978-990, 2019.

Distinguishing and segmenting salient objects in like manner scenes, every now and again implied as salient object detection, has pulled in a huge amount of eagerness for PC vision. While various models have been proposed and a couple of utilizations have risen, yet a profound comprehensionof issues is insufficient. We hope to give a broad study of progressing in salient object identification and mastermind this area among other immovably related domains, for instance, regular picture segmentation, object recommendation age, and saliency for obsession forecast. Covering 228 distributions, we review i. Roots, key ideas, and assignments, ii.Center methods and principle displaying patterns, and iii.Datasets and assessment measurements in salient object identification. We likewise talk about open issues, for example, assessment measurements and dataset predisposition in model execution and propose future research bearings.

Video-saliency, Spatio-temporal constraints, Reliableregions, global saliencyoptimization

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T. Hemanth Kumar, P. Chandra Sekhar Reddy, "A Survey on Salient Object Detection," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.991-994, 2019.

Mobile ad-hoc networks (MANET) are a collection of mobile nodes that communicate with each other without any infrastructure. MANET is one of the temporary network, and it can establish anywhere anytime. Security is an necessary requirement in mobile ad-hoc networks to establish protected communication between mobile nodes. MANET are vulnerable to various types of attacks. One of main attacks is cosmic dust attack and black hole attack, it is a denial of service attack and it drops entire incoming packets between one source to destination. In this paper, it introduced new technique for avoiding both attacks with AODV routing protocol. This technique is tested and evaluated by using OmNetpp tool.

Mobile ad hoc network (MANET), AODV, cosmic dust attack, Black hole attack

[1]. Qussai M. Yaseena, Monther Aldwairi, “An Enhanced AODV Protocol for Avoiding Black Holes in MANET”, Science Direct, Procedia Computer Science , Volume :134,2018, pp. 371–376.
[2]. G. Stephanie Vianna,,T. Vishnu Priya, M. Sathya, “Trust Based Approach To Overcome Black Hole Attack in MANET”, International Journal of Pure and Applied Mathematics, ISSN: 1314-3395,Volume 118 No. 22, 2018, pp. 1763-1769,
[3] Sweta Tarale and Dr. Abha Choubey,” Black Hole Attack Implementation and Detection in Mobile Ad Hoc Networks”, International Journal on Recent and Innovation Trends in Computing and Communication, ISSN: 2321-8169, Volume: 6, Issue: 5, May 2018, pp. 117 - 120.
[4]. Vikram Narayandas, Sujanavan Tiruvayipati, Madusu Hanmandlu and Lakshmi Thimmareddy. “Anomaly Detection System in a Cluster Based MANET”, Computer Communication, Networking and Internet Security, Lecture Notes in Networks and Systems 5,DOI 10.1007/978-981-10-3226-4_2, 2017,pp. 11-21.
[5]. Rashmi and Ameeta Seehra, “Detection and Prevention of Black-Hole Attack in MANETS”, International Journal of Computer Science Trends and Technology (IJCST), ISSN: 2347-8578, Volume 2, Issue 4, Jul-Aug 2014, pp.204-209.
[6]. Minoti Puray and Priyanka Palod, “Black-Hole Attack in MANET: A Study”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), ISSN: 2278 – 1323, Volume 5 Issue 3, March 2016, pp. 597- 601.
[7]. VeerpalKaur and Simpel Rani, “A Hybrid and Secure Clustering Technique for Isolation of Black hole Attack in MANET”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), ISSN: 2278 – 1323, Volume 7, Issue 3, March 2018, pp. 230-237.
[8]. Niranjan Panda1 and Binod Kumar Pattanayak, “Defense Against C-Operative Black-Hole Attack and Gray-hole Attack in MANET”,International Journal of Engineering & Technology, ISSN: 2227-524X, Volume 7, Issue 3.4, 2018, pp. 84-89.
[9]. Asha Ambhaikar, H.R. Sharma, V. K. Mohabey, “Improved AODV Protocol For Solving Link Failure In Manet, International Journal of Scientific & Engineering Research, ISSN 2229-5518, Volume 3, Issue 10, October-2012,pp. 1-6.
[10]. Salman M. Al-Shehri and Pavel Loskot, “Enhancing Reliability of Tactical MANETs by Improving Routing Decisions”, Journal of Low Poer Electrinics and Applications, Volume 8, Issue 49, 2018, pp.1-15.
[11]. S.Gayathri Devi and Dr.A.Marimuthu, “Forwarding Node Reduction with Link Break Time Prediction”, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 24,2017, pp. 14441-14446.
[12]. Amanpreet Singh, Bhupinder Kaur, “To Propose a Novel Technique for Link Recovery in MANET”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 5 Issue 2, February 2016.
[13]. B.Karthikeyan and Dr.S.Hari Ganesh, “Analysis of Reactive AODV Routing Protocol for MANET”, IEEE Xplore, Digital Library,ISBN:978-1-4799-2876-7, Oct 2014, pp. 264-267, Scopus Index, Impact Factor :5.629.
[14]. B.Karthikeyan and Dr.S.Hari Ganesh, “Security and Time Complexity in AODV Routing Protocol”, International Journal of Applied Engineering Research (ISSN:0973-4562), Vol. 10, No.20,June 2015, pp.15542- 155546.
[15]. B. Karthikeyan and Dr.S.Hari Ganesh, “Encrypt - Security Improved Ad Hoc On Demand Distance Vector Routing Protocol (En-SIm AODV)”, ARPN Journal of Engineering and Applied Sciences (ISSN: 1819-6608), Vol. 11, No. 2, January 2016,pp. 1092-1096.
[16]. B. Karthikeyan,Dr.S.Hari Ganesh and Dr. JG.R. Sathiaseelan, “ Optimal Time Bound Ad-Hoc On-demand Distance Vector Routing Protocol (OpTiB-AODV)”, International Journal of Computer Applications (ISSN:0975 – 8887), Vol. 140, No.6, April 2016,pp 7-11.
[17]. B. Karthikeyan, Dr.S.Hari Ganesh and Dr. JG.R. Sathiaseelan , “High Level Security with Optimal Time Bound Ad-Hoc On-demand Distance Vector Routing Protocol (HiLeSec-OpTiB AODV)”,International Journal of Computer Science Engineering(E-ISSN: 2347-2693),Vol. 4, No. 4, April 2016, pp.156-164.

D. Shanmugasundaram, A. R. Md. Shanavas , "Avoidance Cosmic Dust implementing in Ad Hoc on-demand Distance Vector (CDA AODV) Routing Protocol," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.995-1005, 2019.

With the advent of Artificial Intelligence and Machine learning, healthcare is not limited to mere scans and tests, but also caters to doctors helping them diagnose the medical disorder at hand. One such field of healthcare is diagnosis of skin cancer which is also called as Basal cell carcinoma. This is often caused due to deficiency of a skin pigment called melanin, which may deplete due to harsh environmental conditions. This paper concentrates on a machine learning algorithm to detect skin cancer. The accuracy is found to be 90%.

Skin cancer, melanoma, melanin, benign, machine learning

[1] Detection skin cancer using SVM and snake model. Bumrungkun, P., Chamnongthai, K., & Patchoo, W. 2018 International Workshop on Advanced Image Technology
[2] Deep Convolution Pixel-wise Labeling for skin lesion Image Segmentation ,Ali Youssef ;Domenico D. Bloisi ;Mario Muscio ; Andrea Pennisi ; Daniele Nardi ; Antonio Facchiano IEEE International Symposium on Medical Measurements and Applications (MeMeA) Year: 2018
[3] A SVM based diagnosis of melanoma using only useful image features, Suleiman Mustafa; Akio Kimura , International Workshop on Advanced Image Technology (IWAIT) Year-2018
[4] Early Detection of Melanoma Skin Cancer Using Classifiers, VS. Sabeera, P. Vamsi Krishna PG Scholar, Dept. of ECE, R. K. College of Engineering, Vijayawada, Andhra Pradesh Assistant Professor, Dept. of ECE, R.K. College of Engineering, Vijayawada, Andhra Pradesh, Year-2016
[5] Skin Cancer Detection and classification Pratik Dubal, Sankirtan Bhatt, Chaitanya Joglekar, Dr. Sonali Patil, and Department of Information Technology K. J. Somaiya College of Engineering Vidyavihar, Year-2017
[6] Skin Cancer Detection using Artificial Neural Network,
Ekta Singhal M.Tech II Year, Dept of Computer Science Engineering, MUST – FET, Lakshmangarh, India Shamik Tiwari, Assistant Professor, Dept of Computer Science Engineering, MUST – FET, Lakshmangarh, India, Year-2015
[7] Detection of Skin Cancer Using Image Processing Techniques, Chandrahasa M, Varun Vadigeri and Dixit Salecha, Computer Science and Engineering, The National Institute of Engineering, Year-2016
[8] Image Processing for Skin Cancer Features Extraction Md.Amran Hossen Bhuiyan, Ibrahim Azad, Md.Kamal Uddin, Department of Computer Science & Telecommunication Engineering, Noakhali Science & Technology University, Bangladesh, International Journal of Scientific & Engineering Research Volume 4, Issue 2, February-2013

Sanjana M, V. Hanuman Kumar, "Regression Technique to Predict Stages of Basal Cell Carcinoma," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1006-1010, 2019.

Backpropagation algorithm (BP) is one of the most popularized and effective learning algorithm for learning neural networks, starting with Multilayer perceptron’s (MLP’s) to today’s Deep learning models in the domain of Artificial Intelligence (AI). Backpropagation algorithm works on two phases. The forward phase feed the network with input and communication links with synaptic weights, the activation function decides whether the hidden neurons fire or not. The primary focus of the present work is on the backpropagation error, which decides the amount of weight updating based on the errors. The driving force of the algorithm is to minimize the error by gradient descent where we differentiate the error function to get the gradient of the error and update the weights to reduce the error. In this paper, our approach is to reduce the error of Backpropagation neural network (BPNN) based on constraints using swarm intelligence based optimization method. For this, the optimization problem has been formulated mathematically with subjected constraints under the acceptable range of network parameters. This research investigation presents a comparison of results obtained from solving the minimization problem with different variants of swarm intelligence technique such as PSO, HBPSO, and ALCPSO.

Backpropagation, Deep learning, PSO, HBPSO, ALCPSO

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Munmi Gogoi, Ashim Jyoti Gogoi, Shahin Ara Begum, "Optimizing Error Function of Backpropagation Neural Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1011-1016, 2019.

Homology Modeling is an advance technique for the determination of protein structure. Here, we describe the necessary steps of computational technique for prediction of three dimensional protein structure and discuss various tools and techniques which are used for the same. Homology Modeling has an important role in Drug designing against various disease and we illustrate this by one example of ZIKA virus Protein. This article aims to introduce effortless technique for prediction of protein structure and the importance of known structure of ZIKA protein for drug discovery and ultimately for betterment of society.

Homology Modeling,Computational Technique,ZIKA protein

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Nehal V.Rami, Jil Dedania, "Homology Modeling:Protein Structure Prediction," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1017-1023, 2019.

In this hustling world, enjoyment is a need for every one of us to refresh our temper and energy. Entertainment regains our self-assurance for work and we can work extra enthusiastically. For revitalizing ourselves, we are able to pay attention to our preferred music or can watch films of our preference. For looking favorable movies online, we will make use of movie recommendation systems, that are extra dependable, when you consider that searching of preferred films would require more and more time which one can ‘t have the funds to waste. In this paper, to improve the quality of a movie recommendation system, a deep learning-based approach is presented to find out what exactly was being talked about in the user`s review and the sentiments that people are expressing.

Deep Learning , Recommendation System, Review Sentiment Analysis

[1] S. M. Taheri and I. Irajian, "DeepMovRS : A unified framework for deep learning-based movie recommender Systems," 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), Kerman, 2018, pp. 200-204. DOI: 10.1109/CFIS.2018.8336633
[2] H.-T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H.Aradhye, G.Anderson, G. Corrado, W. Chai, M. Ispir, R Anil, Z. Haque, L. Hong,V. Jain, X. Liu and H. Shah, Wide & Deep Learning for Recommender Systems, In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (DLRS 2016), (2016). ACM, New York, NY, USA, 7-10.
[3] P. Covington, J. Adams and E. Sargin, Deep Neural Networks for YouTube Recommendations, In Proceedings of the 10th ACM Conference on Recommender Systems, (2016). ACM, New York, NY, USA, 191-198.
[4] X. He, L. Liao, H. Zhang, L. Nie, X. Hu and T.-S. Chua, Neural Collaborative Filtering, In Proceedings of the 26th International Conference on World Wide Web (WWW ’17), (2017). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 173-182.
[5] Y. Koren, R. Bell and C. Volinsky, Matrix Factorization Techniques for Recommender Systems, Computer, 42, 8 (August 2009), 30-37.
[6]Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean and G. Hinton, Distributed Representations of Words and Phrases and their Compositionality,NIPS`13 Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2.
[7] H. Wang, N. Wang and D.-Y. Yeung, Collaborative Deep Learning for Recommender Systems, In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’15), (2015). ACM, New York, NY, USA, 1235-1244.
[8] F. Zhang, N. J. Yuan, D. Lian, X. Xie and W.-Y. Ma, Collaborative Knowledge Base Embedding for Recommender Systems, In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16), (2016). ACM, New York, NY, USA, 353-362.

Kunal Raj, Atulya Abhinav Das, Antariksh Guha, Parth Sharma, Mohana Kumar S, "Movie Recommendation System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1024-1028, 2019.

Artificial Intelligence (AI) plays an important role in many medical diagnosis systems. AI techniques uses for classifying the normal and abnormal cells are present in the cervix in the region of uterus. The classification of cancerous and non-cancerous cervical cells is detected by using AI techniques which gives accurate results. Compare to manual screening techniques like Pap smear test, the AI techniques gives better results and less time consuming. This paper presents several classifiers are used to classifies the normal and abnormal cells of Pap smear images.

Cervical cancer, Support Vector Machine, Discriminant analysis, Decision tree, K-nearest neighbor

[1] Rajeev Gupta, Abid Sarwar, Vinod Sharma, “Screening of cervical cancer by Artificial Intelligence based Analysis of Digitized Papanicolaou-Smear Images”, International Journal of Contemporary Medical Research 2017; 4(5): 1108-1113.
[2] Priyanka K Malli, Dr.Suvarna Nandyal, “Machine learning technique for detection of cervical cancer using KNN and Artificial neural network”, International journal of Emerging Trends and Technology in Computer Science(IJETICS), volume 6, Issue 4, July-Aug 2017.
[3] M.Anousouya Devi, S.Ravi, et.al, “Classification of cervical cancer using Artificial Neural Network”, IMCIP-2016, Procedia computer science, 89 (2016), 465-472.
[4] Chandraprabha R, Seema singh, “Artificial Intelligent System for diagnosis of cervical cancer: A Brief Review and Future outline”, IJLRET, NC3PS-2016, pp.38-41.
[5] Abid sarwar, Vinod sharma, Rajeev gupta, “Hybrid ensemble learning technique for screening of cervical cancer using Papanicolaou smear image analysis”, Medicine Universe, July 2015, vol-4, p.54-62.
[6] Dr.N.Ganesan, Dr.K.Venkatesh, et.al, “Application of Neural Networks in Diagnosing Cancer Disease using Demographic Data”, International Journal of Computer Application, 2010, volume 1-No.26.
[7] Miao wu, Chuanboyan, Huiquiang Liu, et.al, “Automatic Classification of Cervical Cancer rom Cytological images by using Convolutional Neural Network”, Portland press, Bioscience Reports (2018), 38, BSR20181769.
[8] M.K.Soumya, K.Snwha and C.Arunvinodh, “Cervical Cancer Detection and Classification using Texture Analysis”, Biomedical and Pharmacology Journal, vol.9 (2), 663-671 (2016).
[9] Masakazu Sato, Koji Horie, Aki Hara, “Application of Deep Learning to the Classification of images from Colposcopy”,Oncology letters, 15:3518-3523, 2018.
[10] Yessi Jusman, Siti Noraini Sulaiman, Nor Ashidi, “ Capability of New Features from FTIR Spectral of Cervical Cells for Cervical Precancerous
Diagnostic System using MLP Networks”, IEEE, 978-1-4244-4547-9/09, TENCON 2009.
[11] A. S. Phatak and B, P. A., Classification of MR Images of Cervical Cancer Using SVM and ANN Engineering, issue 2277, (2015).
[12] B.Ashok, Dr.Aruna “comparison of Feature selection methods for Diagnosis of Cervical cancer using SVM classifier” Journal of Engineering Research Applications IISN:2248-9622,vol.6,Issue 1,(Part-1) January2016 pp.94-99
[13] Y. Marinakis, G. Dounias, and J. Jantzen, “Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification,”Computers in Biology and Medicine,vol.39,no.1,pp.69–78,2009
[14] P.K.Malli,S.Nandyal,“Machine learning Technique for detection of Cervical Cancer using k-NN and Artificial Neural Network”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 2017
[15] Y. LeCun, Y. Bengioand, and G. E. Hinton. “Deep learning”, Nature, 521:436–444, 2015
[16] M.A.Devi,S.Ravi,J.Vaishnavi and S.Punitha,“Classification of Cervical Cancer using Artificial Neural Networks”, Procedia Computer Science 89, pp.465 – 472, 2016.
[17] B.Sharma and K.K.Mangat, “Various Techniques for Classification and Segmentation of Cervical Cell Images - A Review”, International Journal of Computer Applications (0975 – 8887), vol 147, 2016.
[18] L.Thampi and V.Paul,“Automatic Segmentation and Classification in Cervical Cancer Images: Evaluation and Challenges”, International Journal of Pure and Applied Mathematics, vol 119, 2018
[19] Kitchener HC, Blanks R, Cubie H, Desai M, Dunn G, Legood R, Gray a, Sadique Z, Moss S. MAVARIC “A comparison of automation-assisted and manual cervical screening: A randomised controlled trial”, Health technology assessment (Winchester, England) [Internet]. 2011 Jan;15:iii–iv, ix–xi, 1–170. pubmed/21266159.
[20] Trevisan J, Angelov PP, Carmichael PL, Scott AD, Martin FL. “Extracting biological information with computational analysis of Fourier-transform infrared (FTIR) bio-spectroscopy datasets: current practices to future perspectives”, The Analyst. 2012 Jul; 137:3202-3215.

Rajpriya.R, Saravanan.M.S, "Evaluation of Classifiers Performance in Cervical Cancer Detection," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1029-1035, 2019.

Indian economy is largely dependent on the crop produce provided by the farmers. The agricultural output is affected by the condition of the plants which will be baring the consumable products. Diseased plants show stunted growth and are way below the optimal output which needs to be generated. Thus, such plants need to be treated in timely manner so such diseases could be treated before the health of the plant deteriorates further. The project is aimed at solving this problem by detecting the disease which the plant is facing by using concepts of image classification and deep learning. A camera is used to take a picture of the leaf and image is passed through a pre calibrated weighted neural network which uses alexnet architecture. the Output neuron which the plant ends up after passing through neural network is disease identified by the Neural network and measures are suggested to improve the condition for helping the plant get rid of the disease and provide optimal results in agricultural output. Thus, focus is on detecting the disease preventing the plants growth and thus provide precautionary measures to solve the problem.

Agriculture, leaf disease, deep learning, alexnet architecture,CNN

[1] V. Singh and A. Misra, “Detection of plant leaf diseases using image segmentation and soft computing techniques,” Information Processing in Agriculture, vol. 4, no. 1, pp. 41–49, 2017.
[2] D. A. Bashish, M. Braik, and S. Bani-Ahmad, “Detection and Classification of Leaf Diseases using K-means-based Segmentation and Neural-networks-based Classification,” Information Technology Journal, vol. 10, no. 2, pp. 267–275, 2011.
[3] Sankaran, Sindhuja, et al. "A review of advanced techniques for detecting plant diseases." Computers and Electronics in Agriculture 72.1 (2010): 1-13.
[4] Sujatha, R., Sravan Kumar, Y., & Akhil, G. U. (2017). Leaf disease detection using image processing. J. Chem. Pharm. Sci, 670-672.
[5] Zhou, Zhiyan, et al. “Color-Based Corner Detection Algorithm for Rice Plant-Hopper Infestation Area on Rice Stem Using the RGB Color Space.” 2011 Louisville, Kentucky, August 7 - August 10, 2011, 2011, doi:10.13031/2013.37803
[6] Chaudhary, Piyush, et al. "Color transform based approach for disease spot detection on plant leaf." International Journal of Computer Science and Telecommunications 3.6 (2012): 65-70.
[7] Muthukannan, Kanthan, and Pitchai Latha. "A PSO model for disease pattern detection on leaf surfaces." Image Analysis & Stereology 34.3 (2015): 209-216.
[8] Nardari, Guilherme Vicentim, et al. "Crop Anomaly Identification with Color Filters and Convolutional Neural Networks." 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE). IEEE, 2018.

Sadhvik Reddy, Saumit Sandesh C, Srividya KA, Sandeep Reddy, Mohana Kumar S, Mallegowda M, "Study of Leaf Disease using Deep Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1036-1040, 2019.

We have been witnessing a continual transformation of the automotive industry, whereby new technologies are integrated into the vehicles, changing the traditional concept as we know it and improving safety, performance and efficiency. The vehicular revolution is fuelling the automotive industries, most significant transformation seen in decades. However, as modern vehicles become more connected they also become much more vulnerable to cyber-attacks. A fully working Intrusion Detection System approach is proposed to protect connected vehicles (fleets and individuals) against such attacks. We present a new approach for detecting anomalies, tailored to the temporal nature of our domain. However, as vehicles become more connected, they also become more vulnerable to remote cyber-attacks, as researchers have recently been pointing out. It is demonstrated how, in cases where it is possible to compromise cars internal network, it becomes possible to control a wide range of essential functions: disabling the brakes, selectively braking individual wheels, stopping the engine, etc.

Intrusion Detection, Anomaly Detection, IDS systems and platforms, Vehicle Network Security

[1] Bayrem Triki, Slim Rekhis, Mhamed Chammem, Noureddine Boudriga, “A privacy preseriving solution for the protection against Sybil attacks in vehicular adhoc networks”, Wireless and Mobile Networking Conference, 2013.
[2] Chen Chen, Xin Wang, Weili Han, Binyu Zang, “A robust detection of the Sybil attack in urban VANETs”, ICDCS Workshop 2009.
[3] W. Chang, J. Wu, “A survey of Sybil attack in Networks”, .Sensor Networks for Sustainable Development, CRC Press.
[4] S. Park, B. Aslam, D. Turgut, Cliff C. Zou, “Defence Against Sybil Attack in Vehicular Ad-hoc Network Based on Roadside Unit Support”, In MILCOM, pages
1-7, 2009.
[5] Bo Yu, Chang-Zhong Xu, Bin Xiao, “Detecting Sybil Attacks in VANETs”, In: Journal of Parallel and Distributed Computing 73.6 (2013), pp. 746 –756.
[6] Mina Rahbari, Mohammad Ali Jabreil Jamali, “Efficient Detection of Sybil Attack Based on Cryptography in VANET”, International Journal of Network Security &
Its Applications (IJNSA) (November 2011).
[7] Shan Chang, Yong Qi, Hongzi Zhu,Jizhong Zhao, Xuemin(Sherman) Shen, “Footprint: Detecting Sybil Attacks in Urban Vehicular Networks”, Parallel and
Distributed Systems, IEEE Transactions on, vol. 23. no. 6, 2012, pp. 1103-1114; DOI 10.1109/tpds.2011.263.
[8] Kenza Mekliche, Dr. Samira Moussaoui, “L-P2DSA: Location-based Privacy-Preserving Detection of Sybil Attacks”, 11th international symposium on programming
and systems (April 2013), pp. 187-192.
[9] Rasheed Hussain, Heekuck oh, “On Secure and Privacy Aware Sybil Attack Detection in Communication”, In Journal of Wireless Personal Communication, DOI: 10.1007/s11277-014-1659-5.
[10] Tong Zhou, Romit R. Choudhury, P. Ning, K. Chakrabarty, “P2DAP- Sybil Attacks Detection in Vehicular Ad-hoc Networks”, IEEE Journal on Selected Areas in Communications 29(3), 582–594 (2011).

Prajwal Gaikwad, Palash Saroware, Akshata Gaikwad, Shubhangi Tudme, Kajal Pardeshi, "IDS Vehicle Attack Detection and Prevention using Network Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1041-1046, 2019.