DDoS Flood and Destination Service Changing Sensor

Hsu, Fu-Hau; Lee, Chia-Hao; Wang, Chun Yi; Hung, Rui Yi; Zhuang, YungYu

doi:10.3390/s21061980

Cited by 5 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… A denial-of-service attack aimed at overloading a node. To implement this scenario, a SYN flood attack was used, the victim’s open port was attacked, in this case, Port 22 [ 34 ]. During the attack, the victim node received many connection requests, because of which the message queue overflowed, and the node was blocked, while the network remained available.…”

Section: Resultsmentioning

confidence: 99%

An Adaptive Protection System for Sensor Networks Based on Analysis of Neighboring Nodes

Gamec

Basan

et al. 2021

Sensors

View full text Add to dashboard Cite

Creation and operation of sensor systems is a complex challenge not only for industrial and military purposes but also for consumer services (“smart city”, “smart home”) and other applications such as agriculture (“smart farm”, “smart greenhouse”). The use of such systems gives a positive economic effect and provides additional benefits from various points of view. At the same time, due to a large number of threats and challenges to cyber security, it is necessary to detect attacks on sensor systems in a timely manner. Here we present an anomaly detection method in which sensor nodes observe their neighbors and detect obvious deviations in their behavior. In this way, the community of neighboring nodes works collectively to protect one another. The nodes record only those parameters and attributes that are inherent in any node. Regardless of the node’s functionality, such parameters include the amount of traffic passing through the node, its Central Processing Unit (CPU) load, as well as the presence and number of packets dropped by the node. Our method’s main goal is to implement protection against the active influence of an internal attacker on the whole sensor network. We present the anomaly detection method, a dataset collection strategy, and experimental results that show how different types of attacks can be distinguished in the data produced by the nodes.

show abstract

Section: Resultsmentioning

confidence: 99%