Edge Computing-Based Intrusion Detection System for Smart Cities Development Using IoT in Urban Areas

Author(s):  
S. Vimal ◽  
A. Suresh ◽  
P. Subbulakshmi ◽  
S. Pradeepa ◽  
M. Kaliappan
2019 ◽  
Vol 90 ◽  
pp. 101842 ◽  
Author(s):  
Moayad Aloqaily ◽  
Safa Otoum ◽  
Ismaeel Al Ridhawi ◽  
Yaser Jararweh

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5954
Author(s):  
Vladimir Shakhov ◽  
Olga Sokolova ◽  
Insoo Koo

Multi-access edge computing has become a strategic concept of the Internet of Things. The edge computing market has reached USD several billion and is growing intensively. In the edge-computing paradigm, most of the data is processed close to, or at the edge of, the network. This greatly reduces the computation and communication load of the network core. Moreover, edge computing provides better support for user privacy. On the other hand, an increase in data processing locations will proportionately increase the attack surface. An edge node can be put out of service easily by being flooded with spoofed packets owing to limited capacities and resources. Furthermore, wireless edge nodes are quite vulnerable to energy exhaustion attacks. In this situation, traditional network security mechanisms cannot be used effectively. Therefore, a tradeoff between security and efficiency is needed. This study considered the requirements under which the use of an intrusion detection system (IDS) is justified. To the best of our knowledge, this is a first attempt to combine IDS quality, system performance degradation due to IDS operations, and workload specificity into a unified quantitative criterion. This paper is an extended version of a report published in the proceedings of the ICCSA 2020 and differs from it in many ways. In particular, this paper considers novel mathematical problems regarding the deployment strategies for an IDS and the corresponding inverse problems and provides closed-form solutions for a few previously unsolved problems.


2020 ◽  
Vol 61 ◽  
pp. 102324 ◽  
Author(s):  
Md Arafatur Rahman ◽  
A. Taufiq Asyhari ◽  
L.S. Leong ◽  
G.B. Satrya ◽  
M. Hai Tao ◽  
...  

2021 ◽  
Vol 14 (3) ◽  
pp. 20-37
Author(s):  
Arun Kumar Bediya ◽  
Rajendra Kumar

Internet of things (IoT) comprises a developing ecosystem of responsive and interconnected devices, sensors, networks, and software. The internet of things keeps on extending with the number of its different equipment segments for smart cities, healthcare, smart homes, assisted living, smart vehicles, transportation, framework, and many more are the areas where the internet of things benefits human lives. IoT networks are meant to be monitored on real-time events, and if these devices get attacked, it can have an unfavorable effect on the system. This paper discussed many possible attacks at IoT networks and distributed denial of service (DDoS) attack is one of the most dangerous among them. Blockchain technology can be utilized to develop a framework to protect IoT systems; blockchain is a new technology used for cryptocurrency transactions. This paper proposed BIoTIDS an intrusion detection system for the IoT network using blockchain. BIoTIDS is able to detect an intruder in the IoT network and also able to identify DDoS attacks in IoT networks.


2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xuefei Cao ◽  
Yulong Fu ◽  
Bo Chen

In this paper, a network intrusion detection system is proposed using Bayesian topic model latent Dirichlet allocation (LDA) for mobile edge computing (MEC). The method employs tcpdump packets and extracts multiple features from the packet headers. The tcpdump packets are transferred into documents based on the features. A topic model is trained using only attack-free traffic in order to learn the behavior patterns of normal traffic. Then, the test traffic is analyzed against the learned behavior patterns to measure the extent to which the test traffic resembles the normal traffic. A threshold is defined in the training phase as the minimum likelihood of a host. In the test phase, when a host’s test traffic has a likelihood lower than the host’s threshold, the traffic is labeled as an intrusion. The intrusion detection system is validated using DARPA 1999 dataset. Experiment shows that our method is suitable to protect the security of MEC.


2020 ◽  
Vol 9 (3) ◽  
pp. 39 ◽  
Author(s):  
Rabie A. Ramadan

The world is experiencing the new development of smart cities. Smart cities’ infrastructure in its core is based on wireless sensor networks (WSNs) and the internet of things (IoT). WSNs consist of tiny smart devices (Motes) that are restricted in terms of memory, storage, processing capabilities, and sensing and communication ranges. Those limitations pose many security issues where regular cryptography algorithms are not suitable to be used. Besides, such capabilities might be degraded in case cheap sensors are deployed with very large numbers in applications, such as smart cities. One of the major security issues in WSNs that affect the overall operation, up to network interruption, in smart cities is the sinkhole routing attack. The paper has three-fold contributions: (1) it utilizes the concept of clustering for energy saving in WSNs, (2) proposing two light and simple algorithms for intrusion detection and prevention in smart cities—threshold-based intrusion detection system (TBIDS) and multipath-based intrusion detection system (MBIDS), and (3) utilizing the cross-layer technique between the application layer and network layer for the purpose of intrusion detection. The proposed methods are evaluated against recent algorithms—S-LEACH, MS-LEACH, and ABC algorithms.


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