Hardware implementation of naive bayes classifier for malware detection

Naïve bayes classifier is a probabilistic supervised machine learning algorithm, that can be launched on most general-purpose devices to solve wide range of classification problems. However, when it comes to real time applications, the general-purpose devices are limited in term of their computational throughput, thus this algorithm couldn’t be used for that purpose. The aim of this project is to accelerate this algorithm in hardware environment to improve its performance by exploring its hidden concurrency and map it into parallel hardware as an optimized IP package, suitable for FPGA-SoC applications. Thus, it could be used as a middle box system for real time malware detection. In order for the proposed hardware to meet the requirements of this research, it should be able to handle both training, and inference part in hardware, and also should be able to receive a flow of 20 features, each of 32-bitsize, organized in 4-gram format. To meet these requirements, an enhanced version of the algorithm was developed and tested in Cprogramming. Then an equivalent design with a 5-stages pipelined architecture, and single instruction multiple data capabilities, was built in hardware to address the case. At the end, the proposed hardware found to be 65 times faster in term of its computational throughput compared to an existing design, and that with keeping the accuracy level as high as 94%, under the conditions of experiment carried

Real-time embedded eye detection system

The detection of a person’s eyes is a basic task in applications as important as iris recognition in biometric identification or fatigue detection in driving assistance systems. Current commercial and research systems use software frameworks that require a dedicated computer, whose power consumption, size, and price are significantly large. This paper presents a hardware-based embedded solution for eye detection in real-time. From an algorithmic point-of-view, the popular Viola-Jones approach has been redesigned to enable highly parallel, single-pass image-processing implementation. Synthesized and implemented in an All-Programmable System-on-Chip (AP SoC), this proposal allows us to process more than 88 frames per second (fps), taking the classifier less than 2 ms per image. Experimental validation has been successfully addressed in an iris recognition system that works with walking subjects. In this case, the prototype module includes a CMOS digital imaging sensor providing 16 Mpixels images, and it outputs a stream of detected eyes as 640 × 480 images. Experiments for determining the accuracy of the proposed system in terms of eye detection are performed in the CASIA-Iris-distance V4 database. Significantly, they show that the accuracy in terms of eye detection is 100%.This work has been partially developed within the project RTI2018-099522-B-C4X, funded by the Gobierno de España and FEDER funds, and the ARMORI project (CEIATECH-10) funded by the University of Málaga. Portions of the research in this paper use the CASIA-Iris V4 collected by the Chinese Academy of Sciences - Institute of Automation (CASIA)

FPGA implementation of naive bayes classifier for network security

In the vast usage of internet nowadays, the rate of cybercrime such as fraud, hacking, identity theft, network intrusion, software piracy and espionage are becoming more critical. Malware code writers used this chance to create malware that able to breach the security and gain access to the information. Hence, the importance of malware detection system becoming more significant as the users need the protection from the malware threats. Most of malware detection systems implement signature based classification where only known malware can be detected. Nowadays, new malwares are able to change its signature sequence regularly in order to avoid detection. This polymorphic malware becomes the limitation for signature based detection approach. This project aim is to proposed signature-based detection approach that able to detect polymorphic malware by using Naïve Bayes algorithm. The integration of the classifier architecture onto FPGA board in order to measures the performances of the system. The feature from network traffic subset to Snort signature detection of known malware and benign samples are extracted using overlapping Ngram string format. The data set is then being used for training and testing for the classifier. The classifier for the malware detection used Naïve Bayes algorithm that using Bayesian Theorem probability for the features in the data set to determine types of the flow. The model is then being implemented into hardware FPGA architecture and being coded in RTL. The target FPGA that being used in Vivado software is Xilinx Virtex-7 VC709 that able to support the system requirements. The hardware performance of the model was analyzed and compared with the Naïve Bayes software classifier for the performance evaluation. The proposed hardware NB malware detection classifier has managed to achieve 96.3% accuracy and improved FPR rate of 3.1%. The hardware NB malware detection classifier on FPGA architecture also able to achieve better resource utilization and improved detection speed of 0.13 μs per flow

Energy efficient enabling technologies for semantic video processing on mobile devices

Semantic object-based processing will play an increasingly important role in future multimedia systems due to the ubiquity of digital multimedia capture/playback technologies and increasing storage capacity. Although the object based paradigm has many undeniable benefits, numerous technical challenges remain before the applications becomes pervasive, particularly on computational constrained mobile devices. A fundamental issue is the ill-posed problem of semantic object segmentation. Furthermore, on battery powered mobile computing devices, the additional algorithmic complexity of semantic object based processing compared to conventional video processing is highly undesirable both from a real-time operation and battery life perspective. This thesis attempts to tackle these issues by firstly constraining the solution space and focusing on the human face as a primary semantic concept of use to users of mobile devices. A novel face detection algorithm is proposed, which from the outset was designed to be amenable to be offloaded from the host microprocessor to dedicated hardware, thereby providing real-time performance and reducing power consumption. The algorithm uses an Artificial Neural Network (ANN), whose topology and weights are evolved via a genetic algorithm (GA). The computational burden of the ANN evaluation is offloaded to a dedicated hardware accelerator, which is capable of processing any evolved network topology. Efficient arithmetic circuitry, which leverages modified Booth recoding, column compressors and carry save adders, is adopted throughout the design. To tackle the increased computational costs associated with object tracking or object based shape encoding, a novel energy efficient binary motion estimation architecture is proposed. Energy is reduced in the proposed motion estimation architecture by minimising the redundant operations inherent in the binary data. Both architectures are shown to compare favourable with the relevant prior art

Wood Classification Based on Edge Detections and Texture Features Selection

One of the properties of wood is a mechanical property, includes: hardness, strength, cleavage resistance, etc. Among these properties there that can be measured or estimated by visual observation on cross-sectional areas of wood, which is based on inter-fiber density, fiber size, and lines that build the annual rings. In this paper, we proposed a new wood quality classification method based on edge detections. Edge detection is applied to the wood test images with the aim to improving the characteristics of wood fibers so as to make it easier to distinguish their quality. Gray Level Co-occurrence Matrix (GLCM) used to obtain wood texture features, while the wood quality classification done by Naïve Bayes classifier. Found in our experimental results that the first-order edge detection is likely to provide a good accuracy rate and precision. The second order edge detection is highly dependent on the choice of parameters and tends to give worse classification results, as filtering the original wood image, thus blurring characteristics related to wood density. Selection of features obtained from co-occurrence matrix is also quite affected the classification results

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks

An Exploration of the Feasibility of FPGA Implementation of Face Recognition Using Eigenfaces

Biometric identification has been a major force since 1990\u27s. There are different types of approaches for it; one of the most significant approaches is face recognition. Over the past two decades, face recognition techniques have improved significantly, the main focus being the development of efficient algorithm. The state of art algorithms with good recognition rate are implemented using programming languages such as C++, JAVA and MATLAB, these requires a fast and computationally efficient hardware such as workstations. If the face recognition algorithms could be written in a Hardware Description Language, they could be implemented in an FPGA. In this thesis we have choose the eigenfaces algorithm, since it is simple and very efficient, this algorithm is first solved analytically, and then the architecture is designed for FPGA implementation. We then develop the Verilog module for each of these modules and test their functionality using a Verilog Simulator and finally we discuss the feasibility of FPGA implementation. Implementing the face recognition technology in an FPGA would mean that they would require relatively low power and the size is drastically reduced when compared to the workstations. They would also be much faster and efficient, since they are specifically designed for face recognition

A Review of Physical Human Activity Recognition Chain Using Sensors

In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition