Résumé:
Human kind is an evolving creature that has always looked to make his
life easier, that led him to create modern civilization. Technology is a non-stop
arrow that keeps moving forward towards a bright future, brilliant innovations
and new challenges. Wireless sensor networks (WSN) is one of the leading topics
in our modern society, it’s the key to achieve the dream of global smart cities
with IOT (Internet Of Things ) devices and it’s the key for many exciting
domains like remote medicine, smart agronomy, etc. However, WSNs are
exposed to cyber threats every instant, whether they are intentionally by an
adversary or because of poor system management. Thus, security in WSN is very
critical and challenging at the same time due to the constraints of sensors like the
concise abilities such as limited memory, energy consumption constraint.
Therefore, any security measurements must take into consideration these factors
in order to grant the network the fullest performance that it should get without
any delay or packet loss or any sort of abnormal functioning.
In this thesis, an Intrusion detection system (IDS) engine is proposed
based on machine learning and deep learning techniques combined with features
engineering to obtain the highest results for good packet classification.
KDDCUP99 and NSL-KDD are the datasets used to test the model, the data is
preprocessed and then passed as input to a feature selection algorithm XGBoost
that selects columns based on features scores .Since WSNs computation power is
limited, binary classification is used, to classify normal from abnormal traffic
using a Deep Neural network of four layers, input layer, two hidden layers and
the output layer.
The results obtained during this study are accurate and precise compared
to what researchers have accomplished in their published papers. For KDDCUP
precision and accuracy scores are 99.86%, for NSL-KDD 83.21% precision score
and 76.21% accuracy score.
