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Intelligent Intrusion Detection in Low-Power IoTs

机译：低功耗物联网中的智能入侵检测

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Security and privacy of data are one of the prime concerns in today's Internet of Things (IoT). Conventional security techniques like signature-based detection of malware and regular updates of a signature database are not feasible solutions as they cannot secure such systems effectively, having limited resources. Programming languages permitting immediate memory accesses through pointers often result in applications having memory-related errors, which may lead to unpredictable failures and security vulnerabilities. Furthermore, energy efficient IoT devices running on batteries cannot afford the implementation of cryptography algorithms as such techniques have significant impact on the system power consumption. Therefore, in order to operate IoT in a secure manner, the system must be able to detect and prevent any kind of intrusions before the network (i.e., sensor nodes and base station) is destabilised by the attackers. In this article, we have presented an intrusion detection and prevention mechanism by implementing an intelligent security architecture using random neural networks (RNNs). The application's source code is also instrumented at compile time in order to detect out-of-bound memory accesses. It is based on creating tags, to be coupled with each memory allocation and then placing additional tag checking instructions for each access made to the memory. To validate the feasibility of the proposed security solution, it is implemented for an existing IoT system and its functionality is practically demonstrated by successfully detecting the presence of any suspicious sensor node within the system operating range and anomalous activity in the base station with an accuracy of 97.23%. Overall, the proposed security solution has presented a minimal performance overhead.
机译：数据的安全性和隐私性是当今物联网（IoT）的主要问题之一。诸如基于签名的恶意软件检测和签名数据库的定期更新之类的常规安全技术不是可行的解决方案，因为它们无法有效保护具有有限资源的此类系统。允许通过指针立即进行内存访问的编程语言通常会导致应用程序出现与内存相关的错误，这可能会导致无法预测的故障和安全漏洞。此外，依靠电池运行的高能效IoT设备无法承受密码算法的实施，因为此类技术对系统功耗具有重大影响。因此，为了以安全的方式操作IoT，系统必须能够在攻击者破坏网络（即传感器节点和基站）稳定之前检测并防止任何类型的入侵。在本文中，我们通过使用随机神经网络（RNN）实现智能安全体系结构，提出了一种入侵检测和预防机制。还在编译时对应用程序的源代码进行检测，以检测超出范围的内存访问。它基于创建标记，将其与每个内存分配结合在一起，然后为对内存的每次访问放置附加的标记检查指令。为了验证所提出的安全解决方案的可行性，该解决方案是为现有物联网系统实施的，其功能通过成功检测到系统工作范围内任何可疑传感器节点的存在以及基站中的异常活动而得到了实际证明，其准确性为97.23％。总体而言，建议的安全解决方案提供了最小的性能开销。

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