Biblio

In order to control users’ access to the information system, it is necessary to develop a security system that can work in real time and easily reconfigure. This problem can be solved using a fuzzy logic. In this paper the authors propose a fuzzy distribution system for access to the student assessment system, which takes into account the level of user access, identifier and the risk of attack during the request. This approach allows process fuzzy or incomplete information about the user and implement a sufficient level of confidential information protection.

This paper presents a MATLAB Graphical User Interface (GUI) based tool that determines the performance evaluation metrics of the physically unclonable functions (PUFs). The PUFs are hardware security primitives which can be utilized in several hardware security applications like integrated circuits protection, device authentication, secret key generation, and hardware obfuscation. Like any other technology approach, PUFs evaluation requires testing different performance metrics, each of which can be determined by at least one mathematical equation. The proposed tool (PUFs Tool) reads the PUF instances’ output and then computes and generates the values of the main PUFs’ performance metrics: uniqueness, reliability, uniformity, and bit-aliasing. In addition, it generates a bar code for each PUF instance considered in the evaluation process. The PUFs Tool is designed and developed using the app designer of MATLAB software 2021b.

Vehicular Ad-Hoc Network (VANET) is a subcategory of Intelligent Transportation Systems (ITS) that allows vehicles to communicate with other vehicles and static roadside infrastructure. However, the integration of cyber and physical systems introduce many possible points of attack that make VANET vulnerable to cyber attacks. In this paper, we implemented a machine learning-based intrusion detection system that identifies False Data Injection (FDI) attacks on a vehicular network. A co-simulation framework between MATLAB and NS-3 is used to simulate the system. The intrusion detection system is installed in every vehicle and processes the information obtained from the packets sent by other vehicles. The packet is classified into either trusted or malicious using Support Vector Machines (SVM). The comparison of the performance of the system is evaluated in different scenarios using the following metrics: classification rate, attack detection rate, false positive rate, and detection speed. Simulation results show that the SVM-based IDS is able to provide high accuracy detection, low false positive rate, consequently improving the traffic congestion in the simulated highway.

The expression of cyber-attacks on communication links in smart grids has emerged recently. In microgrids, cooperation between agents through communication links is required, thus, microgrids can be considered as cyber-physical-systems and they are vulnerable to cyber-attack threats. Cyber-attacks can cause damages in control systems, therefore, the resilient control methods are necessary. In this paper, a resilient control approach against false data injection attack is proposed for secondary control of DC microgrids. In the proposed framework, a PI controller with an adjustable gain is utilized to eliminate the injected false data. The proposed control method is employed for both sensor and link attacks. Convergence analysis of the measurement sensors and the secondary control objectives under the studied control method is performed. Finally, a DC microgrid with four units is built in Matlab/Simulink environment to verify the proposed approach.

Encryption has become an important need for each and every data transmission. Large amount of delicate data is transferred regularly through different computer networks such as e-banking, email applications and file exchange. Cryptanalysis is study of analyzing the hidden information in the system. The process of cryptanalysis could be done by various features such as power, sound, electromagnetic radiation etc. Lightweight cryptography plays an important role in the IoT devices. It includes various appliances, vehicles, smart sensors and RFID-tags (RFID). PRESENT is one such algorithm, designed for resource constrained devices. This requires less memory and consumes less power. The project propounds a model in which the cryptographic keys are analyzed by the trace of power.

The purpose of the work is a formalized description of the method determining numerical expression of the danger from actions potentially implemented by an information security violator. The implementation of such actions may lead to a disruption of the ordered functioning of multilevel distributed automated process control systems, which indicates the importance of developing new adequate solutions for predicting attacks consequences. The analysis of the largest destructive effects on information security systems of critical objects is carried out. The most common methods of obtaining the value of the hazard quotient of information security violators' destructive actions are considered. Based on the known methods for determining the possible damage from attacks implemented by a potential information security violator, a new, previously undetected in open sources method for determining the hazard quotient of destructive actions of an information security violator has been proposed. In order to carry out experimental calculations by the proposed method, the authors developed the required software. The calculations results are presented and indicate the possibility of using the proposed method for modeling threats and information security violators when designing an information security system for automated process control systems.

In this paper, RBF-based multistage auto-encoders are used to detect IDS attacks. RBF has numerous applications in various actual life settings. The planned technique involves a two-part multistage auto-encoder and RBF. The multistage auto-encoder is applied to select top and sensitive features from input data. The selected features from the multistage auto-encoder is wired as input to the RBF and the RBF is trained to categorize the input data into two labels: attack or no attack. The experiment was realized using MATLAB2018 on a dataset comprising 175,341 case, each of which involves 42 features and is authenticated using 82,332 case. The developed approach here has been applied for the first time, to the knowledge of the authors, to detect IDS attacks with 98.80% accuracy when validated using UNSW-NB15 dataset. The experimental results show the proposed method presents satisfactory results when compared with those obtained in this field.

This research analyzes a seemingly malicious behavior known as a block withholding (BWH) attack between pools of cryptocurrency miners in Bitcoin-like systems featuring blockchain distributed databases. This work updates and builds on a seminal paper, The Miner's Dilemma, which studied a simplified scenario and showed that a BWH attack can be rational behavior that is profitable for the attacker. The new research presented here provides an in-depth profit analysis of a more complex and realistic BWH attack scenario, which includes mutual attacks between multiple, non-uniform Bitcoin mining pools. As a result of mathematical analysis and MATLAB modeling, this paper illustrates the Nash equilibrium conditions of a system of independent mining pools with varied mining rates and computes the equilibrium rates of mutual BWH attack. The analysis method quantifies the additional profit the largest pools extract from the system at the expense of the smaller pools. The results indicate that while the presence of BWH is a net negative for smaller pools, they must participate in BWH to maximize their remaining profits, and the results quantify the attack rates the smaller pools must maintain. Also, the smallest pools maximize profit by not attacking at all-that is, retaliation is not a rational move for them.

Occurrence of faults in power systems is unavoidable but their timely recognition and location enhances the reliability and security of supply; thereby resulting in economic gain to consumers and power utility alike. Distribution Network (DN) is made smarter by the introduction of sensors and computers into the system. In this paper, detection and classification of faults in DN using Artificial Neural Network (ANN) is emphasized. This is achieved through the employment of Back Propagation Algorithm (BPA) of the Feed Forward Neural Network (FFNN) using three phase voltages and currents as inputs. The simulations were carried out using the MATLAB® 2017a. ANN with various hidden layers were analyzed and the results authenticate the effectiveness of the method.

Face detection is an application widely used in video surveillance systems and it is the first step for subsequent applications such as monitoring and recognition. For facial detection, there are a series of algorithms that allow the face to be extracted in a video image, among which are the Viola & Jones waterfall method and the method by geometric models using the Hausdorff distance. In this article, both algorithms are theoretically analyzed and the best one is determined by efficiency and resource optimization. Considering the most common problems in the detection of faces in a video surveillance system, such as the conditions of brightness and the angle of rotation of the face, tests have been carried out in 13 different scenarios with the best theoretically analyzed algorithm and its combination with another algorithm The images obtained, using a digital camera in the 13 scenarios, have been analyzed using Matlab code of the Viola & Jones and Viola & Jones algorithm combined with the Kanade-Lucas-Tomasi algorithm to add the feature of completing the tracking of a single object. This paper presents the detection percentages, false positives and false negatives for each image and for each simulation code, resulting in the scenarios with the most detection problems and the most accurate algorithm in face detection.

The traditional electricity distribution system is rapidly shifting from the passive infrastructure to a more active infrastructure, giving rise to a smart grid. In this project an active electricity distribution network and its components have been studied. A 14-node SCADA-based active distribution network model has been proposed for managing this emerging network infrastructure to ensure reliability and protection of the network The proposed model was developed using matlab /simulink software and the fuzzy logic toolbox. Surge arresters and circuit breakers were modelled and deployed in the network at different locations for protection and isolation of fault conditions. From the reliability analysis of the proposed model, the failure rate and outage hours were reduced due to better response of the system to power fluctuations and fault conditions.

Trust management is a promising alternative solution to different complex security algorithms for Underwater Wireless Sensor Networks (UWSN) applications due to its several resource constraint behaviour. In this work, we have proposed a trust management model to improve location privacy of the UWSN. Adaptive Neuro Fuzzy Inference System (ANFIS) has been exploited to evaluate trustworthiness of a sensor node. Also Markov Decision Process (MDP) has been considered. At each state of the MDP, a sensor node evaluates trust behaviour of forwarding node utilizing the FIS learning rules and selects a trusted node. Simulation has been conducted in MATLAB and simulation results show that the detection accuracy of trustworthiness is 91.2% which is greater than Knowledge Discovery and Data Mining (KDD) 99 intrusion detection based dataset. So, in our model 91.2% trustworthiness is necessary to be a trusted node otherwise it will be treated as a malicious or compromised node. Our proposed model can successfully eliminate the possibility of occurring any compromised or malicious node in the network.

Distributed Generation Photovoltaic Systems -DGPV - connected to the power distribution grid through electronic inverters can contribute, in an aggregate scenario, to the performance of several power system control functions, notably in self-healing and voltage regulation along a distribution feeder. This paper proposes the use of an optimization method for voltage regulation, focused on reactive power injection control, based on a comprehensive architecture model that coordinates multiple photovoltaic distributed sources to support grid reconfiguration after self-healing action. A sensitivity analysis regarding the performance of voltage regulation, based on a co-simulation of PSCAD and MatLab, shows the effectiveness of using dispersed generation sources to assist grid reconfiguration after disturbances caused by severe faults.

The existing radial topology makes the power system less reliable since any part in the system failure will disrupt electrical power delivery in the network. The increasing security concerns, electrical energy theft, and present advancement in Information and Communication Technologies are some factors that led to modernization of power system. In a smart grid, a network of smart sensors offers numerous opportunities that may include monitoring of power, consumer-side energy management, synchronization of dispersed power storage, and integrating sources of renewable energy. Smart sensor networks are low cost and are ease to deploy hence they are favorable contestants for deployment smart power grids at a larger scale. These networks will result in a colossal volume of dissimilar range of data that require an efficient processing and analyzing process in order to realize an efficient smart grid. The existing technology can be used to collect data but dealing with the collected information proficiently as well as mining valuable material out of it remains challenging. The paper investigates communication technologies that maybe deployed in a smart grid. In this paper simulations results for the Additive White Gaussian Noise (AWGN) channel are illustrated. We propose a model and a communication network domain riding on the power system domain. The model was interrogated by simulation in MATLAB.

This paper focuses on optimizing the sigmoid filter for detecting Low-Rate DoS attacks. Though sigmoid filter could help for detecting the attacker, it could severely affect the network efficiency. Unlike high rate attacks, Low-Rate DoS attacks such as ``Shrew'' and ``New Shrew'' are hard to detect. Attackers choose a malicious low-rate bandwidth to exploit the TCP's congestion control window algorithm and the re-transition timeout mechanism. We simulated the attacker traffic by editing using NS3. The Sigmoid filter was used to create a threshold bandwidth filter at the router that allowed a specific bandwidth, so when traffic that exceeded the threshold occurred, it would be dropped, or it would be redirected to a honey-pot server, instead. We simulated the Sigmoid filter using MATLAB and took the attacker's and legitimate user's traffic generated by NS-3 as the input for the Sigmoid filter in the MATLAB. We run the experiment three times with different threshold values correlated to the TCP packet size. We found the probability to detect the attacker traffic as follows: the first was 25%, the second 50% and the third 60%. However, we observed a drop in legitimate user traffic with the following probabilities, respectively: 75%, 50%, and 85%.

In order to investigate the relationship and effect on the performance of magnetic modulator among applied DC current, excitation source, excitation loop current, sensitivity and induced voltage of detecting winding, this paper measured initial permeability, maximum permeability, saturation magnetic induction intensity, remanent magnetic induction intensity, coercivity, saturated magnetic field intensity, magnetization curve, permeability curve and hysteresis loop of main core 1J85 permalloy of magnetic modulator based on ballistic method. On this foundation, employ curve fitting tool of MATLAB; adopt multiple regression method to comprehensively compare and analyze the sum of squares due to error (SSE), coefficient of determination (R-square), degree-of-freedom adjusted coefficient of determination (Adjusted R-square), and root mean squared error (RMSE) of fitting results. Finally, establish B-H curve mathematical model based on the sum of arc-hyperbolic sine function and polynomial.

Conventional intrusion detection systems for smart grid communications rely heavily on static based attack detection techniques. In essence, signatures created from historical data are compared to incoming network traffic to identify abnormalities. In the case of attacks where no historical data exists, static based approaches become ineffective thus relinquishing system resilience and stability. Moving target defense (MTD) has shown to be effective in discouraging attackers by introducing system entropy to increase exploit costs. Increase in exploit cost leads to a decrease in profitability for an attacker. In this paper, a Moving Target Defense Intrusion Detection System (MTDIDS) is proposed for smart grid IPv6 based advanced metering infrastructure. The advantage of MTDIDS is the ability to detect anomalies across moving targets by means of planar keys thereupon increasing detection rate. Evaluation of MTDIDS was carried out in a smart grid advanced metering infrastructure simulated in MATLAB.

The video streaming between the sender and the receiver involves multiple unsecured hops where the video data can be illegally copied if the nodes run malicious forwarding logic. This paper introduces a novel method to stream video data through dual channels using dual data paths. The frames' pixels are also scrambled. The video frames are divided into two frame streams. At the receiver side video is re-constructed and played for a limited time period. As soon as small chunk of merged video is played, it is deleted from video buffer. The approach has been tried to formalize and initial simulation has been done over MATLAB. Preliminary results are optimistic and a refined approach may lead to a formal designing of network layer routing protocol with corrections in transport layer.

In our digital world internet is a widespread channel for transmission of information. Information that is transmitted can be in form of messages, images, audios and videos. Due to this escalating use of digital data exchange cryptography and network security has now become very important in modern digital communication network. Cryptography is a method of storing and transmitting data in a particular form so that only those for whom it is intended can read and process it. The term cryptography is most often associated with scrambling plaintext into ciphertext. This process is called as encryption. Today in industrial processes images are very frequently used, so it has become essential for us to protect the confidential image data from unauthorized access. In this paper Advanced Encryption Standard (AES) which is a symmetric algorithm is used for encryption and decryption of image. Performance of Advanced Encryption Standard algorithm is further enhanced by adding a key stream generator W7. NIOS II soft core processor is used for implementation of encryption and decryption algorithm. A system is designed with the help of SOPC (System on programmable chip) builder tool which is available in QUARTUS II (Version 10.1) environment using NIOS II soft core processor. Developed single core system is implemented using Altera DE2 FPGA board (Cyclone II EP2C35F672). Using MATLAB the image is read and then by using DWT (Discrete Wavelet Transform) the image is compressed. The image obtained after compression is now given as input to proposed AES encryption algorithm. The output of encryption algorithm is given as input to decryption algorithm in order to get back the original image. The implementation of which is done on the developed single core platform using NIOS II processor. Finally the output is analyzed in MATLAB by plotting histogram of original and encrypted image.

Security of secret data has been a major issue of concern from ancient time. Steganography and cryptography are the two techniques which are used to reduce the security threat. Cryptography is an art of converting secret message in other than human readable form. Steganography is an art of hiding the existence of secret message. These techniques are required to protect the data theft over rapidly growing network. To achieve this there is a need of such a system which is very less susceptible to human visual system. In this paper a new technique is going to be introducing for data transmission over an unsecure channel. In this paper secret data is compressed first using LZW algorithm before embedding it behind any cover media. Data is compressed to reduce its size. After compression data encryption is performed to increase the security. Encryption is performed with the help of a key which make it difficult to get the secret message even if the existence of the secret message is reveled. Now the edge of secret message is detected by using canny edge detector and then embedded secret data is stored there with the help of a hash function. Proposed technique is implemented in MATLAB and key strength of this project is its huge data hiding capacity and least distortion in Stego image. This technique is applied over various images and the results show least distortion in altered image.

The emergence of new technologies, in addition with the popularization of mobile devices and wireless communication systems, demands a variety of requirements that current Internet is not able to comply adequately. In this scenario, the innovative information-centric Entity Title Architecture (ETArch), a Future Internet (FI) clean slate approach, was design to efficiently cope with the increasing demand of beyond-IP networking services. Nevertheless, despite all ETArch capabilities, it was not projected with reliable networking functions, which limits its operability in mobile multimedia networking, and will seriously restrict its scope in Future Internet scenarios. Therefore, our work extends ETArch mobility control with advanced quality-oriented mobility functions, to deploy mobility prediction, Point of Attachment (PoA) decision and handover setup meeting both session quality requirements of active session flows and current wireless quality conditions of neighbouring PoA candidates. The effectiveness of the proposed additions were confirmed through a preliminary evaluation carried out by MATLAB, in which we have considered distinct applications scenario, and showed that they were able to outperform the most relevant alternative solutions in terms of performance and quality of service.
 

The notion of trust is considered to be the cornerstone on patient-psychiatrist relationship. Thus, a trustfully background is fundamental requirement for provision of effective Ubiquitous Healthcare (UH) service. In this paper, the issue of Trust Evaluation of UH Providers when register UH environment is addressed. For that purpose a novel trust evaluation method is proposed, based on cloud theory, exploiting User Profile attributes. This theory mimics human thinking, regarding trust evaluation and captures fuzziness and randomness of this uncertain reasoning. Two case studies are investigated through simulation in MATLAB software, in order to verify the effectiveness of this novel method.