Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things

Securing the Internet of Things (IoT) is a necessary milestone toward expediting the deployment of its applications and services. In particular, the functionality of the IoT devices is extremely dependent on the reliability of their message transmission. Cyber attacks such as data injection, eavesdropping, and man-in-the-middle threats can lead to security challenges. Securing IoT devices against such attacks requires accounting for their stringent computational power and need for low-latency operations. In this paper, a novel deep learning method is proposed for dynamic watermarking of IoT signals to detect cyber attacks. The proposed learning framework, based on a long short-term memory (LSTM) structure, enables the IoT devices to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal. This method enables the IoT's cloud center, which collects signals from the IoT devices, to effectively authenticate the reliability of the signals. Furthermore, the proposed method prevents complicated attack scenarios such as eavesdropping in which the cyber attacker collects the data from the IoT devices and aims to break the watermarking algorithm. Simulation results show that, with an attack detection delay of under 1 second the messages can be transmitted from IoT devices with an almost 100% reliability.Comment: 6 pages, 9 figure

Password Based a Generalize Robust Security System Design Using Neural Network

Among the various means of available resource protection including biometrics, password based system is most simple, user friendly, cost effective and commonly used. But this method having high sensitivity with attacks. Most of the advanced methods for authentication based on password encrypt the contents of password before storing or transmitting in physical domain. But all conventional cryptographic based encryption methods are having its own limitations, generally either in terms of complexity or in terms of efficiency. Multi-application usability of password today forcing users to have a proper memory aids. Which itself degrades the level of security. In this paper a method to exploit the artificial neural network to develop the more secure means of authentication, which is more efficient in providing the authentication, at the same time simple in design, has given. Apart from protection, a step toward perfect security has taken by adding the feature of intruder detection along with the protection system. This is possible by analysis of several logical parameters associated with the user activities. A new method of designing the security system centrally based on neural network with intrusion detection capability to handles the challenges available with present solutions, for any kind of resource has presented

Unmasking Optical Chaotic Cryptosystems Based on Delayed Optoelectronic Feedback

29 páginas, 22 figuras, 3 tablas.The authors analyze the security of optical chaotic communication systems. The chaotic carrier is generated by a laser diode subject to delayed optoelectronic feedback. Transmitters with one and two fixed delay times are considered. A new type of neural networks, modular neural networks, is used to reconstruct the nonlinear dynamics of the transmitter from experimental time series in the single-delay case, and from numerical simulations in single and two-delay cases. The authors show that the complexity of the model does not increase when the delay time is increased, in spite of the very high dimension of the chaotic attractor. However, it is found that nonlinear dynamics reconstruction is more difficult when the feedback strength is increased. The extracted model is used as an unauthorized receiver to recover the message. Therefore, the authors conclude that optical chaotic cryptosystems based on optoelectronic feedback systems with several fixed time delays are vulnerable.This work was supported by CICYT (Spain) under Project TEC2009-14581-C02-02.Peer reviewe