The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.
This paper presents techniques for segmentation and change classification using logistic regression. The research was conducted on SPOT 5 multispectral multitemporal images covering the 2010 floods in Pakistan. Segmentation was performed to extract the built up area (BUA) from the satellite images and change detection was performed to find the damaged BUA. The damaged area was classified into three categories based on the extent of damage. The segmentation results were validated using statistical measures like precision, recall, and dice coefficient on available ground truth. The results of change classification were compared and found consistent with the manual assessment report produced by UNO experts using Worldview 1 satellite imagery with submeter resolution. The proposed scheme and results give an indication that SPOT 5 imagery can be used for fast automatic damage assessment and classification immediately after a natural calamity. The proposed change detection technique was also applied on Unites States Geographical Survey dataset. We compared our change detection results with established methods like change vector analysis, Principal component analysis using K-means and commercially available software Erdas Imagine on both the above-mentioned datasets. The comparison results suggest that our proposed algorithm performs better than the other methods.
Summary
Software‐defined networking (SDN) is a new networking architecture that decouples both the control and management planes from the data plane of forwarding devices. Control and management planes are implemented at a logically centralized entity called the controller. Despite numerous advantages, SDN is more prone to logical errors like loops, black holes, network reachability problems, and access control list (ACL) policies violation. In the existing approaches, the network requirements are specified by different network administrators using the ACL policies. SDN allows multiple network administrators to specify the ACL policies simultaneously, which may lead to conflicts and overlaps among the ACL policies. In this research work, a novel technique, called auto‐resolving overlapping and conflicts in ACL policies (ROCA), is proposed to efficiently detect and solve both the conflicts and the overlaps among the ACL policies by using the techniques of set theory, 3D structure, and separating axis theorem. It is shown by simulation and testing on the real network traces that ROCA outperforms the existing approaches in terms of computation time avoiding conflicts and overlapping among the ACL policies.
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