RAD-EI: A routing attack detection scheme for edge-based Internet of Things environment

2019 ◽  
Vol 32 (15) ◽  
pp. e4024 ◽  
Author(s):  
Mohammad Wazid ◽  
Poonam Reshma Dsouza ◽  
Ashok Kumar Das ◽  
Vivekananda Bhat K ◽  
Neeraj Kumar ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Attack Detection ◽  
Detection Scheme ◽  
Internet Of Things Environment ◽  
Routing Attack ◽  
Edge Based

scholarly journals Designing Efficient Sinkhole Attack Detection Mechanism in Edge-Based IoT Deployment

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1300
Author(s):  
Sumit Pundir ◽  
Mohammad Wazid ◽  
Devesh Pratap Singh ◽  
Ashok Kumar Das ◽  
Joel J. P. C. J. P. C. Rodrigues ◽  
...  
Keyword(s):  
Network Performance ◽  
False Positive Rate ◽  
Security Analysis ◽  
Base Station ◽  
Attack Detection ◽  
Delivery Ratio ◽  
Performance Parameters ◽  
Detection Mechanism ◽  
Detection Scheme ◽  
Edge Based

The sinkhole attack in an edge-based Internet of Things (IoT) environment (EIoT) can devastate and ruin the whole functioning of the communication. The sinkhole attacker nodes ( S H A s) have some properties (for example, they first attract the other normal nodes for the shortest path to the destination and when normal nodes initiate the process of sending their packets through that path (i.e., via S H A ), the attacker nodes start disrupting the traffic flow of the network). In the presence of S H A s, the destination (for example, sink node i.e., gateway/base station) does not receive the required information or it may receive partial or modified information. This results in reduction of the network performance and degradation in efficiency and reliability of the communication. In the presence of such an attack, the throughput decreases, end-to-end delay increases and packet delivery ratio decreases. Moreover, it may harm other network performance parameters. Hence, it becomes extremely essential to provide an effective and competent scheme to mitigate this attack in EIoT. In this paper, an intrusion detection scheme to protect EIoT environment against sinkhole attack is proposed, which is named as SAD-EIoT. In SAD-EIoT, the resource rich edge nodes (edge servers) perform the detection of different types of sinkhole attacker nodes with the help of exchanging messages. The practical demonstration of SAD-EIoT is also provided using the well known NS2 simulator to compute the various performance parameters. Additionally, the security analysis of SAD-EIoT is conducted to prove its resiliency against various types of S H A s. SAD-EIoT achieves around 95.83 % detection rate and 1.03 % false positive rate, which are considerably better than other related existing schemes. Apart from those, SAD-EIoT is proficient with respect to computation and communication costs. Eventually, SAD-EIoT will be a suitable match for those applications which can be used in critical and sensitive operations (for example, surveillance, security and monitoring systems).

scholarly journals IFACNN: efficient DDoS attack detection based on improved firefly algorithm to optimize convolutional neural networks

2021 ◽  
Vol 19 (2) ◽  
pp. 1280-1303
Author(s):  
Jiushuang Wang ◽  
◽  
Ying Liu ◽  
Huifen Feng
Keyword(s):  
Internet Of Things ◽  
Firefly Algorithm ◽  
Denial Of Service ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Detection Scheme ◽  
Ddos Attack ◽  
Improved Firefly Algorithm ◽  
Iot Devices ◽  
Ddos Attack Detection

<abstract><p>Network security has become considerably essential because of the expansion of internet of things (IoT) devices. One of the greatest hazards of today's networks is distributed denial of service (DDoS) attacks, which could destroy critical network services. Recent numerous IoT devices are unsuspectingly attacked by DDoS. To securely manage IoT equipment, researchers have introduced software-defined networks (SDN). Therefore, we propose a DDoS attack detection scheme to secure the real-time in the software-defined the internet of things (SD-IoT) environment. In this article, we utilize improved firefly algorithm to optimize the convolutional neural network (CNN), to provide detection for DDoS attacks in our proposed SD-IoT framework. Our results demonstrate that our scheme can achieve higher than 99% DDoS behavior and benign traffic detection accuracy.</p></abstract>

scholarly journals A context-aware information-based clone node attack detection scheme in Internet of Things

2021 ◽  
pp. 103271
Author(s):  
Khizar Hameed ◽  
Saurabh Garg ◽  
Muhammad Bilal Amin ◽  
Byeong Kang ◽  
Abid Khan
Keyword(s):  
Internet Of Things ◽  
Attack Detection ◽  
Context Aware ◽  
Detection Scheme

Comment on “DIO Suppression Attack Against Routing in the Internet of Things”

2019 ◽  
Author(s):  
Abhishek Verma ◽  
Virender Ranga
Keyword(s):  
Internet Of Things ◽  
Network Topology ◽  
Impact Analysis ◽  
The Internet ◽  
Routing Performance ◽  
Routing Attack ◽  
The Internet Of Things ◽  
Specific Reason

<div>We have thoroughly studied the paper of Perazzo et al., which presents a routing attack named DIO suppression attack with its impact analysis. However, the considered simulation grid of size 20mx20m does not correspond to the results presented in their paper. We believe that the incorrect simulation detail needs to be rectified further for the scientific correctness of the results. In this comment, it is shown that the suppression attack on such small sized network topology does not have any major impact on routing performance, and specific reason is discussed for such behavior.</div>

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