Cognitive Lightweight Logistic Regression-Based IDS for IoT-Enabled FANET to Detect Cyberattacks

Abstract

In recent few years, flying ad hoc networks are utilized more for interconnectivity. In the topological scenario of FANETs, IoT nodes are available on ground where UAVs collect information. Due to high mobility patterns of UAVs cause disruption where intruders easily deploy cyberattacks like DoS/DDoS. Flying ad hoc networks use to have UAVs, satellite, and base station in the physical structure. IoT-based UAV networks are having many applications which include agriculture, rescue operations, tracking, and surveillance. However, DoS/DDoS attacks disturb the behaviour of entire FANET which lead to unbalance energy, end-to-end delay, and packet loss. This research study is focused about the detail study of machine learning-based IDS. Also, cognitive lightweight-LR approach is modeled using UNSW-NB 15 dataset. IoT-based UAV network is introduced using machine learning to detect possible security attacks. The queuing and data traffic model is utilized to implement DT, RF, XGBoost, AdaBoost, Bagging and logistic regression in the environment of IoT-based UAV network. Logistic regression is the proposed approach which is used to estimate statistical possibility. Overall, experimentation is based on binomial distribution. There exists linear association approach in logistic regression. In comparison with other techniques, logistic regression behaviour is lightweight and low cost. The simulation results presents logistic regression better results in contrast with other techniques. Also, high accuracy is balanced well in optimal way.

Improving Intrusion Detection in UAV Communication Using an LSTM-SMOTE Classification Method

“…The authors of [11] mentioned the topological situation of Flying Ad-Hoc Networks (FANETs), where IoT nodes are accessible on the ground and UAVs gather data. UAVs' high mobility patterns lead to disruptions where intruders may quickly launch Denial of Service (DoS) or Distributed Denial of Service (DDoS) assaults.…”

DDoS Attack and Detection Methods in Internet-Enabled Networks: Concept, Research Perspectives, and Challenges

“…The internet of things (IoT) has recently emerged as one of the enabling technologies that have been implemented in a variety of applications [1,2]. In the same vein, innovative internet-enabled technologies such as the Internet of Flying Things (IoFT) [3], the Internet of Drones (IoD) [4], the Flying Ad-hoc Network (FANET), or drone networks [5] are being implemented to provide decentralized and scalable solutions in these applications. Recently, the IoD has emerged with a new paradigm, where a set of flying vehicles (unmanned aerial vehicles (UAVs)) communicate among themselves and with a ground control station via the internet to execute a range of tasks in various ways [6,7].…”

“…Coupled with the fact that most IoT devices lack access control, have insecure default passwords, and use unprotected credentials, these technologies are gradually becoming desirable targets for cyberattacks. For instance, the UAVs and some other entities involved in the IoD, FANET, or IoFT are vulnerable to jamming, command injection, and Global Positioning System (GPS) spoofing attacks [5][6][7][8][9][10][11][12][13][14]. With the creation of low-cost software-defined radios (SDRs) [15,16], the potential for GPS spoofing has substantially increased.…”