View source for 20240039938. IOT Blockchain DDOS Detection and Countermeasures simplified abstract (Telefonaktiebolaget LM Ericsson (publ))

=IOT Blockchain DDOS Detection and Countermeasures=


==Organization Name==

[[:Category:Telefonaktiebolaget LM Ericsson (publ)|Telefonaktiebolaget LM Ericsson (publ)]]


[[Category:Telefonaktiebolaget LM Ericsson (publ)]]

==Inventor(s)==

[[:Category:Rani Yadav-ranjan of Santa Clara CA (US)|Rani Yadav-ranjan of Santa Clara CA (US)]][[Category:Rani Yadav-ranjan of Santa Clara CA (US)]]

[[:Category:Arthur R. Brisebois of Westport Point MA (US)|Arthur R. Brisebois of Westport Point MA (US)]][[Category:Arthur R. Brisebois of Westport Point MA (US)]]

[[:Category:Serene Banerjee of Chennai (IN)|Serene Banerjee of Chennai (IN)]][[Category:Serene Banerjee of Chennai (IN)]]

==IOT Blockchain DDOS Detection and Countermeasures - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240039938 titled 'IOT Blockchain DDOS Detection and Countermeasures

==Simplified Explanation==

The abstract of the patent application describes a method for detecting and addressing distributed denial of service (DDoS) attacks launched using Internet of Things (IoT) devices. This method involves decentralized computing, potentially combined with blockchain or other decentralized ledger technology. The process includes identifying anomalies by comparing traffic data against baseline traffic data, reporting anomalies as potential attacks, receiving confirmation that the anomalies are indeed attacks, and implementing a set of response measures. These response measures involve allocating a dedicated frequency channel to the attack traffic and assigning the devices associated with the attack to that channel, while assigning other user devices to different frequency channels. Records of the attack and associated devices are recorded and propagated across nodes, allowing each node to respond appropriately even as the attacking device moves from its original location. Machine learning can be used to improve the analysis of key performance indicators to determine which indicators are most predictive of DDoS attacks.

* Method for detecting and addressing DDoS attacks launched using IoT devices
* Utilizes decentralized computing and potentially blockchain or other decentralized ledger technology
* Compares traffic data against baseline data to identify anomalies
* Reports anomalies as potential attacks and receives confirmation
* Implements a set of response measures, including allocating a dedicated frequency channel to attack traffic and assigning attacking devices to that channel
* Records of the attack and associated devices are recorded and propagated across nodes
* Machine learning can be used to improve the analysis of key performance indicators for predicting DDoS attacks

Potential Applications:
- Network security and protection against DDoS attacks in IoT environments
- Enhancing the resilience and reliability of IoT networks

Problems Solved:
- Detecting and addressing DDoS attacks launched using IoT devices
- Ensuring the security and stability of IoT networks

Benefits:
- Improved detection and response to DDoS attacks in IoT environments
- Decentralized approach allows for effective response even as attacking devices move
- Machine learning enhances the accuracy of attack prediction and response


==Original Abstract Submitted==

distributed denial of service (ddos) attacks launched using internet of things (iot) devices may be detected and addressed using decentralized computing, potentially in combination with blockchain or other decentralized ledger technology. one method of doing this may include identifying an anomaly by comparing traffic data against baseline traffic data, reporting an anomaly as a potential attack, receiving an indication that the anomaly is an attack, and then performing a set of response measures. the set of response measures may comprise allocating a dedicated frequency channel to traffic associated with the attack and assigning devices associated with the attack to that channel, while assigning other user devices to different frequency channels. records of the attack and associated devices may be recorded and propagated across nodes, thereby enabling each node to respond appropriately even as the device moves from its original location. machine learning can be implemented to improve the analysis of key performance indicators to determine which indicators are most predictive of ddos attacks.



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