Biblio

This research work consists in to design a system of occupancy simulation in smart homes based on IoT, in order to create configurations within a home that make look like the daily behavior of home inhabitants. Due to the high rate of burglary in uninhabited places, reaching an 9% in average in 2019 in the Chilean case, technologies have been involved with greater emphasis on improving security systems, where the implementation of the Internet of Things will allow rapid action against the intruder detection in those places. The proposed IoT system is based on a motion sensor, actuators as relays and lights, Arduino platform to control system, and a Amazon Echo virtual assistant to interface with inhabitants. The main contribution of this prototype security system is the integration of different IoT (Adafruit, IFTTT) and control platforms (Arduino uno and NodeMCU), virtual assistant (Alexa) and actuators, which has features that can be replicated in larger processes and with a larger number of devices. The results demonstrate that security system create an environment occupied by owners without to be inside home, through sensors and actuators.

In Forward Error Correction (FEC), redundant bits are added for detecting and correcting bit error which increases the bandwidth. To solve this issue we combined FEC method with higher order M-ary modulation to provide a bandwidth efficient system. An input bit stream is mapped to a bi-orthogonal code on different levels based on the code rates (4/16, 3/16, and 2/16) used. The jamming attack on wireless networks are mitigated by Chaotic Frequency Hopping (CFH) spread spectrum technique. In this paper, to achieve better data rate and to transmit the data in a secured manner we combined FEC and CFH technique, represented as Code and Chaotic Frequency Modulation (CCFM). In addition, two rate adaptation algorithms namely Static retransmission rate ARF (SARF) and Fast rate reduction ARF (FARF) are employed in CFH technique to dynamically adapt the code rate based on channel condition to reduce a packet retransmission. Symbol Error Rate (SER) performance of the system is analyzed for different code rate with the conventional OFDM in the presence AWGN and Rayleigh channel and the reliability of CFH method is tested under different jammer.

At present, the limitation of intangible cultural experience lies in the lack of long-term immersive cultural experience for users. In order to solve this problem, this study divides the process from the perspective of Freudian psychology and combines the theoretical research on intangible cultural heritage and flow experience to get the preliminary research direction. Then, based on the existing interactive experience cases of intangible cultural heritage, a set of method model of immersive interactive experience of intangible cultural heritage based on flow theory is summarized through user interviews in this research. Finally, through data verification, the model is proved to be correct. In addition, this study offers some important insights into differences between primary users and experienced users, and proposed specific guiding suggestions for immersive interactive experience design of intangible cultural heritage based on flow theory in the future.

The project uses 3D immersive technology to innovatively apply virtual reality technology to the monitoring field, and proposes the concept and technical route of remote 3D immersive intelligent control. A design scheme of a three-dimensional immersive remote somatosensory intelligent controller is proposed, which is applied to the remote three-dimensional immersive control of a crawler mobile robot, and the test and analysis of the principle prototype are completed.

Information system security is very important and very complicated, security is to prevent potential crisis. To detect both from external invasion behavior, also want to check the internal unauthorized behavior. Presented here ABHIDS hybrid intrusion detection system model, designed a component Agent, controller, storage, filter, manager component (database), puts forward a new detecting DDoS attacks (trinoo) algorithm and the implementation. ABHIDS adopts object-oriented design method, a study on intrusion detection can be used as a working mechanism of the algorithms and test verification platform.

Attack surface detection for the complex software is needed to find targets for the fuzzing, because testing the whole system with many inputs is not realistic. Researchers that previously applied taint analysis for dealing with different security tasks in the virtual machines did not examined how to apply it for attack surface detection. I.e., getting the program modules and functions, that may be affected by input data. We propose using taint tracking within a virtual machine and virtual machine introspection to create a new approach that can detect the internal module interfaces that can be fuzz tested to assure that software is safe or find the vulnerabilities.

In present scenario features like low-cost, power-efficientand easy-to-implement Wireless Sensor Networks (WSN’s) has become one of growing prospects.though, its security issues have become a popular topic of research nowadays. Specific attacks often experience the security issues as they easily combined with other attacks to destroy the network. In this paper, we discuss about detecting the particular attacks like Sybil, Black-holeand Denial of Service (DoS) attacks on WSNs. These networks are more vulnerable to them. We attempt to investigate the security measures and the applicability of the AODV protocol to detect and manage specific types of network attacks in VANET.The RSA algorithm is proposed here, as it is capable of detecting sensor nodes ormessages transmitted from sensor nodes to the base station and prevents network from being attacked by the source node. It also improves the security mechanism of the AODV protocol. This simulation set up is performed using MATLAB simulation tool

Power quality gains more and more attentions because disturbances in power quality may damage equipment security, power availability and system reliability in power system. Detection and classification of the power quality disturbances is the first step before taking measures to lessen their harmful effects. Common methods to classify power quality disturbances includes signal processing methods, machine learning methods and deep learning methods. Signal processing methods are good at feature extraction, while machine learning methods and deep learning methods are expert in multi-classification tasks. Via combing their respective advantages, this paper proposes a combined method based on variational mode decomposition and convolutional neural networks, which needs a small quantity of samples but achieves high classification precision. The proposed method is proved to be a qualified and competitive scheme for the detection and classification of power quality disturbances.

Optical time domain reflectometry (OTDR) plays an important role in optical fiber communications. To improve the performance of OTDR, we propose a method based on the Variational Mode Decomposition (VMD) and Hilbert transform (HT) for fiber events detection. Firstly, the variational mode decomposition is applied to decompose OTDR data into some intrinsic mode functions (imfs). To determine the decomposition mode number in VMD, an adaptive estimation method is introduced. Secondly, the Hilbert transform is utilized to obtain the instantaneous amplitude of the imf for events localization. Finally, the Dynamic Time Warping (DTW) is used for identifying the type of event. Experimental results show that the proposed method can locate events accurately. Compared with the Short-Time Fourier Transform (STFT) method, the VMD-HT method presents a higher accuracy in events localization, which indicates that the method is effective and applicable.

In power networks, it is important to detect a cyber attack. In this paper, we propose a detection method of false data injection (FDI) attacks. FDI attacks cannot be detected from the estimation error in power networks. The proposed method is based on the distributed state estimation, and is used the tentative estimated state. The proposed method is demonstrated by a numerical example on the IEEE 14-bus system.

The false data injection attacks (FDIAs) in smart grids can offset the power measurement data and it can bypass the traditional bad data detection mechanism. To solve this problem, a new detection mechanism called cosine similarity ratio which is based on the dynamic estimation algorithm of square root cubature Kalman filter (SRCKF) is proposed in this paper. That is, the detection basis is the change of the cosine similarity between the actual measurement and the predictive measurement before and after the attack. When the system is suddenly attacked, the actual measurement will have an abrupt change. However, the predictive measurement will not vary promptly with it owing to the delay of Kalman filter estimation. Consequently, the cosine similarity between the two at this moment has undergone a change. This causes the ratio of the cosine similarity at this moment and that at the initial moment to fluctuate considerably compared to safe operation. If the detection threshold is triggered, the system will be judged to be under attack. Finally, the standard IEEE-14bus test system is used for simulation experiments to verify the effectiveness of the proposed detection method.

Power grids are the most extensive man-made systems that are difficult to control and monitor. With the development of conventional power grids and moving toward smart grids, power systems have undergone vast changes since they use the Internet to transmit information and control commands to different parts of the power system. Due to the use of the Internet as a basic infrastructure for smart grids, attackers can sabotage the communication networks and alter the measurements. Due to the complexity of the smart grids, it is difficult for the network operator to detect such cyber-attacks. The attackers can implement the attack in a manner that conventional Bad Data detection (BDD) systems cannot detect since it may not violate the physical laws of the power system. This paper uses the cross wavelet transform (XWT) to detect stealth false data injections attacks (FDIAs) against state estimation (SE) systems. XWT can capture the coherency between measurements of adjacent buses and represent it in time and frequency space. Then, we train a machine learning classification algorithm to distinguish attacked measurements from normal measurements by applying a feature extraction technique.

Memristive nano crossbar array has paved the way for high density memories but in a very low power environment. But such high density circuits face multiple problems at the time of implementation. The sneak path problem in crossbar array is one such problem which causes difficulty in distinguishing the logical states of the memristors. On the other hand, hardware Trojan causes malfunctioning of the circuit or performance degradation. If any of these are present in the nano crossbar, it is difficult to identify whether the performance degradation is due to the sneak path problem or due to that of Hardware Trojan.This paper makes a comparative study of the sneak path problem and the hardware Trojan to understand the performance difference between both. It is observed that some parameters are affected by sneak path problem but remains unaffected in presence of Hardware Trojan and vice versa. Analyzing these parameters, we can classify whether the performance degradation is due to sneak path or due to Hardware Trojan. The experimental results well establish the proposed methods of detection of hardware Trojan in presence of sneak path in memristive nano crossbar circuits.

The ever-evolving computers has its implications on the data and information and the threats that they are exposed to. With the exponential growth of internet, the chances of data breach are highly likely as unauthorized and ill minded users find new ways to get access to the data that they can use for their plans. Most of the systems today have well designed measures that examine the information for any abnormal behavior (Zero Day Attacks) compared to what has been seen and experienced over the years. These checks are done based on a predefined identity (signature) of information. This is being termed as Intrusion Detection Systems (IDS). The concept of IDS revolves around validation of data and/or information and detecting unauthorized access attempts with an intention of manipulating data. High Performance Soft Computing (HPSC) aims to internalize cumulative adoption of traditional and modern attempts to breach data security and expose it to high scale damage and altercations. Our effort in this paper is to emphasize on the multifaceted tactic and rationalize important functionalities of IDS available at the disposal of HPSC.

We propose an information-theoretical method of attribution of literary texts, developed within the framework of information theory and mathematical statistics. Using the proposed method, the following two problems of disputed authorship in Russian and Soviet literature were investigated: i) the problem of false attribution of some novels to Nekrasov and ii) the problem of dubious attribution of two novels to Bulgakov. The research has shown the high efficiency of the data-compression method for attribution of literary texts.

Internet of Things (IoT) devices have rooted themselves in the everyday life of billions of people. Thus, researchers have applied automated bug finding techniques to improve their overall security. However, due to the difficulties in extracting and emulating custom firmware, black-box fuzzing is often the only viable analysis option. Unfortunately, this solution mostly produces invalid inputs, which are quickly discarded by the targeted IoT device and do not penetrate its code. Another proposed approach is to leverage the companion app (i.e., the mobile app typically used to control an IoT device) to generate well-structured fuzzing inputs. Unfortunately, the existing solutions produce fuzzing inputs that are constrained by app-side validation code, thus significantly limiting the range of discovered vulnerabilities.In this paper, we propose a novel approach that overcomes these limitations. Our key observation is that there exist functions inside the companion app that can be used to generate optimal (i.e., valid yet under-constrained) fuzzing inputs. Such functions, which we call fuzzing triggers, are executed before any data-transforming functions (e.g., network serialization), but after the input validation code. Consequently, they generate inputs that are not constrained by app-side sanitization code, and, at the same time, are not discarded by the analyzed IoT device due to their invalid format. We design and develop Diane, a tool that combines static and dynamic analysis to find fuzzing triggers in Android companion apps, and then uses them to fuzz IoT devices automatically. We use Diane to analyze 11 popular IoT devices, and identify 11 bugs, 9 of which are zero days. Our results also show that without using fuzzing triggers, it is not possible to generate bug-triggering inputs for many devices.

Strings are used to model genomic, natural language, and web activity data, and are thus often shared broadly. However, string data sharing has raised privacy concerns stemming from the fact that knowledge of length-k substrings of a string and their frequencies (multiplicities) may be sufficient to uniquely reconstruct the string; and from that the inference of such substrings may leak confidential information. We thus introduce the problem of protecting length-k substrings of a single string S by applying Differential Privacy (DP) while maximizing data utility for frequency-based mining tasks. Our theoretical and empirical evidence suggests that classic DP mechanisms are not suitable to address the problem. In response, we employ the order-k de Bruijn graph G of S and propose a sampling-based mechanism for enforcing DP on G. We consider the task of enforcing DP on G using our mechanism while preserving the normalized edge multiplicities in G. We define an optimization problem on integer edge weights that is central to this task and develop an algorithm based on dynamic programming to solve it exactly. We also consider two variants of this problem with real edge weights. By relaxing the constraint of integer edge weights, we are able to develop linear-time exact algorithms for these variants, which we use as stepping stones towards effective heuristics. An extensive experimental evaluation using real-world large-scale strings (in the order of billions of letters) shows that our heuristics are efficient and produce near-optimal solutions which preserve data utility for frequency-based mining tasks.

With the developing of computer technology, Convolutional Neural Network (CNN) has made big development in both application region and research field. However, CAPTCHA (one Turing Test to tell difference between computer and human) technology is also widely used in many websites verification process and it has received great attention from researchers. In this essay, we introduced the CNN based on tensorflow framework and use the MINIST data set which is used in handwritten digit recognition to analyze the parameters and the structure of the CNN model. Moreover, we use different activation functions and compares them with different epochs. We also analyze many problems during the experiment to make the original data and the result more accurate.

Autonomous vehicles (AVs) offer a wide range of promising benefits by reducing traffic accidents, environmental pollution, traffic congestion and land usage etc. However, to reap the intended benefits of AVs, it is inevitable that this technology should be trusted and accepted by the public. The consumer's substantial trust upon AVs will lead to its widespread adoption in the real-life. It is well understood that the preservation of strong security and privacy features influence a consumer's trust on a product in a positive manner. In this paper, we introduce a novel concept of digital labels for AVs to increase consumers awareness and trust regarding the security level of their vehicle. We present an architecture called Cybersecurity Box (CSBox) that leverages digital labels to display and inform consumers and passengers about cybersecurity status of the AV in use. The introduction of cybersecurity digital labels on the dashboard of AVs would attempt to increase the trust level of consumers and passengers on this promising technology.

With the rapid advancements in information technology, data security has become an indispensable component. Cryptography performs a significant role in establishing information security. Computational problems have been utilized extensively by cryptographers to construct digital signature schemes. Digital signature schemes offer security services such as confidentiality, authenticity, integrity, and non-repudiation of a message. This paper proposes a modification of the Dilithium signature scheme that is secure against unforgeability attack based on the hardness of lattice problems such as Learning With Errors and Short Integer Solution over lattices. Using the rejection sampling technique, data is sampled from a uniform distribution to generate keys that are expanded into a matrix. The keys are hashed and signed by the sender to generate a message, which is then accepted by the receiver upon verification. Finally, the security analysis for the proposed signature scheme is provided with a strong emphasis on the security of the secret key. We prove that the attacker cannot forge a signature on a message, and recommended parameters are proposed.

The envisioned massive deployment of network slices in 5G and beyond mobile systems makes the shift towards zero-touch, scalable and secure slice lifecycle management a necessity. This is to harvest the benefits of network slicing in enabling profitable services. These benefits will not be attained without ensuring a high level security of the created network slices and the underlying infrastructure, above all in a zero-touch automated fashion. In this vein, this paper presents the architecture of an innovative network slicing security orchestration framework, being developed within the EU H2020 MonB5G project. The framework leverages the potential of Security as a Service (SECaaS) and Artificial Intelligence (AI) to foster fully-distributed, autonomic and fine-grained management of network slicing security from the node level to the end-to-end and inter-slice levels.

Customer Relationship Management (CRM), Supply Chain Management (SCM), banking, and e-commerce are just a few of the internet-primarily based commercial enterprise programmes that make use of distributed computing generation. These programmes are the principal target of large-scale attacks known as DDoS attacks, which cause the denial of service (DoS) of resources to legitimate customers. Servers that provide dependable services to real consumers in distributed environments are vulnerable to such attacks, which send phoney requests that appear legitimate. Flash crowd, on the other hand, is a massive collection of traffic generated by flash events that imitate Distributed Denial of Service assaults. Detecting and distinguishing between Distributed Denial of Service assaults and flash crowds is a difficult problem to tackle, as is preventing DDoS attacks. Existing solutions are generally intended for DDoS attacks or flash crowds, and more research is required to have a thorough understanding. This study presents a technique for distinguishing between different types of Distributed Denial of Service attacks and Flash Crowds. This research work has suggested an approach to prevent DDOS attacks in addition to detecting and discriminating. The performance of the suggested technique is validated using NS-2 simulations.

CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart) is a widely used challenge-measures to distinguish humans and computer automated programs apart. Several existing CAPTCHAs are reliable for normal users, whereas visually impaired users face a lot of problems with the CAPTCHA authentication process. CAPTCHAs such as Google reCAPTCHA alternatively provides audio CAPTCHA, but many users find it difficult to decipher due to noise, language barrier, and accent of the audio of the CAPTCHA. Existing CAPTCHA systems lack user satisfaction on smartphones thus limiting its use. Our proposed system potentially solves the problem faced by visually impaired users during the process of CAPTCHA authentication. Also, our system makes the authentication process generic across users as well as platforms.

We propose an approach for filtering and prioritizing static code analysis alerts while these alerts are being reviewed by the developer. We construct a Prolog knowledge base that captures the data flow information in the source code as well as the reported alerts, their properties and associations with the data flow. The knowledge base is updated as the developer reviews the listed alerts and decides whether they point at an actual fault or not. These updates provide useful information since some of the alerts of the same type can be related in terms of their root cause. Hence, dynamically updated knowledge base can be queried to eliminate or prioritize the remaining alerts in the review list. We present a motivating example to illustrate the approach and its automation by integrating a set of tools.

The latest security methods are based on biometric features. The electrocardiogram is increasingly used in such systems because it provides biometric features that are difficult to falsify. This paper aims to study the use of the electrocardiogram together with the Convolutional Neural Networks, in order to identify the subjects based on the ECG signal and to improve the security. In this study, we used the Fantasia database, available on the PhysioNet platform, which contains 40 ECG recordings. The ECG signal is pre-processed, and then spectrograms are generated for each ECG signal. Spectrograms are applied to the input of several architectures of Convolutional Neural Networks like Inception-v3, Xception, MobileNet and NasNetLarge. An analysis of performance metrics reveals that the subject identification method based on ECG signal and CNNs provides remarkable results. The best accuracy value is 99.5% and is obtained for Inception-v3.