# \"Network Sentiment\" Framework to Improve Security and Privacy for Smart Home
## 2 Related Works  
In a IoT smart home scenario many small, low power, low computation devices are used in the segment of home automation to improve the quality of services and, as consequence, the quality of experience offered to the users [11]. Different aspects for preserving privacy can be found in [12] and several papers in literature examine the security challenges and threats suited for smart homes, as in [6; 13], where surveys of existing protocols for secure communications on IoT can be found, together with open challenges and research issues in this area. In [14] a protocol is proposed to secure route optmization and handover management, which uses trust between Proxy mobile IPv6 (PMIPv6) domain and smart home to ensure security as well as performance over the path between mobile nodes and home IoT devices. In [15], the authors propose a secure scheme for data uploading on Cloud to guarantee the integrity of the data with a session key generation assisted by the home gateway. In [5], a review of existing network techniques for enhancing IoT security is provided together with future key technologies for trusted Smart Home systems such as system auto-configuration and security update. A gateway architecture is chosen as the most appropriate for resource-constrained devices and for high system availability. In [16] the authors propose a cross layer method to overcome the traceability of the user in smart home networks 6 LoWPAN. In this case, the IEEE 802.15.4 standards [17] foresees to cipher the payload but leaves out the headers containing the layer 2 addresses of the source and destination of the packets. In the proposed method all the nodes change periodically all their addresses, both at layer 2 and 3, to secure of both the 802.15.4 and 6 LoWPAN protocols. However traditional Information and Communications Technology (ICT) standard security solutions, such as cryptography and authentication tecniques, do not prevent all possible attacks and are not tailored for smart home environment due to the resource-constrained IoT devices, to their heterogeneous interaction and to their different policy and connectivity domains. As consequence, IDSs are required to detect intruders and malicious activities to threaten the network. IDSs can be classified in signature-based or behavior-based. Signature-based IDSs use pattern-matching techniques to detect an attack, while behavior-based IDSs analyze the devices behavior to detect anomalies. The first type is best suited for known attack and the second one for unknown attacks. Hybrid detection technique combining signature and anomaly based approaches can improve the efficacy of IDS. Furthermore, IDSs can be classified in Host, Network or Distributed. Host IDSs only process the data of a single node, Network IDSs are able to monitor a network (typically one or more links), and Distributed IDSs are able to process the data of multiple, independent probes and/or multiple federated IDSs. Several IDSs are designed for wireless sensor networks (WSN), a general survey on IDS and IPS can be found in [18]. However, these IDSs are not directly applicable for IoT because IoT devices are globally accessible, are resource-constrained, are heterogeneous, adopt new protocols such as COAP, RPL. A survey of more IoT-oriented IDSs can be found in [19; 20; 21; 22]. Most of these IDSs focus only on threats at network layer. Examples of real-time IDS for IoT can be SVELTE [23] which meets the requirements of IPv6-connected IoT devices and detects routing attacks such as sink-hole, selective forwarding, and spoofed or altered routing information; Complex Event-processing (CEP) [24] which detects events in real-time by analyzing the stream of information; the IDS by [25] which targets Denial of Service (DoS) attacks on RPL. In [26] an intrusion detection and prevention framework is proposed to detect DoS attacks on the network and attacks against the normal operations rules of the CoAP protocol.