## 8 Conclusions and Future Work  
In this paper, we explore the impact of filtering on the security and privacy of IoT systems. As part of the theoretical work, we discuss what a Smart Home and its devices are and what types of devices exist, and we generalise the communication patterns between them. While we extend our discussion to filtering in general, our understanding of the maturity of filtering is that of a central device in a packet-switched network, capable of inspecting the data flow and, based on policies, rejecting any adversarial or unwanted packets. To make this concrete, we have described the filtering technology called the _Bark_ approach, which we selected as a model for the filtering capabilities for our impact analysis. Proceeding to the analysis, the paper identifies four common scenarios in the Smart Home domain and their communication patterns. We believe that these four scenarios can be used either individually or in combination to describe most of today's Smart Home setups. For each scenario, we have performed in-depth STRIDE and LINDDUN analyses. We identify the following: (a) which elements of the system are being targeted, (b) which are potential attack vectors targeting those elements and (c) what the impacts are. This work provides the academic community with 29 security attack vectors, 32 security and privacy impacts, and 20 privacy attack vectors. All of them are systematically identified and presented in tables, which depict the dependencies between impacts and enabling attack vectors for each entity or data flow separately. Further, we determined that there is a large impact of a filtering method--as powerful as the _Bark_ approach--on the identified attack vectors and their impacts. A four-step scale was chosen for evaluation: _no or only low, some, medium_ or _high_ impact. This is even though the aggregated impact is conservatively calculated: the overall assessment is marked as the minimum impact of all attack vectors leading to it. So even if two of three attack vectors leading to that impact can be fully mitigated, one can only be mitigated with _some_ impact, and the overall impact is rated as _some_. For example, from the STRIDE analysis of an Internet-connected device (see Table 3), when the attacker tries to tamper with the Smart Home Device with the impact to run commands on the device to damage it or turn it into a bot (impact 'RCD'), then even if filtering login attempts to remove two attack vectors (URC, USC), the attack vector of having a remote code execution vulnerability in an interface (attack vector RCE) might only be reduced as some interface might need to remain reachable for functionality. Figure 12 summarises the analysis of the impact of filtering for the STRIDE and LINDDUN impacts. It is important to note that this does not consider any differences in the severity or likelihood of different threats. Moreover, for comparison, the influence of using a Virtual Private Network (VPN) or an Intrusion Detection System (IDSys) are also included. However, the details for VPN and IDSys had to be left out for brevity. From the comparison, we can see that Virtual Private Networks (VPNs), at least when installed on the home router, have _no or only low_ influence on most of the impacts and _some_ influence on only a few attack vectors. They should thus not be suggested or marketed as suitable for maintaining security and privacy in a Smart Home. On the other hand, Intrusion Detection Systems were found to have an effect on many threats, especially in security (STRIDE), while the coverage in the area of privacy (LINDDUN) is not as high. It is important that not a single Intrusion Detection System, but intrusion detection as a whole, was assessed, with results from different approaches. In addition, no distinction was made between detection and prevention. Therefore, those results for IDS are not fully comparable to the ones of filtering, which is fully concerned with the prevention of attacks.