MEC-Based Jamming-Aided Anti-Eavesdropping with Deep Reinforcement Learning for WBANs

Wireless body area network (WBAN) suffers secure challenges, especially the eavesdropping attack, due to constraint resources. In this article, deep reinforcement learning (DRL) and mobile edge computing (MEC) technology are adopted to formulate a DRL-MEC-based jamming-aided anti-eavesdropping (DMEC-JAE) scheme to resist the eavesdropping attack without considering the channel state information. In this scheme, a MEC sensor is chosen to send artificial jamming signals to improve the secrecy rate of the system. Power control technique is utilized to optimize the transmission power of both the source sensor and the MEC sensor to save energy. The remaining energy of the MEC sensor is concerned to ensure routine data transmission and jamming signal transmission. Additionally, the DMEC-JAE scheme integrates with transfer learning for a higher learning rate. The performance bounds of the scheme concerning the secrecy rate, energy consumption, and the utility are evaluated. Simulation results show that the DMEC-JAE scheme can approach the performance bounds with high learning speed, which outperforms the benchmark schemes.

A Key-Based Mutual Authentication Framework for Mobile Contactless Payment System Using Authentication Server

This paper presents a framework for mutual authentication between a user device and a point of sale (POS) machine using magnetic secure transmission (MST) to prevent the wormhole attack in Samsung pay. The primary attribute of this method is authenticating the POS terminals by an authentication server to bind the generated token to a single POS machine. To secure the system from eavesdropping attack, the data transmitted between the user device and the machine is encrypted by using the Elgamal encryption method. The keys used in the method are dynamic in nature. Furthermore, comparison and security analysis are presented with previously proposed systems.

Achieving Secure and Privacy-Preserving in Mobile Social Networks

This chapter proposes a security framework for achieving secure and privacy-preserving mobile social networks named ASPP. Based on the cooperative neighbor, reactive routing protocol, and short signatures technique, the proposed scheme can not only detect and avoid but also can preserve the message privacy against elemental attacks and compound attacks. In addition, ASPP is robust against eavesdropping attack, wormhole attack, packet analysis attack, packet tracing attack, and replay attack. Extensive analyses and experiments are given to demonstrate its high detection rate and practicability under various scenarios.

An advance encryption standard cryptosystem in IoT transaction

IoT create an ecosystem that can be useful to the world with its various services. That make the security of IoT is more important. This paper presents a proposed technique to secure data transaction from IoT device to other node using cryptography technique. AES cryptography and IoT device model ESP8266 is used as a client to send data to a server via HTTP protocol. Security performance matrices used eavesdropping attack with Wireshark sniffing and brute-force attack. will be simulated to the proposed method in order to ensure if there is any possibility it can be cracked using those attacks and to evaluate the performance of ESP8266, several experiments were be conducted. Result gather from this experiment was evaluated based on processing time to see the effectiveness of the platform compare to different data size used while adapting this technique.

Security Aware Resource Allocation, Scheduling for Cognitive-NOMA Network

The demand of real-time wireless communication is increasing drastically where users demand for better Quality of Service (QoS) for various applications. In order to satisfy the communication requirement, communication spectrum must be utilized efficiently and all the available resource must be allocated appropriately. Recently, the cognitive radio network has gained attraction in this field of communication due to its efficient nature of spectrum sensing and sharing. However, the interference between primary and secondary user is a tedious task which can degrade the performance. Hence, multiple-access schemes are introduced and Non-Orthogonal Multiple Access (NOMA) is considered as most promising technique. Several models have been introduced based on the combined model of CR (Cognitive Radio) and NOMA but dynamic resource allocation, channel state information and eavesdropping are the most challenging task in this field. Moreover, user scheduling is also an important parameter which improves the resource utilization. In this work, we have focused on downlink communication for CR-NOMA network and presented a new approach for resource allocation and user scheduling by presenting an optimization strategy. In order to address the eavesdropping attack, the secrecy transmission rate and secrecy capacity are introduced. Finally, simulation study is carried out and the performance of proposed approach is compared with the existing techniques which shows that the proposed approach achieves higher throughput and improve outage probability.