HCL-Classifier: CNN and LSTM based hybrid malware classifier for Internet of Things (IoT)

Abdullah, Muhammed Amin; Yu, Yongbin; Adu, Kwabena; Imrana, Yakubu; Wang, Xiangxiang; Cai, Jingye

doi:10.1016/j.future.2022.12.034

Cited by 11 publications

(5 citation statements)

References 24 publications

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“…Li et al 40 , employing CNN with minimal processing expense, acknowledges its unsuitability for complex designs. Abdullah et al 41 , also utilizing CNN, observes an accuracy increase without detailing the computational complexity. Finally, the CNN method by 42 produces commendable results but encounters difficulties due to a high False Negative rate.…”

Section: Related Workmentioning

confidence: 99%

Deep learning hybridization for improved malware detection in smart Internet of Things

Almazroi,

Ayub

2024

Sci Rep

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The rapid expansion of AI-enabled Internet of Things (IoT) devices presents significant security challenges, impacting both privacy and organizational resources. The dynamic increase in big data generated by IoT devices poses a persistent problem, particularly in making decisions based on the continuously growing data. To address this challenge in a dynamic environment, this study introduces a specialized BERT-based Feed Forward Neural Network Framework (BEFNet) designed for IoT scenarios. In this evaluation, a novel framework with distinct modules is employed for a thorough analysis of 8 datasets, each representing a different type of malware. BEFSONet is optimized using the Spotted Hyena Optimizer (SO), highlighting its adaptability to diverse shapes of malware data. Thorough exploratory analyses and comparative evaluations underscore BEFSONet’s exceptional performance metrics, achieving 97.99% accuracy, 97.96 Matthews Correlation Coefficient, 97% F1-Score, 98.37% Area under the ROC Curve(AUC-ROC), and 95.89 Cohen’s Kappa. This research positions BEFSONet as a robust defense mechanism in the era of IoT security, offering an effective solution to evolving challenges in dynamic decision-making environments.

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Section: Related Workmentioning

confidence: 99%

Deep learning hybridization for improved malware detection in smart Internet of Things

Almazroi,

Ayub

2024

Sci Rep

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“…However, this requires homogeneous on-device models, which may be difficult to implement in networked IoT systems. Other studies have explored the use of deep-learning models for malware detection in IoT devices [132], [157], [158], [159]. For instance, Chaganti et al [157] used deep-learning models to achieve high accuracy but only for certain types of malware.…”

Section: E: Malware Detection Approaches In Iot Devicesmentioning

confidence: 99%

“…For instance, Chaganti et al [157] used deep-learning models to achieve high accuracy but only for certain types of malware. Abdullah et al [158] and Khan and Ullah [159] employed hybrid learning models with a high detection rate but with complexity and limited training datasets. Smmarwar et al [160] developed AI for detecting malware in industrial IoT, emphasizing dynamic feature selection and continuous model updates.…”

Section: E: Malware Detection Approaches In Iot Devicesmentioning

confidence: 99%

A Review of State-of-the-Art Malware Attack Trends and Defense Mechanisms

Ferdous,

Islam,

Mahboubi

et al. 2023

IEEE Access

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The increasing sophistication of malware threats has led to growing concerns in the antimalware community, as malware poses a significant danger to online users despite the availability of numerous defense solutions. This study aims to comprehensively review malware evolution and current attack trends to identify effective defense mechanisms. It reviews the most recent journal articles, conference proceedings, reports, and online resources published during the last five years. We extensively review the malware landscape from 1970 to the present and analyze malware types, operational mechanisms, attack vectors, and vulnerabilities. Furthermore, we explore different defensive strategies developed in response to these evolving threats. Our findings highlight the increasing sophistication of malware attack trends, including a surge in cryptojacking, attacks on mobile devices, Internet of Things devices, ransomware, advanced persistent threats, supply chain attacks, fileless malware, cloud-based attacks, exploitation of remote employees, and attack trends on edge networks. Defense strategies have also evolved in parallel, emphasizing multilayered security measures to counter these dynamic threats. This study highlights the critical need for robust, multilayered security measures to combat dynamic malware. Despite these advancements, some open challenges and significant research gaps remain, which require further innovation. This review serves as a valuable guide for cybersecurity professionals by identifying the key trends, challenges, limitations, and future cybersecurity research opportunities.INDEX TERMS Malware evolution, malware attack trends, defense mechanisms, malware detection, machine learning, deep learning. D. INCREASED NUMBER OF TARGETED RANSOMWARE ATTACKSRecent malware trends have highlighted increased targeted ransomware attacks, in which hackers access a network or system, encrypt data, and demand a ransom to restore access. This increase in ransomware attacks is driven by several factors, including potential financial gains, easy operation, the rise of ransomware-as-a-service platforms, and the popularity of cryptocurrencies like Bitcoin [97]. Ransomware attacks are wide-ranging, targeting everything from desktops and mobile devices and increasingly involving IoT devices.

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“…By leveraging CNN, features can be automatically learned from input data, eliminating the need for manual intervention. For instance, Abdullah et al [ 8 ] introduced a hybrid static classifier that combines CNN and LSTM for the purpose of malicious software classification in the Internet of Things (IoT). Their approach entails utilizing CNN to automatically select and extract features, which are then fed into a bidirectional LSTM for classification.…”

Section: Introductionmentioning

confidence: 99%

A Malicious Code Detection Method Based on Stacked Depthwise Separable Convolutions and Attention Mechanism

Hong

Wang

et al. 2023

Sensors

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To address the challenges of weak model generalization and limited model capacity adaptation in traditional malware detection methods, this article presents a novel malware detection approach based on stacked depthwise separable convolutions and self-attention, termed CoAtNet. This method combines the strengths of the self-attention module’s robust model adaptation and the convolutional networks’ powerful generalization abilities. The initial step involves transforming the malicious code into grayscale images. These images are subsequently processed using a detection model that employs stacked depthwise separable convolutions and an attention mechanism. This model effectively recognizes and classifies the images, automatically extracting essential features from malicious software images. The effectiveness of the method was validated through comparative experiments using both the Malimg dataset and the augmented Blended+ dataset. The approach’s performance was evaluated against popular models, including XceptionNet, EfficientNetB0, ResNet50, VGG16, DenseNet169, and InceptionResNetV2. The experimental results highlight that the model surpasses other malware detection models in terms of accuracy and generalization ability. In conclusion, the proposed method addresses the limitations of traditional malware detection approaches by leveraging stacked depthwise separable convolutions and self-attention. Comprehensive experiments demonstrate its superior performance compared to existing models. This research contributes to advancing the field of malware detection and provides a promising solution for enhanced accuracy and robustness.

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HCL-Classifier: CNN and LSTM based hybrid malware classifier for Internet of Things (IoT)

Cited by 11 publications

References 24 publications

Deep learning hybridization for improved malware detection in smart Internet of Things

Deep learning hybridization for improved malware detection in smart Internet of Things

A Review of State-of-the-Art Malware Attack Trends and Defense Mechanisms

A Malicious Code Detection Method Based on Stacked Depthwise Separable Convolutions and Attention Mechanism

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