Anomaly detection for space information networks: A survey of challenges, techniques, and future directions

Diro, Abebe; Kaisar, Shahriar; Vasilakos, Athanasios V.; Anwar, Adnan; Nasirian, Araz; Olani, Gaddisa

doi:10.1016/j.cose.2024.103705

Cited by 7 publications

References 180 publications

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A cloud‐based hybrid intrusion detection framework using XGBoost and ADASYN‐Augmented random forest for IoMT

Salehpour,

Norouzi,

Balafar

et al. 2024

IET Communications

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Internet of Medical Things have vastly increased the potential for remote patient monitoring, data‐driven care, and networked healthcare delivery. However, the connectedness lays sensitive patient data and fragile medical devices open to security threats that need robust intrusion detection solutions within cloud‐edge services. Current approaches need modification to be able to handle the practical challenges that result from problems with data quality. This paper presents a hybrid intrusion detection framework that enhances the security of IoMT networks. There are three modules in the design. First, an XGBoost‐based noise detection model is used to identify data anomalies. Second, adaptive resampling with ADASYN is done to fine‐tune the class distribution to address class imbalance. Third, ensemble learning performs intrusion detection through a Random Forest classifier. This stacked model coordinates techniques that filter noise and preprocess imbalanced data, identifying threats with high accuracy and reliability. These results are then experimentally validated on the UNSW‐NB15 benchmark to demonstrate effective detection under realistically noisy conditions. The novel contributions of the work are a new hybrid structural paradigm coupled with integrated noise filtering, and ensemble learning. The proposed advanced oversampling with ADASYN gives a performance that surpasses all others with a reported 92.23% accuracy.

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A cloud‐based hybrid intrusion detection framework using XGBoost and ADASYN‐Augmented random forest for IoMT

Salehpour,

Norouzi,

Balafar

et al. 2024

IET Communications

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A tantalum oxide based memristive neuron device for anomaly detection application

Wu,

Hu,

Feng

et al. 2024

Applied Physics Letters

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Anomaly detection, a data intensive task, is very important in wide application scenarios. Memristor has shown excellent performance in data intensive tasks. However, memristor used for anomaly detection has rarely been reported. In this Letter, a tantalum oxide (TaOx) memristive neuron device has been developed for anomaly detection application. TaOx, a CMOS compatible material, based memristor shows reliable threshold switching characteristics, which is suitable for constructing memristive neuron. Furthermore, the output frequency of the memristive neuron is found to be proportionate to the applied stimulus intensity and at an inflection point starts to decrease, namely, thresholding effect. Based on the thresholding effect of the neuron output, the application of the memristive neuron for anomaly detection has been simulated. The results indicate that the TaOx memristive neuron with thresholding effect shows better performance (98.78%) than the neuron without threshoding effect (90.89%) for anomaly detection task. This work provided an effective idea for developing memristive anomaly detection system.

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Unveiling the 5G Frontier: Navigating Challenges, Applications, and Measurements in Channel Models and Implementations

Shoaib,

Husnain,

Sayed

et al. 2024

IEEE Access

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In the epoch of the fifth generation (5G) of wireless communication, this survey article illuminates the nuanced landscape of 5G Channel Measurements and Models. It meticulously navigates the challenges, methodologies, and practical implications that define the implementation of this groundbreaking technology. Embarking on the exploration, the article unravels the Challenges in 5G Implementation, addressing technological hurdles and standardized protocols pivotal for seamless integration. Unveiling the Existing Methods and Technologies, it highlights Massive MIMO, millimeter-wave communications, and the integration of artificial intelligence (AI) as transformative elements shaping high-capacity wireless communication. Beyond the technical intricacies, the article delves into the Diverse Applications and Use Cases of 5G, promising a paradigm shift from the Internet of Things (IoT) to mission-critical communications. Frequency Considerations and Channel Bands take center stage, dissecting the critical spectrum domain and exploring high-frequency millimeter-wave bands for enhanced data rates. Addressing concerns, the survey scrutinizes Health Concerns and Regulatory Considerations, presenting a balanced perspective on the impact of 5G networks on health and regulatory landscapes. The Benefits Over 4G and LTE drive the transition, with enhanced data rates, lower latency, and massive device connectivity distinguishing 5G. The environmental impact is considered in Energy Efficiency in 5G Networks, exploring strategies and innovations for making 5G networks more energy-efficient. Global Standards and Interoperability become imperative for 5G's global potential, examining ongoing efforts by standardization bodies for a unified framework. The exploration extends to the realm of 5G Channel Measurements and Models, dissecting Massive MIMO Channel Measurements, Millimeter-Wave Channel Measurements, Beamforming, Antenna Array Configurations, Time-Variant, and Time-Invariant Channel Measurements, Channel Modeling, and Measurement Tools and Techniques. Critically addressing Challenges in 5G Channel Measurements and Models, the article explores limitations and proposes avenues for future research. This survey article serves as a definitive compendium on 5G Channel Measurements and Models, unraveling the complexities, challenges, and opportunities inherent in the deployment of this transformative technology. Its holistic examination provides a valuable resource for researchers, practitioners, and industry stakeholders navigating the dynamic landscape of 5G wireless communication.

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Anomaly detection for space information networks: A survey of challenges, techniques, and future directions

Cited by 7 publications

References 180 publications

A cloud‐based hybrid intrusion detection framework using XGBoost and ADASYN‐Augmented random forest for IoMT

A cloud‐based hybrid intrusion detection framework using XGBoost and ADASYN‐Augmented random forest for IoMT

A tantalum oxide based memristive neuron device for anomaly detection application

Unveiling the 5G Frontier: Navigating Challenges, Applications, and Measurements in Channel Models and Implementations

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