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Journal of Cyber Security and Mobility

Editors-in-Chief:
Ashutosh Dutta, Johns Hopkins University, USA
Ruby Lee, Princeton University, USA
Neeli R. Prasad, International Technological University, San Jose, USA
Wojciech Mazurczyk, Warsaw University of Technology, Poland


ISSN: 2245-1439 (Print Version),

ISSN: 2245-4578 (Online Version)
Vol: 8   Issue: 4

Published In:   October 2019

Publication Frequency: Quarterly


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Hardware Random Number Generator Using FPGA

doi: https://doi.org/10.13052/jcsm2245-1439.841
D. Indhumathi Devi, S. Chithra and M. Sethumadhavan

TIFAC-CORE in Cyber Security, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India

Abstract: [+]    |    Download File [ 1949KB ]    |   Read Article Online

Abstract: Random numbers are employed in wide range of cryptographic applications. Output of an asynchronous sampling of ring oscillators can be used as the source of randomness and Linear Hybrid CellularAutomata is used to improve the quality of random data. FPGA is an ideal platform for the implementation of random number generator for cryptographic applications. The circuit described in this paper has been implemented on a highly efficient FPGA board which generated a 32-bit random number at a frequency of 125 MHz. The generated sequence of random numbers were subjected to Diehard test and NIST test for testing randomness and found to pass these tests. These tests are a battery of statistical tests for measuring the quality of a random number generator.

Keywords: Ring oscillator, Linear Hybrid Cellular Automata, Field Programmable Gate Array, Diehard Test.

Evaluating the Impact of Traffic Sampling on AATAC’s DDoS Detection

doi: https://doi.org/10.13052/jcsm2245-1439.842
Gilles Roudière and Philippe Owezarski

LAAS-CNRS, Universit´e de Toulouse, CNRS, Toulouse, France

Abstract: [+]    |    Download File [ 5972KB ]    |   Read Article Online

Abstract: As Distributed Denial of Service (DDoS) attack are still a severe threat for the Internet stakeholders, they should be detected with efficient tools meeting industrial requirements.We previously introduced theAATACdetector, which showed its ability to accurately detect DDoS attacks in real time on full traffic, while being able to cope with the several constraints due to an industrial operation, as time to detect, limited resources for running detection algorithms, detection autonomy for not wasting uselessly administrators’ time. However, in a realistic scenario, network monitoring is done using sampled traffic. Such sampling may impact the detection accuracy or the pertinence of produced results. Consequently, in this paper, we evaluateAATAC over sampled traffic. We use five different count-based or time-based sampling techniques, and show thatAATAC’s resources consumption is in general greatly reduced with little to no impact on the detection accuracy. Obtained results are succinctly compared with those from FastNetMon, an open-source threshold-based DDoS detector.

Keywords: DDoS detection, sampled traffic, unsupervised learning.

PSV-GWO: Particle Swarm Velocity Aided GWO for Privacy Preservation of Data

doi: https://doi.org/10.13052/jcsm2245-1439.843
Jyothi Mandala1,2, and Dr. M. V. P. Chandra Sekhara Rao3

1Research Scholar, ANU, Guntur, Andhra Pradesh 522019, India
2Assistant Professor, GMRIT, Rajam, Andhra Pradesh 532127, India
3Professor, RVR & JC College of Engineering, Guntur, Andhra Pradesh 522019, India

Abstract: [+]    |    Download File [ 7985KB ]    |   Read Article Online

Abstract: Due to the maximum usage of Social Networking Sites (SNS) the number of individuals that are posting their health information online is increasing. The health information of the user’sis disclosed on these sites, where the organization or various individuals can mine that for numerous research and commercial purposes. Because of this sensitive nature of the medical information, the privacy protection is said to be a main focus for the researchers. On analyzing many of the conventional methods, there is an improvement in the sanitization process but still lacks on the restoration of data. Thus, this paper focused on the privacy preservation over the healthcare records. The proposed model is about the enhancement in the sanitization technique that hides the raw information presented by the users. The sanitization process involves the generation of key that created optimally by introducing a new Particle Swarm Velocity aided GWO (PSV-GWO) algorithm. Additionally, the authorized user can restore these sanitized medical data securely. Finally, the traditional algorithms are compared with the proposed model in terms of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Crow Search Optimization (CSA) and Adaptive Awareness Probability-based CSA (AAP-CSA) and the outcome is analyzed.

Keywords: Healthcare Data Preservation, Data Restoration, Sanitized Data, Key generation, Modified Optimization.

Social Network Self-Protection Model: What Motivates Users to Self-Protect?

doi: https://doi.org/10.13052/jcsm2245-1439.844
Damjan Fujs1, Anže Mihelič1,2 and Simon Vrhovec1

1University of Maribor, Faculty of Criminal Justice and Security, Ljubljana, Slovenia
2University of Ljubljana, Faculty of Law, Ljubljana, Slovenia

Abstract: [+]    |    Download File [ 965KB ]    |   Read Article Online

Abstract: Social networks are an indispensable activity for billions of users making them an attractive target for cyberattacks. There is however only scarce research on self-protection of individuals outside the organizational context. This study aims to address this gap by explaining what motivates individuals to self-protect on social networks. A survey (N = 274) has been conducted among Slovenian Facebook users to test the proposed social network selfprotection model. The results show that privacy concerns and perceived threats significantly affect user’s intention to self-protect. Descriptive norm only affects intention indirectly through perceived threats appearing to contradict a large body of research on behavioral intentions. “If others protect themselves, there must be a serious threat.” On the other hand, it also helps to explain why the direct effect of descriptive norm on security-related behavior is relatively small in other studies. Surveillance concerns, regulation and information sensitivity all significantly affect privacy concerns.Although privacy concerns are currently high due to the recent high-profile privacy-related scandals (e.g., Cambridge Analytica, Facebook, Google+), it may not affect the motivation of users to self-protect as they dealt with issues far beyond their control. Nevertheless, users with higher levels of privacy concerns than their peers may be more motivated to self-protect.1

Keywords: Self-protective behavior, social networks, privacy concerns, surveillance concerns, information sensitivity, regulation, perceived threats, vulnerability, severity.

iShield: A Framework for Preserving Privacy of iOS App User

doi: https://doi.org/10.13052/jcsm2245-1439.845
Arpita Jadhav Bhatt, Chetna Gupta and Sangeeta Mittal

Department of Computer Science & IT, Jaypee Institute of Information Technology, Noida, India

Abstract: [+]    |    Download File [ 4002KB ]    |   Read Article Online

Abstract: Do iOS apps honour user’s privacy? Protection of user’s privacy by apps has lately emerged as a big challenge. Many studies have identified that there exists an inherent trade-off between end user’s privacy and apps’functionality. Some methods have been proposed to preserve user’s privacy of specific data like location and health information. However, a comprehensive framework to enable privacy preserving data sharing by apps has not been found. In this paper, we have proposed iShield - a privacy preserving framework that can be easily integrated by developers at the time of app creation to enforce privacy with minimal performance overhead. Privacy threat to a user has been quantified by calculation of privacy disclosure score of an app user. Empirical results demonstrate that the approach significantly reduces the privacy disclosure of the user.

Keywords: Privacy preserving framework, iOS Apps, static and dynamic analysis, information security.

River Publishers: Journal of Cyber Security and Mobility