Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 24, 2024

With the growing popularity of autonomous vehicles (AVs), confirming their safety has become a significant concern. Vehicle manufacturers have combined Android operating system into AVs to improve consumer comfort. However, diversity and weaknesses pose substantial risks AVs, as these factors can expose them threats, namely malware. The advanced behaviour multi-data source fusion in driving models mitigated recognition accuracy effectualness for To efficiently counter new malware variants, novel techniques distinct from conventional methods must be utilized. Machine learning (ML) cannot detect every complex variant. deep (DL) model is an efficient tool detecting various variants. This manuscript proposes Deep Learning-Based Improved Transformer Model on Malware Detection (DLBITM-AMD) technique Internet (IoVs). main aim presented DLBITM-AMD approach effectually accurately. method performs Z-score normalization process convert raw data standard form. Then, utilizes binary grey wolf optimization (BGWO) select optimum feature subsets. An improved transformer integrated with RNN softmax enhance classification recognition. Finally, snake optimizer algorithm (SOA) employed parameter method. extensive experiment accomplished benchmark dataset. performance validation portrayed superior value 99.26% over existing models.

Язык: Английский

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An effective IDS using CondenseNet and CoAtNet based approach for SDN-IoT environment

Computers & Electrical Engineering, Год журнала: 2025, Номер 123, С. 110305 - 110305

Опубликована: Апрель 1, 2025

Язык: Английский

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Optimized efficient predefined time adaptive neural network for stream traffic classification in software defined network

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 128086 - 128086

Опубликована: Май 1, 2025

Язык: Английский

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A hybrid wrapper technique enabled Network Intrusion Detection System for Software defined networking based IoT networks

Опубликована: Май 3, 2024

Язык: Английский

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Advanced Hybrid Techniques for Cyberattack Detection and Defense in IoT Networks

Security and Privacy, Год журнала: 2024, Номер unknown

Опубликована: Окт. 1, 2024

ABSTRACT The Internet of Things (IoT) represents a vast network devices connected to the Internet, making it easier for users connect modern technology. However, complexity these networks and large volume data pose significant challenges in protecting them from persistent cyberattacks, such as distributed denial‐of‐service (DDoS) attacks spoofing. It has become necessary use intrusion detection systems protect networks. Existing IoT face many problems limitations, including high false alarm rates delayed detection. Also, datasets used training may be outdated or sparse, which reduces model's accuracy, mechanisms not defend when any is detected. To address new hybrid deep learning machine methodology proposed that contributes detecting DDoS spoofing attacks, reducing alarms, then implementing defensive measures. In consists three stages: first stage propose method feature selection consisting techniques (correlation coefficient sequential selector); second model by integrating neural with classifier (cascaded long short‐term memory [LSTM] Naive Bayes classifier); third stage, improving defense blocking ports after threats maintaining integrity. evaluating performance methodology, (CIC‐DDoS2019, CIC‐IoT2023, CIC‐IoV2024) were used, also balanced obtain effective results. accuracy 99.91%, 99.88%, 99.77% was obtained. cross‐validation technique test ensure no overfitting. proven its provides powerful solution enhance security can applied fields other attacks.

Язык: Английский

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Design and Implementation of Secure Cloud Storage System based on Hybrid Cryptographic Algorithm

Опубликована: Авг. 23, 2024

Язык: Английский

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