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Journal of Cyber Security and Mobility

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: 1

Published In:   January 2019

Publication Frequency: Quarterly

Articles in 2020

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A Brief Review of Messaging Protocol Standards for Internet of Things (IoT)

Abdullah Ahmed Omar Bahashwan1 and Selvakumar Manickam2

1National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia (USM), Penang, Malaysia
2Senior Lecturer and Researcher at National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia (USM), Penang, Malaysia

Abstract: [+]    |    Download File [ 3313KB ]    |   Read Article Online

Abstract: As the most recent development in cyberspace technologies, the Internet of Things (IoT) has received substantial research attention. It now occupies a crucial role in advancing society and industry. IoT merges Social Networks and connects devices to allow people interact one another and simplify information sharing. IoT has two main aspects: the internal and the external. Whereas the latter side consists of sensors, actuators, etc. which are physically likely, the former consists of protocols that are highly important. IoT has particular protocols in different layers such as Transport layer, Physical/Link layer and Application layer, which is accountable for messaging and supplying services. This paper explores the foremost widespread IoT data transmission protocols and their main options. The protocols play a vital role in inventing up-to-date IoT projects and devices. This article provides an uncomplicated review of IoT data protocols based on user requirement.

Keywords: Internet of Things (IoT), Applicaton protocol, Data protocols, MQTT, CoAP,Websocket.

Unsupervised Monitoring of Network and Service Behaviour Using Self Organizing Maps

Duc C. Le, A. Nur Zincir-Heywood and Malcolm I. Heywood

Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada

Abstract: [+]    |    Download File [ 2542KB ]    |   Read Article Online

Abstract: Botnets represent one of the most destructive cybersecurity threats. Given the evolution of the structures and protocols botnets use, many machine learning approaches have been proposed for botnet analysis and detection. In the literature, intrusion and anomaly detection systems based on unsupervised learning techniques showed promising performances. This paper investigates the capability of the Self Organizing Map (SOM), an unsupervised learning technique as a data analytics system. In doing so, the aim is to understand how far such an approach could be pushed to analyze the network traffic, and to detect malicious behaviours in the wild. To this end, three different unsupervised SOM training scenarios for different data acquisition conditions are designed, implemented and evaluated. The approach is evaluated on publicly available network traffic (flows) and web server access (web requests) datasets. The results show that the approach has a high potential as a data analytics tool on unknown traffic/web service requests, and unseen attack behaviours. Malicious behaviours both on network and service datasets used could be identified with a high accuracy. Furthermore, the approach achieves comparable performances to that of popular supervised and unsupervised learning methods in the literature. Last but not the least, it provides unique visualization capabilities for enabling a simple yet effective network/service data analytics for security management.

Keywords: network and service data analysis, unsupervised learning, malicious behaviour analysis.

A Cloud Based Conceptual Identity Management Model for Secured Internet of Things Operation

Abubakar Bello1, and Venkatesh Mahadevan2

1Western Sydney University, Penrith, NSW 2751, Australia
2Melbourne Institute of Technology, Melbourne, VIC 3000, Australia

Abstract: [+]    |    Download File [ 291KB ]    |   Read Article Online

Abstract: An era ago, projecting artificial intelligence as the pillar of next-generation technology would have been technically difficult. Today, machines are getting smarter, sparking a new wave of technology that resulted to Internet of Things (IoT). With IoT in play, individuals are able to connect more electronic devices other than smartphones and computers to the Internet. The vision is to create the possibility to manage electronic appliances via the Internet with the most minimal human intervention. IoT promises the application of computing to anything anywhere, and anyone at any time. Thus, it has been estimated that over 100 billion devices will be running the IoT model – drawing the power of cloud processing to create a massive network of devices that are bound to change the essential facets of life in various dimensions. However, several obstacles remain to fulfill this vision, among them is security concerns from an Identity of Things (IDoT) management perspective. IoT devices and users are already under cyber attacks, and any lapse in identity management will propagate these attacks. This paper examined how identity management for IoT is likely to play out in a world where the Internet and cloud technologies are expected to take center stage in the running of day-to-day activities. The paper analyses the identity of things challenges in IoT, followed by a proposal of cloud identity management model for IoT.

Keywords: Internet of Things, IoT Security, Identity of Things, Cloud IoT, Identity Management.

A Survey on User Profiling Model for Anomaly Detection in Cyberspace

Arash Habibi Lashkari, Min Chen and Ali A. Ghorbani

Canadian Institute for Cybersecurity (CIC), University of New Brunswick (UNB) Fredericton, Canada

Abstract: [+]    |    Download File [ 1686KB ]    |   Read Article Online

Abstract: In the face of escalating global Cybersecurity threats, having an automated forewarning system that can find suspicious user profiles is paramount. It can work as a prevention technique for planned attacks or ultimate security breaches. Significant research has been established in attack prevention and detection, but has demonstrated only one or a few different sources with a short list of features. The main goals of this paper are, first, to review the previous user profiling models and analyze them to find their advantages and disadvantages; second, to provide a comprehensive overview of previous research to gather available features and data sources for user profiling; third, based on the deficiencies of the previous models, the paper proposes a new user profiling model that can cover all available sources and related features based on the cybersecurity perspective. The proposed model includes seven profiling criteria for gathering user’s information and more than 270 features to parse and generate the security profile of a user.

Keywords: User Profiling, Cybersecurity Profiling, Big Security Data, Security Data Source, Security Profiling Features, Anomaly Detection, Cybersecurity forewarning system.

A Case Study in Tailoring a Bio-Inspired Cyber-Security Algorithm: Designing Anomaly Detection for Multilayer Networks

Gonzalo P. Suárez1,2, Lazaros K. Gallos1 and Nina H. Fefferman2,3

1Center for Discrete Mathematics and Theoretical Computer Science (DIMACS), Rutgers University, Piscataway, NJ, USA
2Department of Ecology and Evolutionary Biology, College of Arts & Sciences, University of Tennessee, Knoxville, TN, USA
3Department of Mathematics, College of Arts & Sciences, University of Tennessee, Knoxville, TN, USA

Abstract: [+]    |    Download File [ 2840KB ]    |   Read Article Online

Abstract: Although bio-inspired designs for cybersecurity have yielded many elegant solutions to challenging problems, the vast majority of these efforts have been ad hoc analogies between the natural and human-designed systems.We propose to improve on the current approach of searching through the vast diversity of existing natural algorithms for one most closely resembling each new cybersecurity challenge, and then trying to replicate it in a designed cyber setting. Instead, we suggest that researchers should follow a protocol of functional abstraction, considering which features of the natural algorithm provide the efficiency/effectiveness in the real world, and then use those abstracted features as design components to build purposeful, tailored (perhaps even optimized) solutions. Here, we demonstrate how this can work by considering a case study employing this method. We design an extension of an existing (and ad hoc-created) algorithm, DIAMoND, for application beyond its originally intended solution space (detection of Distributed Denial of Service attacks in simple networks) to function on multilayer networks.We show how this protocol provides insights that might be harder or take longer to discover by direct analogy-building alone; in this case, we see that differential weighting of shared information by the providing network layer, and dynamic individual thresholds for independent analysis are likely to be effective.

Keywords: Cyber-Security, Bio-Inspired, Anomaly detection, Multilayer Networks.

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