Home » A Deep Learning Methodology for  Predicting Cybersecurity Attacks on the  Internet of Things 

A Deep Learning Methodology for  Predicting Cybersecurity Attacks on the  Internet of Things 

by Sulaikha Fiyas

The rapid increase in smart devices and the Internet of Things, which allows sharing and receiving data  with other devices and systems over the Internet, increases cyber security threats. These devices are  becoming prime targets for cyber attacks. Furthermore, detecting these attacks by using traditional  techniques is becoming a challenge. We switched to Deep Learning and Machine Learning techniques to  address these challenges. In short words, we used AI-driven approaches to recover from these  challenges. 


The Internet of Things(IoT), the persistent connection of tangible entities with the Internet constitutes  new cyber security risks and demands monitoring and appraisal. Anticipating the types of attacks and  preventing data from unforeseen attacks are the utmost requirements of cybersecurity. Obstructing data  from being attacked by malware is a crucial step toward cybersecurity. Anyhow, the traditional ways are  ineffective as compared to upcoming research. Much research has been done in the domain of ML and  DL to confront these challenges [1-6]. 

Within the framework of IoT, AI algorithms such as ML and DL techniques are working efficiently to cope  with these obstacles [7-11]. Deep Learning, a subset of AI that can recognize nonlinear patterns more  easily and helps in processing computing machines, is playing a key role in all this research and  overcoming these challenges [12-16].  

The fundamental contributions of our work are as follows: 

● Advocating an AI Model-based approach using DL and ML techniques, along with ensemble  learning classifiers for cyber attack detection in IoT, empowered by SMOTE(Synthetic Minority  over-sampling technique)implementation [17]. 

● Enhancing the confidence and accuracy in detecting cyber security attacks in contrast to  prevailing methods and approaches. 

● Producing more precise results in detection by halting access to unauthorized systems, breaches,  and interruptions. 

● Resolving the class imbalance issue by SMOTE [18] to upgrade model generalization.

● Granting insights into the DL model with ensemble learning to detect and prevent cybersecurity  attacks in IoT. 

The research will contribute to and bring enhancement to the domain of cybersecurity and IoT security  by employing ensemble learning. Moreover, it has the power to defend critical data and forecast  upcoming attacks significantly. 

Materials and Methods: 

This study unveils an automated network detection model for IoT. It accumulates sensor flow data and  utilizes feature engineering techniques such as balancing. In addition, DL models are tested for  evaluating performance [24]. 

Bot-IoT Dataset: 

A novel dataset named the Bot-IoT dataset has been used to accomplish this task. This dataset  constitutes IoT data collected from the Cyber Range Lab of UNSW Canberra, incorporating normal traffic  flows and flows generated by botnets emerging from several types of attacks. 

A pragmatic testbed produced a detailed dataset with feature engineering to enhance the performance  of machine learning models. Features were obtained from IoT services, network structure, and  investigative analysis. Devices include smart fridges and smart doors, each performing specific functions. 

The following are the target categories in the Bot-IoT dataset:  

● Benign category: normal, legitimate IoT network activity without harmful Intent. 

● UDP-focused DDoS attacks: flooding networks with TCP requests, exhibiting them impractical for  authorized users. 

● DDoS HTTP attacks: flooding the network with packets, making the service out of reach. ● DDoS HTTP attacks: flooding net server with HTTP requests leading to service disruption. ● TCP DoS attacks: leveraging TCP stack vulnerabilities to deplet devices. 

● UDP DoS attacks: flooding targets with numerous packets causing service outages. 

● HTTP-based DoS attacks: Overburdening the web servers with excessive requests causes underperformance. 

● Keylogging: privately monitoring and recording keystrokes on compromised devices for harmful  aims. 

● Capture of data: unsanctioned capture of information from compromised IoT devices. 

Table 1: Bot-IoT dataset.

Type Target Count
BENIGN Benign 9543
DDoS TCP Attack 19,547,603
DDoS UDP Attack 18,965,106
DDoS HTTP Attack 19,771
DoS TCP Attack 12,315,997
DoS UDP Attack 20,659,491
DoS HTTP Attack 20,659,491
Keylogging Keylogging 1469
Data theft Data theft 118
Total – 73,370,443

The Proposed Model 

Data pre-processing: 

The preprocessing of data is a significant part of developing the model. In the course of preprocessing,  we dealt with missing values and cleaned the data through filtration, scaling, normalizing, and removing  duplicate values. We used Python services while dealing with preprocessing [25-27]. 

Feature Engineering Techniques: 

1. Correlation Coefficient: 

The correlation coefficient quantifies the relationship between two factors in the dataset. In this context,  the correlation coefficient highly helped in recognizing and analyzing the trends and patterns in the BoT IoT dataset. This dataset constitutes information about IoT devices that have been infiltrated by botnets.  The correlation coefficient in this dataset has been determined for variables such as device type,  network traffic pattern, communication protocols, and other connected factors. Once the variable has  been selected, the correlation coefficient is measured by using certain statistical techniques that give  insight into the data. A zero or near zero value indicates no relation, a high value indicates a direct  relation, while a negative indicates an inversely proportional relation.

Figure 1: A correlation coefficient between features of the BoT-IoT dataset. 

2. Feature Importance using Random Forest: 

Feature significance analysis using Random Forest is an efficient technique that is significant for the BoT IoT dataset. This analysis discloses which factor has the greatest impression on the dependent variable.  In this regard, the Bot-Iot dataset was first divided into training and testing datasets. The training dataset  is for training the Random Forest model, while the testing subset is used for making predictions and  evaluating model performance. Feature importance is measured using several techniques such as Gini  impurity, entropy, and permutation importance. These techniques ascertain how each attribute is  involved in reducing impurity in decision trees of random forests. The results can be represented using a  bar chart and a pie chart. The results have been visualized in Figure 2. Applying feature importance  analysis by using random forest certainly allows the authentication of key attributes directing the  behaviour of IoT devices.  

Many insignificant features such as pkSeqID’, ‘proto’, ‘saddr’, ‘sport’, ‘daddr’, ‘dport’, and ‘category have  been dropped after applying the feature importance technique using Random Forest.

Figure 2: A feature importance analysis using Random Forest. 

3. SMOTE Approach: 

An elevated strategy for dealing with imbalanced data is the SMOTE approach, as shown in Figure 3. The  SMOTE algorithm produces new samples by employing linear interpolation between the selection of the  number of samples and those located in their neighbors [28,29]. 

To improve the classification impression of unbalanced data, a selected number of false samples has  been produced as shown in Figure 4. This consequently transmutes an unbalanced data ratio into a  balanced one. 

Figure 3: The Attack class Distribution. 

Ensemble Learning: 

1. Extra Trees Classifiers: 

The Extra Tree classifier is an alternative to the random forest classifier. This Algorithm comprises more  randomness in decision trees than in Random Forests [30]. It deploys ensemble learning in base trees to  make decisions [31]. This technique is more useful when dealing with majority voting. In our specific  context, the Extra Trees Classifier is being used to detect several types of attacks. This also aids in  preventing the model from overfitting and improves the accuracy [32].

Figure 4: Attack class distributions after oversampling the BoT-IoT dataset using the SMOTE algorithm. 

2. Histogram-based Gradient Boosting classifier: 

This uses gradient-boosting, continuously improved training of an ensemble of weak learners [33]. This  classifier uses a histogram to improve accuracy and predictive efficiency [34]. 

A histogram provides statistical information in the context of information distribution in each bin [35].  These measures enable effective calculation during training and also lower the loss rate, causing loss  reduction. The learning rate parameter also assigns values to weak learners, therefore accomplishing the  balance between model complexities and the ratio of convergence [36]. 

3. Adaptive Boosting classifier: 

An Adaptive Boosting classifier(AdaBost) combines weak learners iteratively to form a stout classifier [37]. Decision trees act as weak learners, and their predictions weigh according to predictions. It  provides more benefits to our computation such as being less prone to overfitting, and simple to  implement, leading to a wealth of resources and expertise available to users. These all lead to more  admiration of this algorithm in machine learning.  

4. LGBM classifier: 

The Light Gradient Boosting Machine(LGBM) is the most effective algorithm under gradient boosting  framework [38]. It uses iterations to train weak learners while operating inside a gradient gradient boosting framework. Its renowned feature is its unique optimization that enhances its efficiency and  improves accuracy. 

5. CatBoosting classifier: 

Catboosting is a very powerful technique of machine learning that is significant for classification. It falls  under the category of gradient boosting structure that elevates the efficiency of the model [39]. It  outperforms categorical data such as variables with high dimensions and cardinalities. It preserves the  constancy of the model by randomly shuffling the order of each category during training, thereby  uplifting the accuracy [40].  

Evaluation Metrics: 

The competency of the DL model is determined by using precision, recall, F1 score, computation time,  and accuracy [41,42]. These matrices are computed with the help of TPR, TNR, FPR, and FPR.

True positive rate (TPR): ratio of observed positives to expected positives 

False positive rate (FPR): ratio of values that are truly negative but are expected to be positive ● False negative rate (FNR): ratio values that are positive but observed to be negative ● True negative rate (TNR): ratio values that are negative but foreseen to become negative 

Precision: system’s capacity to accurately detect the presence of an attack; it shows the  relationship between accurately predicted attacks and actual results: 

Precision = TPR/(TPR + FPR) 

Recall: the system’s ability to correctly notice a botnet attack whenever occurs on a network: Recall = TPR/(TPR + FPR) 

Accuracy: the system’s ability to efficiently classify attack and non-attack packets Accuracy = (TNR + TPR)/(TPR + FNR + FPR + TNR) 

F1-score: average of recall and precision: 

F1-Score = 2 × (Recall × Precision)/(Recall + Precision) 

Time complexity: how quickly or slowly an algorithm performs in the same relation to the  amount of data. 


Experimental Settings: 

For this research, we used Python, TensorFlow, and Keras on GoogleColab GPU. Preprocessed attributes  in the BoT_IoT dataset were used in the training procedure. We divided data into 70% training, 20%  validation, and 10% testing. Elevation matrices, which have been discussed above, have been used for  the evaluation of the model. 

Experimental Results: 

We performed 10 ML models for malware detection, including a single classifier, ensemble learning, and  DL techniques. We contrasted the results with and without the SMOTE algorithm concisely used for  managing imbalanced data. 

Table 2: Performance results for detecting IoT network attacks without using the SMOTE algorithm.

Metric Accuracy Precision Recall F1 score CPU time Model size(MB)
Random  Forest 0.9518 0.9538 0.9284 0.9403 21.6 s 23.6
Extra Trees 0.9674 0.9652 0.9517 0.9582 47.6 s 598.7
KNN 0.9083 0.9036 0.8869 0.8947 3.29 s 13.6
SVM 0.6121 0.6280 0.3598 0.3695 21 min 50 s 12.2
HistGBoost 0.9560 0.7488 0.7332 0.7321 13.4 s 1.2
AdaBoost 0.1211 0.4552 0.3482 0.0826 1 min 19 s 0.31
LGBM 0.9323 0.4665 0.4739 0.4690 36.1 s 1.8
CatBoost 0.9819 0.9686 0.9608 0.9646 2 min 55 s 3.5
XGBoost 0.9852 0.9806 0.9654 0.9727 2 min 43 s 1.1
MLP 0.7539 0.3031 0.2942 0.2850 31.5 s 0.005
ANN 0.8308 0.3308 0.4789 0.3701 13 min 48 s 0.027
LSTM 0.7701 0.4887 0.3476 0.3682 10 min 10 s 7.7
GRU 0.8536 0.6058 0.4517 0.4902 11 min 1 s 7.7
RNN 0.8682 0.9189 0.7631 0.8013 10 min 50 s 1.6
Bagging 0.9398 0.9324 0.9160 0.9238 2 min 54 s 240.5 

Table 3: Performance results for detecting IoT network attacks using the SMOTE algorithm from the BoT-IoT dataset.

Metric Accuracy Precision Recall F1 score CPU time Model  size(MB)
CatBoost 0.97661 0.91249 0.9815 0.94369 7 min 43 s 3.48
XGBoost 0.97986 0.94868 0.98084 0.96383 7 min 53 s 1.22
ANN 0.76594 0.61794 0.89682 0.63602 31 min 41 s 0.03
MLP 0.53336 0.31119 0.63571 0.32423 4 min 47 s 0.02
LSTM 0.83418 0.75511 0.92699 0.76773 30 min 6 s 7.69
GRU 0.87806 0.78463 0.93476 0.83175 29 min 50 s 7.69
RNN 0.87147 0.77572 0.94066 0.8257 27 min 3 s 1.62
Bagging 0.94099 0.91357 0.93127 0.92205 9 min 31 s 350.73
Random Forest 0.9425 0.90961 0.9635 0.9304 1 min 7 s 29.60
Extra Trees 0.90922 0.88756 0.8952 0.8906 3.43 s 35.19
KNN 0.90922 0.88756 0.8952 0.8906 3.43 s 35.19
SVM 0.59398 0.4853 0.63258 0.48259 1 h 18 min  19 s25.34
HistGboost 0.97437 0.97758 0.97437 0.97511 47.6 s 1.90
AdaBoost 0.43068 0.32098 0.34041 0.25093 3 min 55 s 0.31 
LGBM 0.98242 0.96029 0.98055 0.96986 4 min 5 s 11.05

Experiments without Using the SMOTE Algorithm: 

The DL models show different results without SMOTE algorithm. Random Forest, KNN, and Extratrees  exhibited fierce accuracy while SVM AdaBoosT accomplished lower accuracy specifically in imbalance  data. Also, the model size varies, with ExtraTrees having the largest model. Overall RAndomForest and  ExtraTrees outperformed without the SMOTE algorithm. 

Figure 5 represents the examination of all models based on accuracy, precision, and F1-score.

Figure 6 shows the performance of DL models without using the SMOTE algorithm. MLP indicates a  lower performance with an accuracy of 0.75 while, ANN performed better with improved accuracy and  precision but failed while balancing data. 

Figure 5: The evaluation results of the proposed ML models on the BoT-IoT dataset without using the SMOTE algorithm. 

Figure 6: The proposed deep learning models’ evaluation results without using the SMOTE algorithm. 

Proceeding to LSTM and GURU, both show equivalent results. LSTM achieves 0.78 accuracy while GURU  achieves 0.85. Both accomplished precision better than ANN and MLP.

On the other hand, the RNN model outperforms all DL models and significantly achieves the highest  accuracy. Nevertheless, it consumes the highest CPU time and largest model size as compared to others. 

Experiments Using the SMOTE Algorithm: 

Table 3 represents the performance results using the SMOTE algorithm. It contrasts the ML model  performance based on precision and recall. Accuracy and F1 score for calculating impact for detecting  attacks are shown in Figure 7. 

Figure 7: The proposed machine learning models’ evaluation results using the SMOTE algorithm. 

CatBoost achieved 97.661% accuracy with a high precision of 0.91243 and a recall of 0.9815. 

XGBoost accomplished 97.986% and precision much better than CatBoost 0.94886 and comparable recall  of 0.98084. MLP model underperformed all others while ANN performed better than MLP but still trailed  behind CatBoost and XGBoost. 

The performance of LSTM, GRU, and RNN was seen as better than that of MLP and ANN architects, as  shown in Figure 8. LSTM achieved an accuracy of 0.83418. GRU and RNN showed comparable accuracies  of 0.878 and 0.87 respectively. However, all the models took higher CPU time and large model sizes.

Figure 8: The proposed deep learning models’ evaluation results using the SMOTE algorithm. 

CatBoost and XGBoost models outstripped all other models with high prescio, recall, and F1 scores. They  consumed less training time and small model sizes. These traits lead them towards suitability for  cybersecurity attack detection in IoT devices and networks. 


Table 2 and Table 3 examine the classifier’s performance on the Bot-IoT dataset with and without the  SMOTE algorithm. Figure 9 shows the ensemble learning with and without the SMOTE algorithm.  Distinctively XGBoost and CatBoost are performing better than all other models and techniques.

Figure 9: Comparison of the best ensemble learning models’ results with and without using the SMOTE algorithm. Figure 9 compares the performance of the DL model with and without the SMOTE algorithm. 

XGBoost and CatBoost sustained higher accuracy, precision, recall, and F1 score. MLP, ANN, GRU, and  LSTM didn’t perform well and showed lesser accuracy in handling imbalance data even with the SMOTE  algorithm. 

Figure 10: Comparison of deep learning models’ performance with and without using the SMOTE algorithm.

Classifiers with lower accuracies struggled hard to illustrate better performance. Among all, SVM with  the least accuracy utilized comparatively greater CPU time showing its inefficiency in dealing with  imbalanced data. 

Figure 11: Comparison of single-classifier models’ performance with and without using the SMOTE algorithm. 

Recent research has utilized the DL models to enhance the efficiency level, but they don’t completely  tackle the security needs. 

Tale 4 shows and compares several cyber security solutions based on study year, dataset, methodology,  algorithms, and accuracy results. 

Table 4: Several cyber security solutions based on study year, dataset, methodology, algorithms, and accuracy results.

Ref. Dataset Methodology Accuracy(%)
Mendonça et al. [19] DS2OS, CICIDS2017 Deep learning 98
Popoola et al. [20] BoT-IoT LAE for dimensionality  reduction and BLSTM  classifier 91.89
Alharbi et al. [21] N-BaIoT A Local–Global best Bat  Algorithm for Neural  Networks 90 
Saharkhizan et al. [22] Modbus/TCP network  traffic LSTM and Ensemble  learning98.99
Pokhrel et al. [23] BoT-IoT Deep learning 87.4
Proposed BoT-IoT CatBoosting XGBoosting 98.19 98.52


This research reveals a better and smart way for detecting malware attacks in IoT networks. It integrates  DL models with other techniques to avoid overfitting. CatBoost and XGBoost are best at spotting attacks  on IoT networks. Future work will include different methods and techniques for detecting malware  attacks.


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Article By: Sulaikha Fiyas

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