This document describes a project called Block Hunter that uses federated learning to build a threat hunting framework to automatically detect attacks in blockchain-based industrial internet of things networks. It has 4 team members working under the guidance of Swetha Madam. The existing systems cannot be implemented on cluster-based local outlier factor or federated learning in IIoT networks. The proposed system uses federated learning to frequently update a model for anomaly detection using data from smart factory members, without collecting all local data centrally. This allows detecting anomalies across a blockchain-based IIoT network securely and privately.

1. BLOCK HUNTER
PROJECT TITLE:-

2. 20TQ5A0508
A.Sravan Kumar
| TEAM MEMBERS:-
01
03
20TQ5A0509
C.Rakesh
20TQ5A0504
S.Chandana
02
19TQ1A0576
V.Sai Kumar
Under The Guidance Of:-
Swetha Madam
04

3. | CONTENTS :-
1) Abstract
2) Literature Survey
3) Existing system
4) Proposed System

4. ABSTRACT
1
Nowadays, blockchain-based technologies are being developed in various
industries to improve data security. In the context of the Industrial Internet
of Things, a chain-based network is one of the most notable applications of
blockchain technology. IIoT devices have become increasingly prevalent in
our digital world, especially in support of developing smart factories.
Although blockchain is a powerful tool, it is vulnerable to cyber attacks.
Detecting anomalies in blockchain-based IIoT networks in smart factories is
crucial in protecting networks and systems from unexpected attacks. we use
Federated Learning (FL) to build a threat hunting framework called Block
Hunter to automatically hunt for attacks in blockchain-based IIoT networks.

5. |Literature Survey
A Blockchain-Based Solution for Enhancing Security and Privacy in Smart Factory
AUTHORS:- J. Wan, J. Li, M. Imran, D. Li, and F. e Amin
Through the Industrial Internet of Things (IIoT), a smart factory has
entered the booming period. However, as the number of nodes and
network size become larger, the traditional IIoT architecture can no
longer provide effective support for such enormous system.
Therefore, they introduce the Blockchain architecture, which is an
emerging scheme for constructing the distributed networks, to
reshape the traditional IIoT architecture
2

6. Blockchain Attack Discovery via Anomaly Detection
AUTHORS:- F. Scicchitano, A. Liguori, M. Guarascio, E. Ritacco, and G. Manco,.
In this work, they define an anomaly detection system based on a encoder-decoder
deep learning model, that is trained exploiting information extracted by monitoring
blockchain activities. Experiments on complete historical logs of Ethereum Classic
network prove the capability of the our model to effectively detect the publicly
reported attacks. To the best of their knowledge, their approach was the first one that
provides a comprehensive and feasible solution to monitor the security of blockchain
transactions.

7. ➢ A Blockchain based IOT network security already exists it is a
link-mining tool based on anomaly detection within Blockchain
networks.
➢ It Aim’s at gathering Meta-data in the form of forks (i.e.
divergent paths from the Blockchain protocol) in order to find
the semantic interpretation of anomalous paths/activities,
through mutual information based measure
EXISTING SYSTEM
3

8. DISADVANTAGES OF EXISTING SYSTEM:-
❖ The system is not implemented on
Cluster-Based Local Outlier Factor.
❖ It is not implemented in IIOT
❖ It is not implemented in Federated
Learning

9. ➔ We use FL to update the model frequently and to obtain a
global model for detecting an anomaly. After learning about
each smart factory’s data, devices, and service provider, the
model’s parameters will be sent to the parameter server for
aggregation and to update our general model.
PROPOSED SYSTEM
4

10. ❖ Federation Construction: The subset of smart factory
members, cluster, selected to receive the model locally.
❖ Decentralized Training: When a cluster of smart factories is
selected, it updates its model using its local data.
❖ Model Accumulation: Responsible for accumulating and
merging the data models. Data is not sent and integrated
from the federation to the server individually.
ADVANTAGES OF PROPOSED SYSTEM:-

11. Any Queries ?

12. Thank You !