Fusion of deep learning based cyberattack detection and classification model for intelligent systems

Alzubi, Omar A.; Qiqieh, Issa; Alzubi, Jafar A.

doi:10.1007/s10586-022-03686-0

Cited by 41 publications

(12 citation statements)

References 27 publications

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“…These algorithms will continue to improve themselves through a process called reinforcement learning, thereby responding to new types of attacks and machine learning strategies. [5][6][7][57][58][59] All of the aforementioned studies are connected in some way, and they all offer partial answers to the problem of detecting and preventing DoS attacks. We find that DoS attacks in SDN may be identified and mitigated by different methods, including the inclusion of hardware requirements, packet drop rate, the blocking of malicious traffic at the port and switch levels, false alerts, detection accuracy, and computational overhead.…”

Section: Related Workmentioning

confidence: 99%

“…Our ambition involves leveraging artificial intelligence‐powered algorithms to independently identify and categorize risks with exceptional precision and efficiency. These algorithms will continue to improve themselves through a process called reinforcement learning, thereby responding to new types of attacks and machine learning strategies 5–7,57–59 …”

Section: Related Workmentioning

confidence: 99%

“…Data security and privacy are significant issues, and there is a lack of standardized data protocols in many IoT devices, highlighting the importance of ensuring security in SDN architecture. [5][6][7] The challenges associated with administration and operation of conventional network technologies have prompted the emergence of SDNs. In traditional network architectures, distinct network components are responsible for managing the control plane and data plane functions.…”

Section: Introductionmentioning

confidence: 99%

“…Its incorporation into computer networks, computing environments, smart gadgets, and healthcare technologies has resulted in enhanced personalized and efficient care. Data security and privacy are significant issues, and there is a lack of standardized data protocols in many IoT devices, highlighting the importance of ensuring security in SDN architecture 5–7 …”

Section: Introductionmentioning

confidence: 99%

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TPAAD: Two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network

Nisa,

Khan,

Ahmad

et al. 2024

Int J Network Mgmt

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Software‐defined networking (SDN) has received considerable attention and adoption owing to its inherent advantages, such as enhanced scalability, increased adaptability, and the ability to exercise centralized control. However, the control plane of the system is vulnerable to denial‐of‐service (DoS) attacks, which are a primary focus for attackers. These attacks have the potential to result in substantial delays and packet loss. In this study, we present a novel system called Two‐Phase Authentication for Attack Detection that aims to enhance the security of SDN by mitigating DoS attacks. The methodology utilized in our study involves the implementation of packet filtration and machine learning classification techniques, which are subsequently followed by the targeted restriction of malevolent network traffic. Instead of completely deactivating the host, the emphasis lies on preventing harmful communication. Support vector machine and K‐nearest neighbours algorithms were utilized for efficient detection on the CICDoS 2017 dataset. The deployed model was utilized within an environment designed for the identification of threats in SDN. Based on the observations of the banned queue, our system allows a host to reconnect when it is no longer contributing to malicious traffic. The experiments were run on a VMware Ubuntu, and an SDN environment was created using Mininet and the RYU controller. The results of the tests demonstrated enhanced performance in various aspects, including the reduction of false positives, the minimization of central processing unit utilization and control channel bandwidth consumption, the improvement of packet delivery ratio, and the decrease in the number of flow requests submitted to the controller. These results confirm that our Two‐Phase Authentication for Attack Detection architecture identifies and mitigates SDN DoS attacks with low overhead.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

TPAAD: Two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network

Nisa,

Khan,

Ahmad

et al. 2024

Int J Network Mgmt

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show abstract

“…Meanwhile, the convergence speed was very low. Alzubi et al 21 reviewed a fusion deep learning (FDL) technique to classify and detect cyber‐attacks and provide security in intelligence systems. The FDL approach used the MobileNetv2 model to extract malware with low computational complexity.…”

Section: Literature Surveymentioning

confidence: 99%

A novel‐cascaded ANFIS‐based deep reinforcement learning for the detection of attack in cloud IoT‐based smart city applications

Almasri

Alajlan

2023

Concurrency and Computation

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SummaryVast usages of Internet of Things (IoT) devices in various smart applications have laid a foundation for the evolution of modern smart cities. The increasing dependency of smart city applications on communication and information technologies enhances operational efficiency, sustainability, and automation of city services. However, due to the heterogeneous nature of IoT devices, the network faces critical security issues while executing continued network operations and services, particularly by cyber‐attacks. One of the predominant and rampant cyber‐attacks in smart city applications is botnet attacks. Therefore, a novel deep learning model for the detection and isolation of cyber‐attacks is proposed in the cloud IoT‐based smart city applications to protect against such cyber‐attacks. The proposed framework utilizes two different modules to automatically detect and isolate the malicious traffic emanating from compromised IoT devices with more efficiency. Here, two different datasets namely the IoT network intrusion and the ISCX 2012 IDs datasets are utilized for the evaluation of the proposed framework. In the first phase, the compromised device which communicates malicious network traffics through the network is identified using a cascaded adaptive neuro‐fuzzy inference system (CANFIS). After detection, IP address of abnormal traffic is recorded and informed to the system administrator. In the second phase, communication pathways of compromised devices with other normal devices are blocked and the compromised devices are isolated from the network using the modified deep reinforcement learning (MDRL) approach. The analytic result shows that the proposed framework achieves a greater accuracy rate of about 98.7% as compared to other state‐of‐art methods.

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ADSBAN: Anomaly detection system for body area networks utilizing IoT and machine learning

Siddiqui,

Kalra,

Krishna

2024

Concurrency and Computation

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SummaryBody area networks (BANs) play a pivotal role in modern healthcare, enabling real‐time data collection and monitoring of vital patient parameters, thereby empowering healthcare providers to respond swiftly to any serious health conditions. These networks depend on strategically placed sensors on the patient's body to collect important health data. The integrity of this data is really important, as problems with a component can cause doctors to make mistakes that could be life‐threatening. Consequently, developing and evaluating robust anomaly detection methods for BANs is paramount. To address this concern, an anomaly detection system for body area networks (ADSBAN) has been proposed in this article. Different machine learning methods have been tested and implemented to improve the proposed system's performance. These include decision tree, K‐nearest neighbor, logistic regression (LR), random forest, AdaBoost (Adaptive Boosting), and XGBoost (Extreme Gradient Boosting). Further, this framework has been tested with emulated and standard datasets and compared with existing methodologies. The research employs an IoT Flock emulator to replicate BAN conditions and simulate two distinct attack scenarios. Wireshark aids in thoroughly analyzing network traffic, while Python, in conjunction with tools like Keras and Pandas, is instrumental in implementing the ML models. Further, a CIC flowmeter has been utilized to convert the .pcap file format into .CSV file format. Comprehensive evaluation metrics such as precision, recall, accuracy, F1 score, and the Mathew correlation coefficient (MCC) demonstrate the consistent superiority of LR on emulated (i.e., traffic generated through a simulator IoT Flock) and standard datasets (BoT‐IoT). LR shows the highest accuracy of 99.92% on emulated and standard datasets, while XGBoost has the highest average accuracy of 99.92% on standard dataset. This research significantly bolsters the reliability of healthcare data, instilling confidence among healthcare professionals in their decision‐making processes. By safeguarding the integrity of vital health data in BANs, it advances the quality of patient care. It underscores the indispensable role of ML techniques in fortifying the resilience of healthcare systems.

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Fusion of deep learning based cyberattack detection and classification model for intelligent systems

Cited by 41 publications

References 27 publications

TPAAD: Two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network

TPAAD: Two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network

A novel‐cascaded ANFIS‐based deep reinforcement learning for the detection of attack in cloud IoT‐based smart city applications

ADSBAN: Anomaly detection system for body area networks utilizing IoT and machine learning

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