Software Defined Security Architecture for a Smart Home Networks Using Token Sharing Mechanism

Author(s):  
Utkarsh Saxena ◽  
J.S Sodhi ◽  
Yaduveer Singh

: Since the end of 2000, there are lot of revolutions occurs in the field of Internet of Things (IoT), that affect tremendously on the world internet infrastructure. Smart Home is a dwelling which incorporates the key electrical appliances of a home connected to each other in a network, so that it can be easily accessed through remote device. The complexity of a smart home lies in the fact that it comprises of many heterogeneous networks which works simultaneously in order to achieve common task. Since each and every network has some sort of vulnerability associated with it, the same lies with Smart home network. Each of the layers of a smart home architecture is associated with some vulnerability. These Vulnerability could be dangerous and can exploit the network if not properly handled. This Paper discussed a secure framework based on Token Sharing mechanism using Squid Authentication for Access Control in a Smart Home Networks.

Author(s):  
Tommaso Pecorella ◽  
Laura Pierucci ◽  
Francesca Nizzi

A Smart Home is characterized by the presence of a huge number of small, low power devices, along with more classical devices. According to the Internet of Things (IoT) paradigm, all of them are expected to be always connected to the Internet in order to provide enhanced services. In this scenario, an attacker can undermine both the network security and the user’s security/privacy. Traditional security measures are not sufficient, because they are too difficult to setup and are either too weak to effectively protect the user or too limiting for the new services effectiveness. The paper suggests to dynamically adapt the security level of the smart home network according to the user perceived risk level what we have called network sentiment analysis. The security level is not fixed, established by a central system (usually by the Internet Service Provider) but can be changed with the users cooperation. The security of the smart home network is improved by a distributed firewalling and Intrusion Detection Systems both to the smart home side as to the Internet Service Provider side. These two parts must cooperate and integrate their actions for reacting dynamically to new and ongoing threats. Moreover, the level of network sentiment detected can be propagate to nearby home networks (e.g. the smart home networks of the apartments inside a building) to increase/decrease their level of security, thus creating a true in-line Intrusion Prevention System (IPS). The paper also presents a test bed for Smart Home to detect and counteract to different attacks against the IoT devices,,Wi-Fi and Ethernet connections .


2018 ◽  
Vol 24 (3) ◽  
pp. 913-924 ◽  
Author(s):  
Pradip Kumar Sharma ◽  
Jin Ho Park ◽  
Young-Sik Jeong ◽  
Jong Hyuk Park

2018 ◽  
Vol 10 (12) ◽  
pp. 125 ◽  
Author(s):  
Tommaso Pecorella ◽  
Laura Pierucci ◽  
Francesca Nizzi

A Smart Home is characterized by the presence of a huge number of small, low power devices, along with more classical devices. According to the Internet of Things (IoT) paradigm, all of them are expected to be always connected to the Internet in order to provide enhanced services. In this scenario, an attacker can undermine both the network security and the user’s security/privacy. Traditional security measures are not sufficient, because they are too difficult to setup and are either too weak to effectively protect the user or too limiting for the new services effectiveness. The paper suggests to dynamically adapt the security level of the smart home network according to the user perceived risk level what we have called network sentiment analysis. The security level is not fixed, established by a central system (usually by the Internet Service Provider) but can be changed with the users cooperation. The security of the smart home network is improved by a distributed firewalls and Intrusion Detection Systems both to the smart home side as to the Internet Service Provider side. These two parts must cooperate and integrate their actions for reacting dynamically to new and on going threats. Moreover, the level of network sentiment detected can be propagate to nearby home networks (e.g., the smart home networks of the apartments inside a building) to increase/decrease their level of security, thus creating a true in-line Intrusion Prevention System (IPS). The paper also presents a test bed for Smart Home to detect and counteract to different attacks against the IoT sensors, Wi-Fi and Ethernet connections.


Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2783 ◽  
Author(s):  
Linh-An Phan ◽  
Taehong Kim

Smart home is one of the most promising applications of the Internet of Things. Although there have been studies about this technology in recent years, the adoption rate of smart homes is still low. One of the largest barriers is technological fragmentation within the smart home ecosystem. Currently, there are many protocols used in a connected home, increasing the confusion of consumers when choosing a product for their house. One possible solution for this fragmentation is to make a gateway to handle the diverse protocols as a central hub in the home. However, this solution brings about another issue for manufacturers: compatibility. Because of the various smart devices on the market, supporting all possible devices in one gateway is also an enormous challenge. In this paper, we propose a software architecture for a gateway in a smart home system to solve the compatibility problem. By creating a mechanism to dynamically download and update a device profile from a server, the gateway can easily handle new devices. Moreover, the proposed gateway also supports unified control over heterogeneous networks. We implemented a prototype to prove the feasibility of the proposed gateway architecture and evaluated its performance from the viewpoint of message execution time over heterogeneous networks, as well as the latency for device profile downloads and updates, and the overhead needed for handling unknown commands.


Author(s):  
Shiwei Wang ◽  
Xiaoling Wu ◽  
Hainan Chen ◽  
Yanwen Wang ◽  
Daiping Li

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