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– AN EFFICIENT SPAM DETECTION TECHNIQUE FOR IOT
DEVICES USING MACHINE LEARNING
ABSTRACT
The Internet of Things (IoT) is a group of millions of devices having sensors and
actuators linked over wired or wireless channel for data transmission. IoT has
grown rapidly over the past decade with more than 25 billion devices are expected
to be connected by 2020. The volume of data released from these devices will
increase many-fold in the years to come. In addition to an increased volume, the
IoT devices produces a large amount of data with a number of different modalities
having varying data quality defined by its speed in terms of time and position
dependency. In such an environment, machine learning algorithms can play an
important role in ensuring security and authorization based on biotechnology,
anomalous detection to improve the usability and security of IoT systems. On the
other hand, attackers often view learning algorithms to exploit the vulnerabilities in
smart IoT-based systems. Motivated from these, in this paper, we propose the
security of the IoT devices by detecting spam using machine learning. To achieve
this objective, Spam Detection in IoT using Machine Learning framework is
proposed. In this framework, five machine learning models are evaluated using
various metrics with a large collection of inputs features sets. Each model
computes a spam score by considering the refined input features. This score depicts
the trustworthiness of IoT device under various parameters. REFIT Smart Home
dataset is used for the validation of proposed technique. The results obtained
proves the effectiveness of the proposed scheme in comparison to the other
existing schemes.
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CONTEXT
Chapter list of content Page no.
ABSTRACT i
LIST OF FIGURES ii
1 INTRODUCTION 1
1.1
PROPOSED SYSTEM 1
2 LITERATURE SURVEY 19
3 SYSTEM REQUIREMENTS 24
3.1 HARDWARE REQUIREMENTS 24
3.2 SOFTWARE REQUIREMENTS 24
3.3 LANGUAGE SPECIFICATIONS 24
3.4 HISTORY OF PYTHON 25
3.5 APPLICATION OF PYTHON 25
3.6 FEATURES OF PYTHON 26
3.7 FEASIBILITY STUDY 26
3.7.1 ECONOMICAL FEASIBILITY 27
3.7.2 TECHNICAL FEASIBILITY 27
3.7.3 SOCIAL FEASIBILITY 28
4 SYSTEM ANALYSIS 29
4.1 PURPOSE 29
4.2 SCOPE 29
4.3 EXISTING SYSTEM 29
4.4 PROPOSED SYETEM 30
5 SYSTEM DESIGN 32
5.1 INPUT DESIGN 32
5.2 OUTPUT DESIGN 32
5.3 DATA FLOW DIAGRAM 33
6 MODULES 39
6.1 LOGIN MODULE 39
6.2 DATA COLLECTION MODULE 39
6.3 PRE-PROCESSING MODULE 40
6.4 TRAIN AND TEST MODULE 40
6.5 DETECTION OF SPAM 41
7 SYSTEM IMPLEMENTATION 42
7.1 SYSTEM ARCHITECTURE 42
8 SYSTEM TESTING 43
8.1 TEST PLAN 43
8.2 VERIFICATION 43
