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A. Dataset
The dataset [32] used in this research study is comprised
of two types of attacks, i.e., TCPSYN and UDP flood attacks,
and the attacks are directed towards the server attached to the
backbone network of the campus. The router is attached to
a blockchain-based network. There are three computers used
for attacking the network, and the traffic is captured using the
hping3 software. The attack packets contain spoofed source IP
addresses and are generated randomly that appear to the router
as if the traffic is coming from various sources. In this study,
we investigate the behavior of such attacks at the router and the
peer-to-peer level. The traffic is captured from the router and
peer-to-peer network by mirroring the traffic to the computer
for storage. The saved traffic is used for further analysis and
evaluation of intrusion detection systems.B. EVALUATION CRITERIA
1) Accuracy: Accuracy is a widely used criterion for the evaluation of machine learning algorithms. Our dataset is
imbalanced with more Benign traffic compared to TCPSYNC and UDPFLOOD attacks. Therefore, we will be looking into
some other popular criteria to assess the machine learning model¡¯s performance. The formula for Accuracy is given
below. Accuracy = (TP + TN)=(TP + FN + FP + TN)
2) Precision: It is defined as the ratio of correctly classified items to actually classified items.
Precision = TP=(TP + FP)
3) F1-Score: The F1-Score is also known F-Measure that calculates the harmonic mean of Precision (P) and Recall(R) to
find the score. The maximum value for F1-score is 1 known as Dice similarity coefficient. The formula for F1-Score is given
below.
F1 = 2 (P R=P + R)
Where P and R represent Precision and Recall, respectively.
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IEEE/ICACT20220078 Slide.09
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