Privacy-preserving malware detection in Android-based IoT devices through federated Markov chains

D’Angelo, Gianni; Farsimadan, Eslam; Ficco, Massimo; Palmieri, Francesco; Robustelli, Antonio

doi:10.1016/j.future.2023.05.021

Cited by 39 publications

(2 citation statements)

References 17 publications

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“…Specifically, Figure 8 provides insights into the most frequently employed evaluation metrics, addressing our RQ7 about their popularity in published studies. Although most of the metrics contributed detection/classification performance of the Fed-AIDS models, this include confusion matrix [80,81,112,114], Matthews Correlation Coefficient (MCC) [78], Area Under Curve (AUC) [90,135], Pearson Correlation Coefficient (PCC) [120], and also combined of Accuracy, Precision (Detection rate), Recall also known as True Positive Rate (TPR) and F-score [79,86,93,115]. Lastly, False Alarm Rate (FAR) also known as False Positive Rate (FPR) [51].…”

Section: F Evaluation Metricsmentioning

confidence: 99%

Review on Approaches of Federated Modeling in Anomaly-Based Intrusion Detection for IoT Devices

Isma’ila,

Danyaro,

Muazu

et al. 2024

IEEE Access

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The novelty of Federated Learning (FL) has emerged as a promising alternative to centralized machine learning systems in the context of Anomaly-Based Intrusion Detection Systems (AIDS) deployed on Internet of Things (IoT) devices. Unlike traditional centralized models, FL allows on-device model training and updates, reducing privacy concerns and issues like single points of failure and high false alarm rates (FAR). This approach, termed 'Fed-AIDS,' offers a more secure and efficient solution. However, the development of Fed-AIDS models faces challenges related to limited training data and the diverse nature of IoT datasets. Additionally, FL's decentralized nature introduces weight divergence issues arising from non-Independently and Identically Distributed (non-IID) clients. To address these challenges and optimize Fed-AIDS modeling, interdisciplinary research efforts are vital. The primary objective of this study is to conduct an up-to-date review by adopting a Systematic Literature Review (SLR) approach to analyze existing studies of Fed-AIDS modeling procedures for IoT devices. Data from the published studies were retrieved from Scopus database, which covered major publishers like IEEE, Elsevier and others. Specifically, our review conducted from the following Fed-AIDS perspectives: workflow and tools, training dataset, complexities of non-IID data in Fed-AIDS models, classification tasks, aggregation tasks, and model validation metrics. Based on the research findings, the study highlights a series of challenges and proposes potential solutions to stand in future research in Fed-AIDS modeling, aiming to advance the field of IoT device security.

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Section: F Evaluation Metricsmentioning

confidence: 99%

Review on Approaches of Federated Modeling in Anomaly-Based Intrusion Detection for IoT Devices

Isma’ila,

Danyaro,

Muazu

et al. 2024

IEEE Access

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“…Slow internet speeds during an incident response time can be caused by obsolete technology, network congestion, and a large number of network-connected devices [ 1 ]. This can result in slow download and upload speeds, high latency, and poor network stability, which can significantly impact the incident response [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Efficient Internet-of-Things Cyberattack Depletion Using Blockchain-Enabled Software-Defined Networking and 6G Network Technology

Razaque,

Yoo,

Bektemyssova

et al. 2023

Sensors

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Low-speed internet can negatively impact incident response by causing delayed detection, ineffective response, poor collaboration, inaccurate analysis, and increased risk. Slow internet speeds can delay the receipt and analysis of data, making it difficult for security teams to access the relevant information and take action, leading to a fragmented and inadequate response. All of these factors can increase the risk of data breaches and other security incidents and their impact on IoT-enabled communication. This study combines virtual network function (VNF) technology with software -defined networking (SDN) called virtual network function software-defined networking (VNFSDN). The adoption of the VNFSDN approach has the potential to enhance network security and efficiency while reducing the risk of cyberattacks. This approach supports IoT devices that can analyze large volumes of data in real time. The proposed VNFSDN can dynamically adapt to changing security requirements and network conditions for IoT devices. VNFSDN uses threat filtration and threat-capturing and decision-driven algorithms to minimize cyber risks for IoT devices and enhance network performance. Additionally, the integrity of IoT devices is safeguarded by addressing the three risk categories of data manipulation, insertion, and deletion. Furthermore, the prioritized delegated proof of stake (PDPoS) consensus variant is integrated with VNFSDN to combat attacks. This variant addresses the scalability issue of blockchain technology by providing a safe and adaptable environment for IoT devices that can quickly be scaled up and down to pull together the changing demands of the organization, allowing IoT devices to efficiently utilize resources. The PDPoS variant provides flexibility to IoT devices to proactively respond to potential security threats, preventing or mitigating the impact of cyberattacks. The proposed VNFSDN dynamically adapts to the changing security requirements and network conditions, improving network resiliency and enabling proactive threat detection. Finally, we compare the proposed VNFSDN to existing state-of-the-art approaches. According to the results, the proposed VNFSDN has a 0.08 ms minimum response time, a 2% packet loss rate, 99.5% network availability, a 99.36% threat detection rate, and a 99.77% detection accuracy with 1% malicious nodes.

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Advanced hybrid malware identification framework for the Internet of Medical Things, driven by deep learning

Safeer,

Tahir,

Nawaz

et al. 2024

Security and Privacy

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The Internet of Things (IoT) effortlessly enables communication between items on the World Wide Web and other systems. This extensive use of IoTs has created new services and automation in numerous industries, enhancing the standard of living, especially in healthcare. Internet of Medical Things (IoMT) adoption has been beneficial during pandemic conditions by enabling remote patient monitoring and therapy. Nevertheless, the excessive use of IoMT has raised security concerns as it can compromise critical data. This breach in security can result in an inaccurate diagnosis or violate privacy. This research presents a novel approach to hybrid deep learning‐based detection of malware solutions for the IoT. This study uses RNN‐Bi‐LSTM to detect and extract significant features related to an already existing dataset. The proposed model exhibits a detection accuracy of 98.38% when evaluated using these existing datasets. Statistical tests like Mathew co‐relation and Log Loss also indicated reliability of proposed framework. The distinguished feature of our framework is its ability to combine complex deep learning models for IoMT security, which is of economic and scientific importance. It certainly offers a reliable solution for healthcare applications that rely on real‐time functionality and dependency on IoMT systems.

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Privacy-preserving malware detection in Android-based IoT devices through federated Markov chains

Cited by 39 publications

References 17 publications

Review on Approaches of Federated Modeling in Anomaly-Based Intrusion Detection for IoT Devices

Review on Approaches of Federated Modeling in Anomaly-Based Intrusion Detection for IoT Devices

Efficient Internet-of-Things Cyberattack Depletion Using Blockchain-Enabled Software-Defined Networking and 6G Network Technology

Advanced hybrid malware identification framework for the Internet of Medical Things, driven by deep learning

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