IoTBoT-IDS: A novel statistical learning-enabled botnet detection framework for protecting networks of smart cities

Ashraf, Javed; Keshk, Marwa; Moustafa, Nour; Abdel‐Basset, Mohamed; Khurshid, Hasnat; Bakhshi, Asim Dilawer; Mostafa, Reham R.

doi:10.1016/j.scs.2021.103041

Cited by 101 publications

(53 citation statements)

References 36 publications

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“…of Classes), the proportion of classification or detection accuracy (ACC%), the proportion of positive predictive value (PPV%), and the proportion of true positive rate (TPR%). Also, nine intelligent IoT-IDS-systems are deemed in this assessment as engaging diverse supervised ML systems containing: Extremely Randomized Trees (XRT) Classifier [ 35 ], Statistical Learning (STL) Classifier [ 36 ], eXtreme Gradient Boosting (XGB) Classifier [ 37 , 40 ], Hybrid ML Scheme combining decision trees, random forests, and Naïve bays algorithms (HYB) Classifier [ 38 ], shallow convolutional neural networks (S-CNN) Classifier [ 39 , 60 ], Classification And Regression Trees (CART) Classifier [ 41 ], k-nearest neighbor (kNN) Classifier, and our best system employing ensemble boosted trees (EBT) Classifier. According to the information provided in the table, it can be clearly inferred that our model is prominent as it recorded the best performance results among all other schemes.…”

Section: Resultsmentioning

confidence: 99%

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Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

Al‐Haija

Badawi²

2021

Sensors

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Network Intrusion Detection Systems (NIDSs) are indispensable defensive tools against various cyberattacks. Lightweight, multipurpose, and anomaly-based detection NIDSs employ several methods to build profiles for normal and malicious behaviors. In this paper, we design, implement, and evaluate the performance of machine-learning-based NIDS in IoT networks. Specifically, we study six supervised learning methods that belong to three different classes: (1) ensemble methods, (2) neural network methods, and (3) kernel methods. To evaluate the developed NIDSs, we use the distilled-Kitsune-2018 and NSL-KDD datasets, both consisting of a contemporary real-world IoT network traffic subjected to different network attacks. Standard performance evaluation metrics from the machine-learning literature are used to evaluate the identification accuracy, error rates, and inference speed. Our empirical analysis indicates that ensemble methods provide better accuracy and lower error rates compared with neural network and kernel methods. On the other hand, neural network methods provide the highest inference speed which proves their suitability for high-bandwidth networks. We also provide a comparison with state-of-the-art solutions and show that our best results are better than any prior art by 1~20%.

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Section: Resultsmentioning

confidence: 99%

“…A recent and relevant study to our work was proposed in [ 36 ]. The authors presented a statistical-analysis-based NIDS that employs Beta Mixture Model (BMM) and a Corrs entropy model.…”

Section: Related Workmentioning

confidence: 99%

Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

Al‐Haija

Badawi²

2021

Sensors

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“…If there is no neighbor to an individual, then the solution is updated using equation (17). The Levy flight is calculated using (18). Here b and r1 are constant values.…”

Section: Deep Neural Network (Trained Using Enhancedmentioning

confidence: 99%

“…Dempster Shafer's combination rule is used to combine the outputs of both models. We have evaluated our work against CICIDS 2017, CICIDS 2018 [10] and TON IoT datasets [11][12][13][14][15][16][17][18].…”

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confidence: 99%

Deep Learning Based Attack Detection in IIoT using Two-Level Intrusion Detection System

Raja

Karthikeyan

Abilash

et al. 2021

Preprint

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The Industrial Internet of Things (IIoT), also known as Industry 4.0, has brought a revolution in the production and manufacturing sectors as it assists in the automation of production management and reduces the manual effort needed in auditing and managing the pieces of machinery. IoT-enabled industries, in general, use sensors, smart meters, and actuators. Most of the time, the data held by these devices is surpassingly sensitive and private. This information might be modified, 1 stolen, or even the devices may be subjected to a Denial of Service (DoS) attack. As a consequence, the product quality may deteriorate or sensitive information may be leaked. An Intrusion Detection System (IDS), implemented in the network layer of IIoT, can detect attacks, thereby protecting the data and devices. Despite substantial advancements in attack detection in IIoT, existing works fail to detect certain attacks obfuscated from detectors resulting in a low detection performance. To address the aforementioned issue, we propose a Deep Learning-based Two Level Network Intrusion Detection System (DLTL-NIDS) for IIoT environment, emphasizing challenging attacks. The attacks that attain low accuracy or low precision in level-1 detection are marked as challenging attacks. Experimental results show that the proposed model, when tested against TON IoT, figures out the challenging attacks well and achieves an accuracy of 99.97%, precision of 95.62%, recall of 99.5%, and F1-score of 99.65%. The proposed DL-TLNIDS, when compared with state-of-art models, achieves a decrease in false alarm rate to 2.34% (flagging normal traffic as an attack) in IIoT.

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“…With the rise in advanced technologies and their applications in real life have changed into a smarter life like form urban areas to intellectual cities [6]. This also helped in usage of various sources and services in hand to very common people as well.…”

Section: Literature Surveymentioning

confidence: 99%

An Integrated Reinforcement DQNN Algorithm to Detect Crime Anomaly Objects in Smart Cities

Mandala¹,

P²,

Vulapula³

2021

IJACSA

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In olden days it is difficult to identify the unsusceptible forces happening in the society but with the advancement of smart devices, government has started constructing smart cities with the help of IoT devices, to capture the susceptible events happening in and around the surroundings to reduce the crime rate. But, unfortunately hackers or criminals are accessing these devices to protect themselves by remotely stopping these devices. So, the society need strong security environment, this can be achieved with the usage of reinforcement algorithms, which can detect the anomaly activities. The main reason for choosing the reinforcement algorithms is it efficiently handles a sequence of decisions based on the input captured from the videos. In the proposed system, the major objective is defined as minimum identification time from each frame by defining if then decision rules. It is a sort of autonomous system, where the system tries to learn from the penalties posed on it during the training phase. The proposed system has obtained an accuracy of 98.34% and the time to encrypt the attributes is also less.

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IoTBoT-IDS: A novel statistical learning-enabled botnet detection framework for protecting networks of smart cities

Cited by 101 publications

References 36 publications

Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

Deep Learning Based Attack Detection in IIoT using Two-Level Intrusion Detection System

An Integrated Reinforcement DQNN Algorithm to Detect Crime Anomaly Objects in Smart Cities

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