Clustering based Rewarding Algorithm to Detect Adversaries in Federated Machine Learning based IoT Environment

@article{Yadav2021ClusteringBR,
  title={Clustering based Rewarding Algorithm to Detect Adversaries in Federated Machine Learning based IoT Environment},
  author={Krishna Yadav and B. Gupta},
  journal={2021 IEEE International Conference on Consumer Electronics (ICCE)},
  year={2021},
  pages={1-6}
}
  • Krishna Yadav, B. Gupta
  • Published 2021
  • Computer Science
  • 2021 IEEE International Conference on Consumer Electronics (ICCE)
In recent times, federated machine learning has been very useful in building the intelligent intrusion detection system for IoT devices. As IoT devices are equipped with a security architecture vulnerable to various attacks, these security loopholes may bring a risk during federated training of decentralized IoT devices. Adversaries can take control over these IoT devices and inject false gradients to degrade the global model performance. In this paper, we have proposed an approach that detects… Expand

Figures and Tables from this paper

References

SHOWING 1-10 OF 20 REFERENCES
Mitigating Sybils in Federated Learning Poisoning
TLDR
FoolsGold is described, a novel defense to this problem that identifies poisoning sybils based on the diversity of client updates in the distributed learning process that exceeds the capabilities of existing state of the art approaches to countering sybil-based label-flipping and backdoor poisoning attacks. Expand
Federated Machine Learning: Concept and Applications
TLDR
This work proposes building data networks among organizations based on federated mechanisms as an effective solution to allow knowledge to be shared without compromising user privacy. Expand
ANN Based Scheme to Predict Number of Zombies in a DDoS Attack
TLDR
ANN is employed to estimate number of zombies involved in a DDoS attack and hence solves the problem of low detection precision and weak detection stability of ANN which occurs when used for low frequent attack estimation. Expand
SCEF: A Model for Prevention of DDoS Attacks From the Cloud
Distributed denial of service (DDoS) attacks are some of the biggest threats to network performance and security today. With the advent of cloud computing, these attacks can be performed remotely onExpand
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent
TLDR
Krum is proposed, an aggregation rule that satisfies the resilience property of the aggregation rule capturing the basic requirements to guarantee convergence despite f Byzantine workers, which is argued to be the first provably Byzantine-resilient algorithm for distributed SGD. Expand
Survey of intrusion detection systems: techniques, datasets and challenges
TLDR
A taxonomy of contemporary IDS is presented, a comprehensive review of notable recent works, and an overview of the datasets commonly used for evaluation purposes are presented, and evasion techniques used by attackers to avoid detection are presented. Expand
IoT-Based Big Data Secure Management in the Fog Over a 6G Wireless Network
TLDR
The major purpose of this work is to create a novel and secure cache decision system (CDS) in a wireless network that operates over an SB, which will offer the users safer and efficient environment for browsing the Internet, sharing and managing large-scale data in the fog. Expand
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning
TLDR
Biscotti is a fully decentralized peer to peer (P2P) approach to multi-party ML, which uses blockchain and cryptographic primitives to coordinate a privacy-preserving ML process between peering clients and is able to protect the privacy of an individual client's update and the performance of the global model at scale. Expand
Security in Internet of Things: issues, challenges, taxonomy, and architecture
TLDR
This paper discusses various research challenges that still exist in the literature, which provides better understanding of the problem, current solution space, and future research directions to defend IoT against different attacks. Expand
Can't You Hear Me Knocking: Identification of User Actions on Android Apps via Traffic Analysis
TLDR
This paper investigates to which extent it is feasible to identify the specific actions that a user is doing on mobile apps, by eavesdropping their encrypted network traffic, and designs a system that achieves this goal by using advanced machine learning techniques. Expand
...
1
2
...