Addressing Polymorphic Advanced Threats in Internet of Things Networks by Cross‐Layer Profiling
Hisham Alasmary, Afsah Anwar, Laurent Njilla, Charles Kamhoua, Aziz Mohaisen · 2020
With the persistence of the Internet, connected Internet of Things (IoT) devices have spread widely and rapidly. Contemporary security mechanisms for the IoT infrastructure suffer from several security and privacy challenges that are of paramount importance. To understand this importance, different methods are needed to mitigate security issues such as confidentiality, integrity, or availability violations. Prior work on IoT security and privacy have focused on threats in the hardware and software, such as access control and authentication, etc.; however, no comprehensive approach has been put forward to address security and privacy concerns. Advanced Persistent Threats (APT) are a stealthy threat and is considered as a major roadblock in securing smart home networks. This work proposes an intrusion detection system (IDS), the Security Layer for Smart Home (SLaSH), as a cross-layer security approach that integrates the capabilities and features of different layers to secure the entire smart home network by analyzing the attack surface of the device, network, and service layer. Additionally, we suggest a machine learning-based detection system built on both rules- and behavior-based features for cross-layer intrusion detection.