Detecting and Extracting Illegal Signs from Video

This project focuses on developing an automated system to detect illegal signs in urban environments from videos. The system utilizes computer vision and machine learning techniques, specifically the YOLOv5 object detection framework, to accurately identify and locate illegal signs in video frames. It incorporates a verification process using Optical Character Recognition (OCR) to differentiate between legal and illegal signs based on the extracted text information. The system is designed as a user-friendly web application, allowing users to upload videos or images for analysis and receive comprehensive results. The system can achieve a detection accuracy of up to 78.6%. With this system, authorities can effectively manage and regulate illegal signs in urban areas, contributing to better urban landscapes

Modified recurrent equation-based cubic spline interpolation for missing data recovery in phasor measurement unit (PMU) [version 3; peer review: 2 approved, 1 not approved]

Background Smart grid systems require high-quality Phasor Measurement Unit (PMU) data for proper operation, control, and decision-making. Missing PMU data may lead to improper actions or even blackouts. While the conventional cubic interpolation methods based on the solution of a set of linear equations to solve for the cubic spline coefficients have been applied by many researchers for interpolation of missing data, the computational complexity increases non-linearly with increasing data size. Methods In this work, a modified recurrent equation-based cubic spline interpolation procedure for recovering missing PMU data is proposed. The recurrent equation-based method makes the computations of spline constants simpler. Using PMU data from the State Load Despatch Center (SLDC) in Madhya Pradesh, India, a comparison of the root mean square error (RMSE) values and time of calculation (ToC) is calculated for both methods. Results The modified recurrent relation method could retrieve missing values 10 times faster when compared to the conventional cubic interpolation method based on the solution of a set of linear equations. The RMSE values have shown the proposed method is effective even for special cases of missing values (edges, continuous missing values). Conclusions The proposed method can retrieve any number of missing values at any location using observed data with a minimal number of calculations

Driving event recognition using machine learning and smartphones [version 2; peer review: 2 approved]

Background: The lack of real-time monitoring is one of the reasons for the lack of awareness among drivers of their dangerous driving behavior. This work aims to develop a driver profiling system where a smartphone’s built-in sensors are used alongside machine learning algorithms to classify different driving behaviors. Methods: We attempt to determine the optimal combination of smartphone sensors such as accelerometer, gyroscope, and GPS in order to develop an accurate machine learning algorithm capable of identifying different driving events (e.g. turning, accelerating, or braking). Results: In our preliminary studies, we encountered some difficulties in obtaining consistent driving events, which had the potential to add “noise” to the observations, thus reducing the accuracy of the classification. However, after some pre-processing, which included manual elimination of extraneous and erroneous events, and with the use of the Convolutional Neural Networks (CNN), we have been able to distinguish different driving events with an accuracy of about 95%. Conclusions: Based on the results of preliminary studies, we have determined that the proposed approach is effective in classifying different driving events, which in turn will allow us to determine driver’s driving behavior

Intrusion detection framework for encrypted networks

Network-based Intrusion Detection Systems (NIDSs) monitor network traffic for signs of malicious activities that have the potential to disrupt entire network infrastructures and services. NIDS can only operate when the network traffic is available and can be extracted for analysis. However, with the growing use of encrypted networks such as Virtual Private Networks (VPNs) that encrypt and conceal network traffic, a traditional NIDS can no longer access network traffic for analysis. The goal of this research is to address this problem by proposing a detection framework that allows a commercial off-the-shelf NIDS to function normally in a VPN without any modification. One of the features of the proposed framework is that it does not compromise on the confidentiality afforded by the VPN. Our work uses a combination of Shamir’s secret-sharing scheme and randomised network proxies to securely route network traffic to the NIDS for analysis. The detection framework is effective against two general classes of attacks – attacks targeted at the network hosts or attacks targeted at framework itself. We implement the detection framework as a prototype program and evaluate it. Our evaluation shows that the framework does indeed detect these classes of attacks and does not introduce any additional false positives. Despite the increase in network overhead in doing so, the proposed detection framework is able to consistently detect intrusions through encrypted networks

Robust Watermarking Scheme Using Chinese Remainder Theorem-Based Error Correcting Codes

One of the more promising technologies that can be used to curtail the illegal usage of copyrighted contents is digital watermarking. A robust watermarking scheme using error correcting codes is proposed. In this block-based scheme for greyscale images, relationships between blocks of pixels are exploited to inconspicuously embed the watermark. Furthermore, watermark recovery is performed without the need of the original image watermark. The robustness of the watermarking scheme is tested for various image manipulation techniques

Multiple error detection and correction based on redundant residue number systems

This paper presents some results on multiple error detection and correction based on the Redundant Residue Number System (RRNS). RRNS is often used in parallel processing environments because of its ability to increase the robustness of information passing between the processors. The proposed multiple error correction scheme utilizes the Chinese Remainder Theorem (CRT) together with a novel algorithm that significantly simplifies the error correcting process for integers. An extension of the scheme further reduces the computational complexity without compromising its error correcting capability. Proofs and examples are provided for the coding technique

Anonymous Multi-Party Identification Without Certificates

Security and Privacy concerns are a growing problem in our internet world today in which privacy is not given enough concern when compared to security. For example, in building security systems depend upon identification schemes which is secure but offers no privacy to end users. There needs to be a way to guarantee the privacy of the people entering the building while maintaining building security. We design two schemes that can be used in cases like this where the privacy of the end user is guaranteed while maintaining the security of the scheme

Detecting attacks in encrypted networks using secret-sharing schemes\ud

Secret-sharing schemes describe methods to securely share a secret among a group of participants. A properly constructed secret-sharing scheme guarantees that the share belonging to one participant does not reveal anything about the shares of others or even the secret itself. Besides being used to distribute a secret, secret-sharing schemes have also been used in secure multi-party computations and redundant residue number systems for error correction codes. In this paper, we propose that the secret-sharing scheme be used as a primitive in a Network-based Intrusion Detection System (NIDS) to detect attacks in encrypted Networks. Encrypted networks such as Virtual Private Networks (VPNs) fully encrypt network traffic which can include both malicious and non-malicious traffic. Traditional NIDS cannot monitor such encrypted traffic. We therefore describe how our work uses a combination of Shamir's secret-sharing scheme and randomised network proxies to enable a traditional NIDS to function normally in a VPN environment.\u

Towards intrusion detection for encrypted networks

Traditionally, network-based Intrusion Detection Systems (NIDS) monitor network traffic for signs of malicious activities. However, with the growing use of Virtual Private Networks (VPNs) that encrypt network traffic, the NIDS can no longer analyse the encrypted data. This essentially negates any protection offered by the NIDS. Although the encrypted traffic can be decrypted at a network gateway for analysis, this compromises on data confidentiality. In this paper, we propose a detection framework which allows a traditional NIDS to continue functioning, without compromising the confidentiality afforded by the VPN. Our approach uses Shamir's secret-sharing scheme and randomised network proxies to enable detection of malicious activities in encrypted channels. Additionally, this approach is able to detect any malicious attempts to forge network traffic with the intention of evading detection. Our experiments show that the probability of a successful evasion is low, at about 0.98\% in the worst case. We implement our approach in a prototype and present some preliminary results. Overall, the proposed approach is able to consistently detect intrusions and does not introduce any additional false positives

Chicken Disease Detection by Identifying Chicken Droppings

Taking care of chicken by manually for a big farm is not that easy. Chicken can be affected by many kinds of disease. To find out the disease before it spread among all chickens and give proper medical treatment is very important and also very sensitive. Disease also affects chicken’s digestive system which results in changing color of chicken droppings. By detecting chicken droppings using deep learning, it can easily identify chicken disease. In this study, we propose a solution based on the best real-time object detection algorithm You only look once version 7 (YOLOv7) to detect chicken disease by identifying chicken droppings so that farmers can aware of about disease affected chicken