Configurable Secured Adaptive Routing Protocol for Mobile Wireless Sensor Networks

This paper aims at designing, building, and simulating a secured routing protocol to defend against packet dropping attacks in mobile WSNs (MWSNs). This research addresses the gap in the literature by proposing Configurable Secured Adaptive Routing Protocol (CSARP). CSARP has four levels of protection to allow suitability for different types of network applications. The protocol allows the network admin to configure the required protection level and the ratio of cluster heads to all nodes. The protocol has an adaptive feature, which allows for better protection and preventing the spread of the threats in the network. The conducted CSARP simulations with different conditions showed the ability of CSARP to identify all malicious nodes and remove them from the network. CSARP provided more than 99.97% packets delivery rate with 0% data packet loss in the existence of 3 malicious nodes in comparison with 3.17% data packet loss without using CSARP. When compared with LEACH, CSARP showed an improvement in extending the lifetime of the network by up to 39.5%. The proposed protocol has proven to be better than the available security solutions in terms of configurability, adaptability, optimization for MWSNs, energy consumption optimization, and the suitability for different MWSNs applications and conditions

Configurable Secured Adaptive Routing Protocol for Mobile Wireless Sensor Networks

This paper aims at designing, building, and simulating a secured routing protocol to defend against packet dropping attacks in mobile WSNs (MWSNs). This research addresses the gap in the literature by proposing Configurable Secured Adaptive Routing Protocol (CSARP). CSARP has four levels of protection to allow suitability for different types of network applications. The protocol allows the network admin to configure the required protection level and the ratio of cluster heads to all nodes. The protocol has an adaptive feature, which allows for better protection and preventing the spread of the threats in the network. The conducted CSARP simulations with different conditions showed the ability of CSARP to identify all malicious nodes and remove them from the network. CSARP provided more than 99.97% packets delivery rate with 0% data packet loss in the existence of 3 malicious nodes in comparison with 3.17% data packet loss without using CSARP. When compared with LEACH, CSARP showed an improvement in extending the lifetime of the network by up to 39.5%. The proposed protocol has proven to be better than the available security solutions in terms of configurability, adaptability, optimization for MWSNs, energy consumption optimization, and the suitability for different MWSNs applications and conditions

Lightweight identity based online/offline signature scheme for wireless sensor networks

Data security is one of the issues during data exchange between two sensor nodes in wireless sensor networks (WSN). While information flows across naturally exposed communication channels, cybercriminals may access sensitive information. Multiple traditional reliable encryption methods like RSA encryption-decryption and Diffie–Hellman key exchange face a crisis of computational resources due to limited storage, low computational ability, and insufficient power in lightweight WSNs. The complexity of these security mechanisms reduces the network lifespan, and an online/offline strategy is one way to overcome this problem. This study proposed an improved identity-based online/offline signature scheme using Elliptic Curve Cryptography (ECC) encryption. The lightweight calculations were conducted during the online phase, and in the offline phase, the encryption, point multiplication, and other heavy measures were pre-processed using powerful devices. The proposed scheme uniquely combined the Inverse Collusion Attack Algorithm (CAA) with lightweight ECC to generate secure identitybased signatures. The suggested scheme was analyzed for security and success probability under Random Oracle Model (ROM). The analysis concluded that the generated signatures were immune to even the worst Chosen Message Attack. The most important, resource-effective, and extensively used on-demand function was the verification of the signatures. The low-cost verification algorithm of the scheme saved a significant number of valued resources and increased the overall network’s lifespan. The results for encryption/decryption time, computation difficulty, and key generation time for various data sizes showed the proposed solution was ideal for lightweight devices as it accelerated data transmission speed and consumed the least resources. The hybrid method obtained an average of 66.77% less time consumption and up to 12% lower computational cost than previous schemes like the dynamic IDB-ECC two-factor authentication key exchange protocol, lightweight IBE scheme (IDB-Lite), and Korean certification-based signature standard using the ECC. The proposed scheme had a smaller key size and signature size of 160 bits. Overall, the energy consumption was also reduced to 0.53 mJ for 1312 bits of offline storage. The hybrid framework of identity-based signatures, online/offline phases, ECC, CAA, and low-cost algorithms enhances overall performance by having less complexity, time, and memory consumption. Thus, the proposed hybrid scheme is ideally suited for a lightweight WSN

A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT

A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish–subscribe-based protocol for the communication of sensor or event data. The publish–subscribe strategy makes it more attractive for intruders and thus increases the number of possible attacks over MQTT. In this paper, we proposed a Deep Neural Network (DNN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using two different publicly available datasets, including (1) MQTT-IoT-IDS2020 and (2) a dataset with three different types of attacks, such as Man in the Middle (MitM), Intrusion in the network, and Denial of Services (DoS). The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the DNN-based model achieved 99.92%, 99.75%, and 94.94% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 97.08%, 98.12%, and 90.79%, respectively. On the other hand, the proposed DNN model attains the highest accuracy of 97.13% against LSTM and GRUs for the second dataset

A multivariant secure framework for smart mobile health application

This is an accepted manuscript of an article published by Wiley in Transactions on Emerging Telecommunications Technologies, available online: https://doi.org/10.1002/ett.3684 The accepted version of the publication may differ from the final published version.Wireless sensor network enables remote connectivity of technological devices such as smart mobile with the internet. Due to its low cost as well as easy availability of data sharing and accessing devices, the Internet of Things (IoT) has grown exponentially during the past few years. The availability of these devices plays a remarkable role in the new era of mHealth. In mHealth, the sensors generate enormous amounts of data and the context-aware computing has proven to collect and manage the data. The context aware computing is a new domain to be aware of context of involved devices. The context-aware computing is playing a very significant part in the development of smart mobile health applications to monitor the health of patients more efficiently. Security is one of the key challenges in IoT-based mHealth application development. The wireless nature of IoT devices motivates attackers to attack on application; these vulnerable attacks can be denial of service attack, sinkhole attack, and select forwarding attack. These attacks lead intruders to disrupt the application's functionality, data packet drops to malicious end and changes the route of data and forwards the data packet to other location. There is a need to timely detect and prevent these threats in mobile health applications. Existing work includes many security frameworks to secure the mobile health applications but all have some drawbacks. This paper presents existing frameworks, the impact of threats on applications, on information, and different security levels. From this line of research, we propose a security framework with two algorithms, ie, (i) patient priority autonomous call and (ii) location distance based switch, for mobile health applications and make a comparative analysis of the proposed framework with the existing ones.Published onlin

Defence against black hole and selective forwarding attacks for medical WSNs in the IoT

Wireless sensor networks (WSNs) are being used to facilitate monitoring of patients in hospital and home environments. These systems consist of a variety of different components/sensors and many processes like clustering, routing, security, and self-organization. Routing is necessary for medical-based WSNs because it allows remote data delivery and it facilitates network scalability in large hospitals. However, routing entails several problems, mainly due to the open nature of wireless networks, and these need to be addressed. This paper looks at two of the problems that arise due to wireless routing between the nodes and access points of a medical WSN (for IoT use): black hole and selective forwarding (SF) attacks. A solution to the former can readily be provided through the use of cryptographic hashes, while the latter makes use of a neighbourhood watch and threshold-based analysis to detect and correct SF attacks. The scheme proposed here is capable of detecting a selective forwarding attack with over 96% accuracy and successfully identifying the malicious node with 83% accuracy