Alazab Moutaz, Awajan Albara, Alazzam Hadeel, Wedyan Mohammad, Alshawi Bandar, Alturki Ryan
Department of Intelligent Systems, Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19385, Jordan.
Cybersecurity Department, School of Computing and Data Sciences, Oryx Universal College with Liverpool John Moores University, Doha 34110, Qatar.
Sensors (Basel). 2024 Mar 29;24(7):2188. doi: 10.3390/s24072188.
The Internet of Things (IoT) is the underlying technology that has enabled connecting daily apparatus to the Internet and enjoying the facilities of smart services. IoT marketing is experiencing an impressive 16.7% growth rate and is a nearly USD 300.3 billion market. These eye-catching figures have made it an attractive playground for cybercriminals. IoT devices are built using resource-constrained architecture to offer compact sizes and competitive prices. As a result, integrating sophisticated cybersecurity features is beyond the scope of the computational capabilities of IoT. All of these have contributed to a surge in IoT intrusion. This paper presents an LSTM-based Intrusion Detection System (IDS) with a Dynamic Access Control (DAC) algorithm that not only detects but also defends against intrusion. This novel approach has achieved an impressive 97.16% validation accuracy. Unlike most of the IDSs, the model of the proposed IDS has been selected and optimized through mathematical analysis. Additionally, it boasts the ability to identify a wider range of threats (14 to be exact) compared to other IDS solutions, translating to enhanced security. Furthermore, it has been fine-tuned to strike a balance between accurately flagging threats and minimizing false alarms. Its impressive performance metrics (precision, recall, and F1 score all hovering around 97%) showcase the potential of this innovative IDS to elevate IoT security. The proposed IDS boasts an impressive detection rate, exceeding 98%. This high accuracy instills confidence in its reliability. Furthermore, its lightning-fast response time, averaging under 1.2 s, positions it among the fastest intrusion detection systems available.
物联网(IoT)是一种底层技术,它能够将日常设备连接到互联网,并享受智能服务带来的便利。物联网市场正以令人瞩目的16.7%的增长率增长,其市场规模接近3003亿美元。这些引人注目的数据使其成为网络犯罪分子的一个诱人目标。物联网设备采用资源受限的架构构建,以提供紧凑的尺寸和具有竞争力的价格。因此,集成复杂的网络安全功能超出了物联网的计算能力范围。所有这些因素都导致了物联网入侵事件的激增。本文提出了一种基于长短期记忆网络(LSTM)的入侵检测系统(IDS),该系统采用动态访问控制(DAC)算法,不仅能够检测入侵,还能抵御入侵。这种新颖的方法取得了令人印象深刻的97.16%的验证准确率。与大多数入侵检测系统不同,所提出的入侵检测系统模型是通过数学分析进行选择和优化的。此外,与其他入侵检测解决方案相比,它能够识别更广泛的威胁(确切地说是14种),从而增强了安全性。此外,它还经过了微调,以在准确标记威胁和最小化误报之间取得平衡。其令人印象深刻的性能指标(精确率、召回率和F1分数均徘徊在97%左右)展示了这种创新型入侵检测系统提升物联网安全性的潜力。所提出的入侵检测系统拥有令人印象深刻的检测率,超过了98%。如此高的准确率使其可靠性令人信服。此外,其平均响应时间不到1.2秒,堪称闪电般快速,使其跻身于现有最快的入侵检测系统之列。