High Performance Classification Model to Identify Ransomware Payments for Heterogeneous Bitcoin Networks

Al‐Haija, Qasem Abu; Alsulami, Abdulaziz A.

doi:10.3390/electronics10172113

Cited by 46 publications

(32 citation statements)

References 32 publications

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“…Shuffling is basically redistributing the data points/records throughout several execution processes (or even through several computing machines). In this research, since we are about to train/test splitting, we performed a uniform shuffle at every epoch, which guarantees that every item has the same chance to occur at any position [32]. The data shuffling applied in this research is performed using the following procedure:…”

Section: Data Shuffling Process (Dsh)mentioning

confidence: 99%

ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks

Al‐Haija

Al-Dala’ien

2022

JSAN

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Due to the prompt expansion and development of intelligent systems and autonomous, energy-aware sensing devices, the Internet of Things (IoT) has remarkably grown and obstructed nearly all applications in our daily life. However, constraints in computation, storage, and communication capabilities of IoT devices has led to an increase in IoT-based botnet attacks. To mitigate this threat, there is a need for a lightweight and anomaly-based detection system that can build profiles for normal and malicious activities over IoT networks. In this paper, we propose an ensemble learning model for botnet attack detection in IoT networks called ELBA-IoT that profiles behavior features of IoT networks and uses ensemble learning to identify anomalous network traffic from compromised IoT devices. In addition, our IoT-based botnet detection approach characterizes the evaluation of three different machine learning techniques that belong to decision tree techniques (AdaBoosted, RUSBoosted, and bagged). To evaluate ELBA-IoT, we used the N-BaIoT-2021 dataset, which comprises records of both normal IoT network traffic and botnet attack traffic of infected IoT devices. The experimental results demonstrate that our proposed ELBA-IoT can detect the botnet attacks launched from the compromised IoT devices with high detection accuracy (99.6%) and low inference overhead (40 µ-seconds). We also contrast ELBA-IoT results with other state-of-the-art results and demonstrate that ELBA-IoT is superior.

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Section: Data Shuffling Process (Dsh)mentioning

confidence: 99%

ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks

Al‐Haija

Al-Dala’ien

2022

JSAN

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“…The theory of the CIA triad model, which is the conditionality, integrity, and availability, can be used to measure the security level of a system [10]. Conditionality assures that data won't be accessed by unauthorized users, programs, or procedures.…”

Section: Figure 1 Replay Attack Targeting Keyless Systemmentioning

confidence: 99%

Detection of Fake Replay Attack Signals on Remote Keyless Controlled Vehicles Using Pre-Trained Deep Neural Network

Al‐Haija¹,

Alsulami²

2022

Preprint

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The keyless systems have replaced the old fashion methods of inserting physical keys in the keyhole to, i.e., unlock the door, because they are inconvenient and easy to be exploited by the threat actors. Keyless systems use the technology of radio frequency (RF) as an interface to transmit signals from the key fob to the vehicle. However, Keyless systems are susceptible to being compromised by a thread actor who intercepts the transmitted signal and performs a reply attack. In this paper, we propose a transfer learning-based model to identify the replay attacks launched against remote keyless controlled vehicles. Specifically, the system makes use of a pre-trained ResNet50 deep neural network to predict the wireless remote signals used to lock or unlock doors of a remote-controlled vehicle system remotely. The signals are finally classified into three classes: real signal, fake signal high gain, and fake signal low gain. We have trained our model with 100 epochs (3800 iterations) on a KeFRA 2022 dataset, a modern dataset. The model has recorded a final validation accuracy of 99.71% and a final validation loss of 0.29% at a low inferencing time of 50 ms for the model-based SGD solver. The experimental evaluation revealed the supremacy of the proposed model.

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“…Cyberattacks usually are launched against the victim system to violate the major security services, such as confidentiality, integrity, and availability. Currently, cyberattacks are available in different forms such as ransomware, malware, adware, reconnaissance, denial of service, and others [ 47 , 48 ]. In this paper, we develop a cyberattacks detection system making use of efficient machine-learning techniques to provide an accurate, precise, and sensitive prediction process.…”

Section: System Modelingmentioning

confidence: 99%

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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High Performance Classification Model to Identify Ransomware Payments for Heterogeneous Bitcoin Networks

Cited by 46 publications

References 32 publications

ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks

ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks

Detection of Fake Replay Attack Signals on Remote Keyless Controlled Vehicles Using Pre-Trained Deep Neural Network

Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

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