Project Synopsis
Title: Botnet detection using machine learning
Domain: Machine learning
Contents
Page No
1. Introduction
2. Literature Survey
3. Problem Formulation
4. Objectives
5. Methodology
6. Requirements and Feasibility
7. System Architecture
8. References
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Introduction
(18 Font)
A Botnet is a large collection of compromised
machines, referred to as zombies under a common
Command-and-Control infrastructure (C&C), typically
used for nefarious purposes. Botnets are used in a
variety of online crimes including, and not limited to,
large scale DDoS attacks, Spam, Click Fraud, Extortion,
and Identity theft. The scale and geographical
diversity of the machines enlisted in a Botnet, coupled
with easily available source code, and support from
communities, as well as mercenary Botmasters
providing Botnet services for rent, have resulted in
Botnets becoming a highly sophisticated and effective
tool for committing online crime in recent times .
Botnets with thousands and millions of nodes have
been observed in the wild, with newer ones being
observe every day .
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2. Literature Survey (18 Font)
A detailed statistical analysis of IoT attack literature in
recent years is summarized. The review outlines the
existing proposed contributions, datasets utilized,
network forensic methods utilized, and research focus
of the primary selected studies. But it does not
introduce the specific detection technology and
compare and analyze the detection methods.
DNS-based botnet detection technologies are classified
into five categories in flow-based, anomaly-based, fluxbased, DGA-based, and bot infection-based. Essential
attributes of a smart DNS-based botnet detection
system are proposed. But the survey did not provide
context for the botnet’s construction mechanism.
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3. Problem Formulation
A product development model was used to define the
life
cycle
in
including
concepts,
recruitment,
interaction, motivation, and attack execution (CRIME).
Literature
proposed a fine-grained, hidden Markov
model-based botnet life cycle model, describing the
state transition of botnets from propagation to
extinction and dividing the typical botnet life cycle into
nine
types
of
hidden
states:
infection/initialization/idle/propagation/attack/mainte
nance/offline/isolation/dead. The model used “state”
instead of “stage” to describe the evolution of botnets
and broke the conventional irreversible and abstract
timing relationship. The model could better represent
the migration and changes of botnets.
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4. Objectives
The objectives of the proposed project are as follows:
• A collection of system calls performed by a binary
• Instruction patterns in a binary
• Malwares that attempt to connect to an IP address
• What times the malware is most active during a given
time of a day
• Low/high processor utilization with particular conditions
• File/accessed or modified file sets by the binary
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5. Methodology
The proposed Botnet detection system is implemented using the following steps:
Step 1: The user will log in to the D-App with their wallet and upload the E-waste item
data.
Step 2: E-waste item data pinned to an IPFS service which returns a hash value, wallet
will send the notification and validate the transaction through RPC protocol (Remote
Procedure Call).
Step 3: The mint function in the contract is triggered which creates a new token and it
will store the token Id and hash which points to the item’s metadata.
Step 4: Botnet item is created.
Step 5: The user sells the item to the collector and the collector gives the fund.
Step 6: Finally, API tracks all the data processed.
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6. Requirements and Feasibility
Students have to write brief overview of requirements and feasibility about 4-5 lines.
6.1 Requirements
The proposed project consists of the following requirements:
6.1.1 Hardware requirements
6.1.2 Software requirements
6.1.1 Hardware Requirements
The hardware requirements for the proposed project are depicted in Table 6.1.
Table 6.1: Hardware requirements (Times new roman 10)
Sl. No
Hardware/Equipment
Specification
1.
Graphics Card
Intel 621 Graphics card or 2GB
2.
RAM
4GB or above
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6.1.2 Software Requirements
The software requirements for the proposed project are depicted in Table 6.2.
Table 6.2: Software requirements (Times new roman 10)
Sl. No
Software
Specification
1.
Anaconda
Anaconda 64 bit
2.
Python
Python 3 and above
3.
Framework
Flask
6.2 Feasibility
All the hardware and software components required for implementing the proposed
project is readily available. Hence project can be carried out within given time constraint
and budget.
7. System Architecture
Figure 7.1 shows the architecture of the proposed system.
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Figure 7.1: Architecture of the Botnet (Times new roman 10)
8. References (Times New Roman 18)
[1] Matija Stevanovic and Myrup Pederson. “Machine
Learning for Identifying Botnet Network Traffic.”
Technical Report to the Aalborg University Denmark.
2013.
[2] David Santana, Shan Suthaharan and Somya
Mohanty. “What We Learn from Learning –
Understanding Capabilities and Limitations of
Machine Learning in BotnetAttacks.” 3. May, 2018. , John
rd
Wiley
Sons,
Ltd.,
Edition,
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