Neighbor Node Trust based Intrusion Detection System for WSN

Qian

IEEE Trans. Cloud Comput.

et al. 2020

With the popularity of wearable devices, along with the development of clouds and cloudlet technology, there has been increasing need to provide better medical care. The processing chain of medical data mainly includes data collection, data storage and data sharing, etc. Traditional healthcare system often requires the delivery of medical data to the cloud, which involves users' sensitive information and causes communication energy consumption. Practically, medical data sharing is a critical and challenging issue. Thus in this paper, we build up a novel healthcare system by utilizing the flexibility of cloudlet. The functions of cloudlet include privacy protection, data sharing and intrusion detection. In the stage of data collection, we first utilize Number Theory Research Unit (NTRU) method to encrypt user's body data collected by wearable devices. Those data will be transmitted to nearby cloudlet in an energy efficient fashion. Secondly, we present a new trust model to help users to select trustable partners who want to share stored data in the cloudlet. The trust model also helps similar patients to communicate with each other about their diseases. Thirdly, we divide users' medical data stored in remote cloud of hospital into three parts, and give them proper protection. Finally, in order to protect the healthcare system from malicious attacks, we develop a novel collaborative intrusion detection system (IDS) method based on cloudlet mesh, which can effectively prevent the remote healthcare big data cloud from attacks. Our experiments demonstrate the effectiveness of the proposed scheme.

Section: Collaborative Ids Based On Cloudlet Meshmentioning

confidence: 99%

Privacy Protection and Intrusion Avoidance for Cloudlet-Based Medical Data Sharing

Qian

IEEE Trans. Cloud Comput.

et al. 2020

Security and Communication Networks

“…In this way, even larger damage can be done to the network and its nodes. (iii) Jamming (see Bao et al [30]; Sajjad et al [31] [36]; Da Silva et al [35]): the attacker uses two or more malicious nodes which are linked by a nonlegitimate connection, a so-called tunnel, faking a route that is shorter than the original one within the network. Packets passing the tunnel can then be used for other kinds of attack.…”

Section: Responses On Attacksmentioning

confidence: 99%

Recent Advancements in Intrusion Detection Systems for the Internet of Things

Khan

Herrmann

2019

Many Internet of Things (IoT) systems run on tiny connected devices that have to deal with severe processor and energy restrictions. Often, the limited processing resources do not allow the use of standard security mechanisms on the nodes, making IoT applications quite vulnerable to different types of attacks. This holds particularly for intrusion detection systems (IDS) that are usually too resource-heavy to be handled by small IoT devices. Thus, many IoT systems are not sufficiently protected against typical network attacks like Denial-of-Service (DoS) and routing attacks. On the other side, IDSs have already been successfully used in adjacent network types like Mobile Ad hoc Networks (MANET), Wireless Sensor Networks (WSN), and Cyber-Physical Systems (CPS) which, in part, face limitations similar to those of IoT applications. Moreover, there is research work ongoing that promises IDSs that may better fit to the limitations of IoT devices. In this article, we will give an overview about IDSs suited for IoT networks. Besides looking on approaches developed particularly for IoT, we introduce also work for the three similar network types mentioned above and discuss if they are also suitable for IoT systems. In addition, we present some suggestions for future research work that could be useful to make IoT networks more secure.

“…Sajjad et al developed an IDS for WSNs based on the trust level of the neighboring nodes. They investigated the network statistics and malicious node behaviors and reported the successful detection of selective forwarding, Hello flooding, and jamming attacks by their IDS.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient weighted trust‐based malicious node detection scheme for wireless sensor networks

Zawaideh¹,

Salamah

2018

Int J Communication

The aim of wireless sensor networks (WSNs) is to gather sensor data from a monitored environment. However, the collected or reported information might be falsified by faults or malicious nodes. Hence, identifying malicious nodes in an effective and timely manner is essential for the network to function properly and reliably. Maliciously behaving nodes are usually detected and isolated by reputation and trust-based schemes before they can damage the network. In this paper, we propose an efficient weighted trust-based malicious node detection (WT-MND) scheme that can detect malicious nodes in a clustered WSN.The node behaviors are realistically treated by accounting for false-positive and false-negative instances. The simulation results confirm the timely identification and isolation of maliciously behaving nodes by the WT-MND scheme. The effectiveness of the proposed scheme is afforded by the adaptive trustupdate process, which implicitly performs trust recovery of temporarily malfunctioning nodes and computes a different trust-update factor for each node depending on its behavior. The proposed scheme is more effective and scalable than the related schemes in the literature, as evidenced by its higher detection ratio (DR) and lower misdetection ratio (MDR), which only slightly vary with the network's size. Moreover, the scheme sustains its efficient characteristics without significant power consumption overheads.