1,575 research outputs found
A Cognitive Framework to Secure Smart Cities
The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms
DESIGN FRAMEWORK FOR INTERNET OF THINGS BASED NEXT GENERATION VIDEO SURVEILLANCE
Modern artificial intelligence and machine learning opens up new era towards video
surveillance system. Next generation video surveillance in Internet of Things (IoT) environment is
an emerging research area because of high bandwidth, big-data generation, resource constraint
video surveillance node, high energy consumption for real time applications. In this thesis, various
opportunities and functional requirements that next generation video surveillance system should
achieve with the power of video analytics, artificial intelligence and machine learning are
discussed. This thesis also proposes a new video surveillance system architecture introducing fog
computing towards IoT based system and contributes the facilities and benefits of proposed system
which can meet the forthcoming requirements of surveillance. Different challenges and issues
faced for video surveillance in IoT environment and evaluate fog-cloud integrated architecture to
penetrate and eliminate those issues.
The focus of this thesis is to evaluate the IoT based video surveillance system. To this end,
two case studies were performed to penetrate values towards energy and bandwidth efficient video
surveillance system. In one case study, an IoT-based power efficient color frame transmission and
generation algorithm for video surveillance application is presented. The conventional way is to
transmit all R, G and B components of all frames. Using proposed technique, instead of sending
all components, first one color frame is sent followed by a series of gray-scale frames. After a
certain number of gray-scale frames, another color frame is sent followed by the same number of
gray-scale frames. This process is repeated for video surveillance system. In the decoder, color
information is formulated from the color frame and then used to colorize the gray-scale frames. In
another case study, a bandwidth efficient and low complexity frame reproduction technique that is
also applicable in IoT based video surveillance application is presented. Using the second
technique, only the pixel intensity that differs heavily comparing to previous frame’s
corresponding pixel is sent. If the pixel intensity is similar or near similar comparing to the
previous frame, the information is not transferred. With this objective, the bit stream is created for
every frame with a predefined protocol. In cloud side, the frame information can be reproduced by
implementing the reverse protocol from the bit stream.
Experimental results of the two case studies show that the IoT-based proposed approach
gives better results than traditional techniques in terms of both energy efficiency and quality of the video, and therefore, can enable sensor nodes in IoT to perform more operations with energy
constraints
Forensic Data Analytics for Anomaly Detection in Evolving Networks
In the prevailing convergence of traditional infrastructure-based deployment
(i.e., Telco and industry operational networks) towards evolving deployments
enabled by 5G and virtualization, there is a keen interest in elaborating
effective security controls to protect these deployments in-depth. By
considering key enabling technologies like 5G and virtualization, evolving
networks are democratized, facilitating the establishment of point presences
integrating different business models ranging from media, dynamic web content,
gaming, and a plethora of IoT use cases. Despite the increasing services
provided by evolving networks, many cybercrimes and attacks have been launched
in evolving networks to perform malicious activities. Due to the limitations of
traditional security artifacts (e.g., firewalls and intrusion detection
systems), the research on digital forensic data analytics has attracted more
attention. Digital forensic analytics enables people to derive detailed
information and comprehensive conclusions from different perspectives of
cybercrimes to assist in convicting criminals and preventing future crimes.
This chapter presents a digital analytics framework for network anomaly
detection, including multi-perspective feature engineering, unsupervised
anomaly detection, and comprehensive result correction procedures. Experiments
on real-world evolving network data show the effectiveness of the proposed
forensic data analytics solution.Comment: Electronic version of an article published as [Book Series: World
Scientific Series in Digital Forensics and Cybersecurity, Volume 2,
Innovations in Digital Forensics, 2023, Pages 99-137]
[DOI:10.1142/9789811273209_0004] \c{opyright} copyright World Scientific
Publishing Company [https://doi.org/10.1142/9789811273209_0004
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