A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Sushmitha, N.
- Survey on the Various Security Issues Associated with the Internet of Things
Authors
1 Department of Information Science and Engineering, Rashtreeya Vidyalaya College of Engineering, Bangalore, IN
Source
Wireless Communication, Vol 10, No 5 (2018), Pagination: 99-103Abstract
The Internet of Things (IoT), is characterized as the system of physical articles—gadgets, vehicles, structures and different things—inserted with hardware, programming, sensors and system availability that empowers these items to gather and trade information. IoT has changed the world as far as mechanization and this is the reason IoT is in incline now a days. IoT empowers heap applications running from smaller scale to full scale and from the paltry to the basic. The usage of IoT has seen a significant growth in the previous years and will continue with a faster rate in the upcoming years. Since the number of devices connected to the Internet is also growing exponentially, more and more information flow takes place. The information may be sensitive such as financial records or personal confidential data. If security in IoT devices is not ensured, then the sensitive information is definitely vulnerable to attacks. Hence it is important to scrutinize the parameters that pose a threat to security in IoT. This paper discusses the broad categorization of the security challenges encountered in IoT and methods adopted to overcome in order to maintain information integrity and privacy.
Keywords
Security, IoT, Privacy, Sensors, Vulnerability.
References
- https://www.lifewire.com/introduction-to-the-internet-of-things-817766
- Amit Kumar Sikder, Giuseppe Petracca, Hidayet Aksu, Trent Jaeger, and A. Selcuk Uluagac, “A Survey on Sensor-based Threats to Internet-of-Things (IoT) Devices and Applications”, in Cryptography and Security (cs.CR), arXiv:1802.02041v1 [cs.CR]
- “Mems accelerometer hardware design flaws (updatea),”https://ics-cert.us-cert.gov/alerts/ICS- ALERT-17-073-01A, accessed: 2017-5-30.
- J. M. Kizza, Guide to Computer - Network Security. Springer, 2013.
- Mohamed Abomhara and Geir M. Køien, “Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks”, 2015, Journal of Cyber Security, Vol 4, Pg 65-88
- I.Naumann and G. Hogben, “Privacy features of european eid card specifications,”Network Security, vol.2008, no. 8, pp. 9–13, 2008.
- Tuhin Borgohain, Uday Kumar and Sugata Sanyal, “Survey of Security and Privacy Issues of Internet of Things”
- Shen, Guicheng, and Bingwu Liu. "The visions,technologies, applications and security issues of Internet of Things." E-Business and E-Government (ICEE), 2011 International Conference on. IEEE, 2011.
- https://blog.atlasrfidstore.com/7-types-security-attacks-rfid-systems
- https://devopedia.org/iot-operating-systems
- Data Encryption Using XTEA and OTP Generation for E-Payment System
Authors
1 Department of Information Science, RV College of Engineering, Bengaluru, IN
Source
Networking and Communication Engineering, Vol 10, No 5 (2018), Pagination: 102-105Abstract
Recently, data security is playing a major role in our day-to-day operations. Securing the Personal Identification Information (PII) is a difficult task. In this paper, two phase authentication process is used to secure the data. CIA triad means Confidentiality, Integrity and Availability, are the three main goals offered by the modern Cryptography. In two phase process, the first is OTP generation and verification for authorized users, the second phase of authentication is provided by encrypting the user details before it stores in the database. When the user wants to view the data it decrypts and displays the content. In this paper Extended Tiny Encryption Algorithm (XTEA) is used to encrypt the plain text into cipher text and also decrypt the cipher text into plain text. XTEA is a block cipher which uses a 64-bit block size and 128 bit key. Main aim of this paper is to provide security for the PII.
Keywords
Security, PII, OTP, XTEA, Encryption and Decryption, Cipher Text, Plain Text.References
- Berna Ors, Osman Semih Kayhan, Ahmet Turan Erozan, “JPEG image encryrption via TEA Algotithm” IEEE, 2015.
- John Jenkins, Joseph Shelton, Kaushik Roy, “One-time Password for Biometric Systems: Disposable Feature Templates” IEEE, 2017.
- Silke Holtmanns, Ian Oliver, “SMS and One-Time-Password Interception in LTE Networks”, IEEE, 2017.
- Devashish Kumar, Amit Agrawal, Puneet Goyal,” Efficiently Improving the Security of OTP”, IEEE, 2015.
- Schneier, Bruce; Kelsey, John (1996-02-21). "Unbalanced Feistel networks and block cipher design". Fast Software Encryption. Springer, Berlin, Heidelberg
- Benedicto B. Balilo Jr., Bobby D. Gerardo, Ruji P. Medina, “ Design of Physical Authentication based on OTP Keypad”, IEEE, 2017
- Shubham Srivastava, Sivasankar M,” On the Generation of Alphanumeric One Time Password” IEEE, 2016.
- Anonymous is my Name-A Survey Paper on Dark Web
Authors
1 Department of Information Science and Engineering, R V College of Engineering, Bengaluru, IN
Source
Networking and Communication Engineering, Vol 10, No 5 (2018), Pagination: 106-109Abstract
In the current world every transaction is online. In order to make the transactions and operations easier, technology and internet is used in every walk of life. The content which we are accessing through popular web browsers are only in the surface level of web. But the web space which was built and used for communication and maintenance of confidential information is now being a place where brutal and illegal activities are happening on a large scale, the place which is called –the dark web. It is portrayed as a den of mysterious and illegal activity. This paper illustrates the survey on the whereabouts of the dark web, its current usage in various activities, the technique of anonymity and a few relevant case studies.
Keywords
The Onion Router, Anonymous, Illegal, Deep Web, Dark Web.References
- M. Splitters, F. Klaver, G. Koot and M. Van Staalduinen,"Authorship Analysis on Dark Marketplace Forums," in proceeding of Intelligence and Security Informatics Conference (EISIC), Manchester, 2015.
- K. Bharat and A. Broder , "A technique for measuring the relative size and overlap of public Web search engines," Computer Networks and ISDN Systems, vol. 30, no. 1-7, pp. 379-388, 1998.
- M. Bergman, "White Paper: The Deep Web: Surfacing Hidden Value," The Journal of Electronic, vol. 7, no. 1, 2001.
- M. Eddy, "Inside the Dark Web," 04 02 2015. Available:http://uk.pcmag.com/security/39461/guide/ inside-thedark-web.
- M. Egan, "What is the Dark Web? How to access the Dark Web. What's the difference between the Dark Web and the Deep Web?," 2016 06 28. [Online]. Available:http://www.pcadvisor.co.uk/how- to/internet/what-isdark-web-how-access-dark-web- deep-jocbeautfiulpeople-3593569/.
- H. Oman, "Security Technology Progress: The 37th IEEE-AESS Carnahan Conference, Taiwan," IEEE Aerospace and Electronic Systems Magazine, vol. 19, no.2, pp. 35-40, 2004.
- H. Chen, "The Terrorism Knowledge Portal: Advanced Methodologies for Collecting and Analyzing Information from the„ Dark Web‟ and Terrorism Research Resources," 08 2003. [Online]. Available http://www.slideshare.net/suyu22/the- terrorismknowledge-portal-advanced-methodologies-forcollecting-and-analyzing-information-from-the- darkweb-and-terrorism-research-resources.
- A Survey on Cyber Security Attacks in Connected Vehicles
Authors
1 Department of Information Science and Engineering, R V College of Engineering, Bangalore, IN
Source
Networking and Communication Engineering, Vol 10, No 5 (2018), Pagination: 110-112Abstract
In 2016, the “Cyber security best practice for modern vehicle” is introduced by the US Department of Transportation. They encouraged companies to follow the National Institute of Standards and Technologies Cyber Security framework, i.e. identify, protect, detect, respond and recover. Now a days modern cars are new target to the hackers as they become increasingly connected. Cyber security attacks like door locks, disable brakes and engine shutdown are few examples. This paper focuses on solution that vehicle systems should take against the cyber attacks and also highlights the importance of making cyber security a priority for the automotive industry.Keywords
Cyber Security, Cyber Attacks.References
- Mahmoud Hashem Eiza, University of Central Lancashire Qiang Ni, “Driving with Sharks: Rethinking Connected Vehicles with Vehicle Cyber security”, IEEE vehicular technology magazine February 2017.
- S. Khandelwal, “Car Hackers Could Face Life In Prison. That's Insane!,” The Hacker News, May 01, 2016. [Online]. May 2016.
- A. Greenberg, “Hackers remotely kill a Jeep on the highway – with me in it,” WIRED, July 21, 2015. [Online]. Sep 30 2015.
- S. Curts,” Self-driving cars can be hacked using laser Pointer,” The telegraph, sep. 08, 2015.
- W.Yan, ”A Two-Year Survey on Security Challenges in Automotive Threat Landscape,” in Proc. IEEE ICCVE, Shenzhen Oct. 2015, pp. 181-189.
- A Complete Survey on Security Issues in E-Mail and Face-Book
Authors
1 Department of Information Science and Engineering, R V College of Engineering, Bengaluru, IN
Source
Digital Image Processing, Vol 10, No 5 (2018), Pagination: 87-89Abstract
Social Media! This word is making people crazy in the era of internet. Every single person is interested in evolution of social networks. Based on the principle of "six degrees of separation", social network allows each person to be connected to five different people in the network. This provides an atmosphere for social connections thus increasing the intensity of shared information. Be it through LinkedIn for job recruitment and client communication or through Face-book to promote people or through Gmail for multiple applications Social Media has come a long way since its start. Although this seems interesting, it is more prone to misuse and thus it is problematic. This paper brings the wide issues related to social media which may be either through humans or any abrupt inaccurate data. Also various measures to be taken care are discussed. Results shows that the communication-medium either Face-book or E-mail leads to important differences in attack inclination. But to the surprise, the success of Face-book related attacks is higher than that of E-mail.
References
- Haider M. Al-Mashhadi and Mohammed H. Alabiech A Survey of Email Service; Attacks, Security Methods and Protocols " International Journal of Computer Applications (0975 – 8887) Volume 162 – No 11, March 2017
- G. Shanmugasundaram,S. Preethi, I. Nivedha, "Investigation on Social Media Spam Detection",2017 International Conference on Innovations in information Embedded and Communication Systems (ICIIECS)
- Zinaida Benenson, Anna Girard, Nadina Hintz and Andreas Luder," Susceptibility to URL-based Internet Attacks: Facebook vs. Email”, The Sixth IEEE Workshop on SECurity and SOCial Networking, 2014.
- Analysis of Machine Learning Algorithms in Prediction of Cardiovascular Diseases
Authors
1 RVCE, Bengaluru, IN
Source
Automation and Autonomous Systems, Vol 10, No 5 (2018), Pagination: 90-92Abstract
Heart failure is considered as one among the most fatal diseases in the contemporary world. Diabetes mellitus, hypertension, and dyslipidemia are considered as the observed predictors of cardiovascular disease. Few routine style risk factors include depression, physical inactivity, smoking, alcohol consumption, stress, food habits and obesity which are the major causes for cardiovascular disease. In India, heart failure among people is increasing at an alarming rate because there is lack of proper estimation for the ischolar_main cause of cardiovascular diseases and the absence of surveillance programme in order to track the occurrence, extensiveness and outcomes of heart failure. Data mining techniques prove to be an efficient approach in predicting the risk of cardiovascular diseases in the data deluge age. In this research study, data mining techniques are applied to get useful information from medical reports of patients. Using machine learning algorithms, the impact of each risk factor on heart disease is predicted. Firstly, the heart disease dataset is collected from the Cleveland Heart Disease database. With the help of the dataset, the attributes significant to the heart attack prediction are extracted. The dataset is split into training and test dataset. Different classification techniques are applied on preprocessed data to measure their accuracy in predicting the risk of heart disease. Two such algorithms are Logistic Regression and Gradient Boosting Algorithm. The objective is to attain high accuracy in the prediction of risk of cardiovascular diseases among patients. In order to prevent the occurrence of the cardiovascular diseases, the prevalence of risk factors should be minimized. Further, early conclusion and treatment can enhance quality and future of individuals who have heart disappointment.
Keywords
Data Mining, Cardiovascular Diseases, Machine Learning Algorithms, Logistic Regression, Gradient Boosting Algorithm.References
- Niti Guru, Anil Dahiya, Navin Rajpal, "Decision Support System for Heart Disease Diagnosis Using Neural Network", Delhi Business Review, Vol. 8, No. I (January - June 2007.
- M. Hertzong, and B. Pozehl, “Cluster analysis of symptom occurrence to identify subgroups of heart failure patients: A pilot study,” Journal of Cardiovascular Nursing, vol. 25, pp. 273–283, July/August 2010.
- M. Panahiazar, V. Taslimitehrani, N. Pereira, and J. Pathak, “Using EHRs and machine learning for heart failure survival analysis,” Studies in health technology and informatics, MedInfo, vol. 216, pp. 40-44, 2015.
- K. Kwon, H. Hwang, H. Kang, K. G. Woo, amd K. Shim, “A remote cardiac monitoring system for preventive care in Consumer Electronics (ICCE),” Proc. IEEE, pp. 197-200, January 2013.