Biometric Based Intrusion Detection System using Dempster-Shafer Theory for Mobile Ad hoc Network Security

In wireless mobile ad hoc network, mainly, two approaches are followed to protect the security such as prevention-based approaches and detection-based approaches. A Mobile Ad hoc Network (MANET) is a collection of autonomous wireless mobile nodes forming temporary network to interchange data (data packets) without using any fixed topology or centralized administration. In this dynamic network, each node changes its geographical position and acts as a router for forwarding packets to the other node. Current MANETs are basically vulnerable to different types of attacks. The multimodal biometric technology gives possible resolves for continuous user authentication and vulnerability in high security mobile ad hoc networks (MANETs). Dempster’s rule for combination gives a numerical method for combining multiple pieces of data from unreliable observers. This paper studies biometric authentication and intrusion detection system with data fusion using Dempster–Shafer theory in such MANETs. Multimodal biometric technologies are arrayed to work with intrusion detection to improve the limitations of unimodal biometric technique

Identification of Biometric-Based Continuous user Authentication and Intrusion Detection System for Cluster Based Manet

Mobile ad hoc is an infrastructure less dynamic network used in many applications; it has been targets of various attacks and makes security problems. This work aims to provide an enhanced level of security by using the prevention based and detection based approaches such as authentication and intrusion detection. The multi-model biometric technology is used for continuous authentication and intrusion detection in high security cluster based MANET. In this paper, an attempt has been made to combine continuous authentication and intrusion detection. In this proposed scheme, Dempster-Shafer theory is used for data fusion because more than one device needs to be chosen and their observation can be used to increase observation accuracy

No soldiers left behind: An IoT-based low-power military mobile health system design

© 2013 IEEE. There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-Time actuation of IoT equipment, and activation of emergency alarms through the inference of a user\u27s situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information

A Trust Model Based on Service Classification in Mobile Services

Internet of Things (IoT) and B3G/4G communication are promoting the pervasive mobile services with its advanced features. However, security problems are also baffled the development. This paper proposes a trust model to protect the user's security. The billing or trust operator works as an agent to provide a trust authentication for all the service providers. The services are classified by sensitive value calculation. With the value, the user's trustiness for corresponding service can be obtained. For decision, three trust regions are divided, which is referred to three ranks: high, medium and low. The trust region tells the customer, with his calculated trust value, which rank he has got and which authentication methods should be used for access. Authentication history and penalty are also involved with reasons.Comment: IEEE/ACM Internet of Things Symposium (IOTS), in conjunction with GreenCom 2010, IEEE, Hangzhou, China, December 18-20, 201

Privacy-Preserving Facial Recognition Using Biometric-Capsules

Indiana University-Purdue University Indianapolis (IUPUI)In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based recognition systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based Biometric-Capsule (BC) scheme. The BC scheme is provably secure, privacy-preserving, cancellable and interoperable in its secure feature fusion design. In this work, we demonstrate that the BC scheme is uniquely fit to secure state-of-the-art facial verification, authentication and identification systems. We compare the performance of unsecured, underlying biometrics systems to the performance of the BC-embedded systems in order to directly demonstrate the minimal effects of the privacy-preserving BC scheme on underlying system performance. Notably, we demonstrate that, when seamlessly embedded into a state-of-the-art FaceNet and ArcFace verification systems which achieve accuracies of 97.18% and 99.75% on the benchmark LFW dataset, the BC-embedded systems are able to achieve accuracies of 95.13% and 99.13% respectively. Furthermore, we also demonstrate that the BC scheme outperforms or performs as well as several other proposed secure biometric methods