A wireless sensor network (WSN) consists of several tiny sensor nodes to monitor, collect, and transmit the physical information from an environment through the wireless channel. The node failure is considered as one of the main issues in the WSN which creates higher packet drop, delay, and energy consumption during the communication. Although the node failure occurred mostly due to persistent energy exhaustion during transmission of data packets. In this paper, Artificial Neural Network (ANN) based Node Failure Detection (NFD) is developed with cognitive radio for detecting the location of the node failure. The ad hoc on-demand distance vector (AODV) routing protocol is used for transmitting the data from the source node to the base station. Moreover, the Mahalanobis distance is used for detecting an adjacent node to the node failure which is used to create the routing path without any node failure. The performance of the proposed ANN-NFD method is analysed in terms of throughput, delivery rate, number of nodes alive, drop rate, end to end delay, energy consumption, and overhead ratio. Furthermore, the performance of the ANN-NFD method is evaluated with the header to base station and base station to header (H2B2H) protocol. The packet delivery rate of the ANN-NFD method is 0.92 for 150 nodes that are high when compared to the H2B2H protocol. Hence, the ANN-NFD method provides data consistency during data transmission under node and battery failure.

This paper is devoted to design and implement a complete security platform for USB flash disks due to the popularity of this device in exchanging data, it is a complete system security solution as it concerns the thumb drive due to the manipulation of I/O operation not the file system. USB flash disks have been the major threat for computer system beside the internet threats where viruses can spread from computer to computer or from computer to network. USB complete security system presented by this paper is composed of three essential elements: kernel filter driver which will be installed in USB device driver stack to intercept all exchanged packets and send it to encryption unit, kernel level encryption/decryption unit and configuration unit. In contrary to most USB security modules the system presented by this paper will store only the round number of the key generator with the encrypted data. Round number will be coded using MD5 algorithm to increase the immunity of attacking data stored in the flash disks.

The increased in number and size of larvae and juvenile growth are estimated based on manual approach in fishing hatcheries. There is a high demand for computer assisted software solution for aquaculture research in early detection and recognition of fish population. There exist several companies who have introduced fish detection technologies into the market. Although able to count the number of larvae with a high accuracy rate, the fish counter software's may encounter difficulties when detecting smaller larvae's and ants in very early stage of birth period. The main aim of the paper is to propose a framework using machine learning techniques that can be of low cost and efficient system for fish counting and growth study. The expected final result will be a complete preliminary prototype with basic camera setup, focus on larval fish. medium term, improve camera setup and quality; focus on larval and juvenile fish. For the Long term, full fish growth tracking and data mining is implemented. The proposed research in this paper will assist the Malaysian fisheries department to have accuracy on detecting the larvae, juvenile and ants in the hatcheries.

The digital world has advanced beyond that was imaginable decades before due to the inventions and innovations brought forth through developments in the complex field of cybertronics. These digital technologies and solutions are now so welded into our lives that the loss or absence of it may possibly mean utter catastrophe to mankind. The computer distinguishes itself and provides a better advantage by presenting the ability to interact with the user via the keyboard and its processed output. The cyberspace with the limitless capabilities it holds made innumerable lives easier as well as more difficult with a dark side known as digital crime. Therefore, due to the growing sophistication of digital crime, the ultimate question raised by professionals and enforcement officers is “What is the most concrete and effective method to prosecute digital criminals?” To answer this question, computer forensic procedures and cyber law have been introduced and implemented. The digital forensics regulation in Malaysia is still in the initial stages not having concise methods and standardized procedures in cybercrime investigation, no attention is being paid over the fragile evidence as live forensic stage is missing in the current digital forensic standard operating procedure. No portable live forensic tool currently being used by digital forensic investigator in Malaysia at the crime scene. The objective of this research is to introduce a new digital forensic model which focuses on live forensic data acquisition stage in digital forensic standard operating procedure and also to develop a handy GUI oriented live forensic data acquisition tool. The methodology used in validating the model and the tool will be by the digital forensic expert user from Malaysia and India. With the existence of this digital forensic model and digital live forensic tool the Malaysian digital forensic investigators will be more productive in accessing the crime scene and also able to effectively acquire the live data and proceed into solving the case.

In both wireless and mobile ad hoc networks, assaults can come from a variety of different sources. The terms “active attack” and “passive attack” describe these two types of attacks. In the network community, the Sybil attack is one of the most often used and deployed techniques for sniffing identities and repurposing them. Multiple identities or Sybil attacks have recently sparked a lot of interest in the research community. The algorithms and networks on which they are tested are vastly different among the many methods that have been offered. Since researchers can’t evaluate these systems side by side or test their efficacy on real-world social networks with a variety of structural features, it’s difficult to say whether there are any other (perhaps more efficient) methods of Sybil protection. In the event of a Sybil attack, the gatecrasher subverts the system framework’s notoriety arrangement by creating a large number of pseudonymous individuals and then using them to add an enormously imbalanced influence. Three factors determine a notoriety framework’s susceptibility to a Sybil attack: how quickly personalities can be generated, how much the notoriety framework accepts inputs from substances that lack a chain of trust, and if the notoriety framework handles all components equally. A large-scale Sybil ambush in Bittor-rent Mainline may be accomplished in a cheap and effective manner, according to confirmation. A substance on a distributed system is a piece of software that has access to the resources of the local community. By displaying a character, a distributed system element reveals itself to the world. A single chemical can have an impact on more than one character. Numerous characters can be assigned to a single element. The personalities of substances in shared systems are used for the objectives of repetition, asset transfer, reliability, and trustworthiness, among other reasons. For remote elements to be aware of characters without necessarily being aware of the personality-to-neighborhood correlation, distributed systems make use of the character as a decision. Each different identification is normally considered to be associated with a separate local entity by convention. A single local entity may have several identities in actuality. In order to avoid and identify Sybil assaults, an empirical technique is used in this study. According to the base paper, any nodes with RSS greater than the provided threshold are regarded to be attackers under the present approach. A centralized way to monitor the mobile nodes is required to prevent this assault. As the server agent assumes full control of the ad-hoc network, malevolent nodes or selfish nodes are fully eliminated from the system.

The ability to recognize a grocery on the shelf of a retail store is an ordinary human skill. Automatic detection of grocery on the shelf of retail store provides enhanced value-added to consumer experience, commercial benefits to retailers and efficient monitoring to domestic enforcement ministry. Compared to machine visionbased object recognition system, automatic detection of retail grocery in a store setting has lesser number of successful attempts. In this paper, we present an enhanced YOLOv4 for grocery detection and recognition. We enhanced through spatial pyramid pooling (SPP) and Intersection over union (IOU) components of YOLOv4 to be more accurate in making recognition and faster in the process. We carried an experiment using modified YOLOv4 algorithm to work with our new customized annotated dataset consist on 12000 images with 13 classes. The experiment result shows satisfactory detection compare to other similar works with mAP of 79.39, IoU threshold of 50%, accuracy of 82.83% and real time performance of 61 frames per second

Kevin Ashton coined the phrase Internet of Things in 1999 with high performance implementation for corporate and social world. Thanks to the success of high-performing Internet of Things (IoT) whereby the tags and sensors are the foundation for IoT implementation of radio frequency identification with enormous implementation patterns. Real world objects and systems that are remotely managed using program- based tools may be outfitted with RFID tags. Radio frequency recognition devices may identify objects and sense information. Very thin micro-sized RFID chips that can attach remotely are built. The internet of things will cross over USD 267 billion in 2020. According to the report by Gartner, there would be $273 billion linked devices around the world in 2014. The quantity, which is equal to 8.4 billion goods, is 31% more than last year. This study examines security and productivity in the IoT. It is very popular to use Internet of Things (IoT) in robotics because of sensor sensors, advanced wireless technology and use of software programming. Both wireless IP-based systems come with built-in GPS modules. The utility of smart cities and home automation was increasingly accentuated by the appearance of vast databases of smart IP-based sensors. Within the scope of this study, one of the goals is to establish simulation trends that can cover protection weakness of the Internet of Things. In the novel, the simulation processes were implemented through Contiki Cooja and CupCarbon. The modern age is greatly being affected by impossibly sophisticated technical devices. It is treated under the umbrella of Internet of Things (IoT). Several applications are commonly using IoT linked technologies to a broad variety of purposes. IoT contains many other concepts such as universal computing, widespread computing, ambient computing, among several others. The work presents the implementation using high performance framework for the security in the IoT environment using security mechanism on IPv6.

Abstract— Image classification of land use using aerial scene classification has become increasingly common around the world. Utilizing the power of Convolutional Neural Networks (CNNs), identification of various city township areas using satellite imagery has become more efficient compared to the previous manual labeling. The objective of this research is to build a convolutional neural network model for residential and commercial area identification. In the research, we also adopted Inception V3 and VGG16 to develop two transfer learning models for the identification system. The Inception V3-based model achieved the highest overall accuracy value of 100%, showing its effectiveness in accurate residential and commercial area identification. The proposed CNN model achieved an accuracy of 99%, while the VGG-16 model with all configurations being frozen achieved 99% accuracy.

The utility organization has already implemented some of security framework and compliance in their data center to secure the data centers of valuable information. However, the implementation of security framework and compliance, still has several issues relates to some restricted areas. There is no effective security framework and compliance, being implemented in their data center such as access control management system at the entrance of the building zone. Therefore, the objective of this research is to develop information security compliance framework in data center in utility company. This research applied qualitative method namely semi-structured interviews for data collection. The contribution of this research will help professionals and security management organizations to understand the best ways they can be used to improve physical security within the context of information security compliance frameworks that play an important role.

The paper aims to identify behavioural theories that influence information security policies compliance behaviour. A systematic review of empirical studies from eleven online databases (ACM digital library, Emerald Insight, IEEE Xplore digital library, Springer link, Science direct, Scopus, Web of Science, Oxford academic journals, SAGE journals, Taylor & Francis and Wiley online library) are conducted. This review identified 29 studies met its criterion for inclusion. The investigated theories were extracted and analysed. Total of 19 theories have been identified and studied concerning to security policy compliance behaviour. The result indicated that the most established theories in information security compliance behaviour studies are the Theory of Planned Behavior and Protection Motivation theory. Meanwhile, General Deterrence Theory, Neutralization theory, Social Bond Theory / Social Control Theory are used moderately in this research area. Less explored theories are namely Self Determination Theory, Knowledge, Attitude, and Behavior, Social Cognitive Theory, Involvement Theory, Health belief model, Theory of Interpersonal Behavior, Extended Parallel Processing Model, Organisational Control Theory, Psychological Reactance Theory, Norm Activation Theory, Organizational Behaviour Theory, Cognitive Evaluation Theory and Extended Job Demands-Resources. The results from this review may guide the development and evaluation of theories promoting information security compliance behaviours. This will further contribute in the development of an integrated theory of information security compliance behaviour.

Data centers are primarily the main targets of cybercriminals and security threats as they host various critical information and communication technology (ICT) services. Identifying the threats and managing the risks associated with data centers have become a major challenge as this will enable organizations to optimize their resources to focus on the most hazardous threats to prevent the potential risks and damages. The objective of this paper is to identify major ICT security threats to data centers in the Malaysian public sector and their causes. The data for this study was collected through interview sessions. A total of 33 respondents from various government organizations were interviewed. The results revealed that the technical threats, spyware, phishing, bluesnarfing threats, social engineering and virus, trojan, malware, ransomware, viral websites threats are the major categories of threats often encountered by the malaysian public sector organizations. The causes for these threats are lack of budget, competent personnel, and manpower for security tasks, user awareness; lack of compliances and monitoring; insufficient security policies and procedures as well as deliberate cyber attacks. The outcome of this study will give a greater degree of awareness and understanding to the ICT security officers, who are entrusted with data center security. © 2021 Institute of Advanced Engineering and Science. All rights reserved.

This paper describes techniques to perform efficient and accurate recognition in larvae images captured from the hatcheries for counting the live and dead larvae’s. In order to accurately model small, irregularly shaped larvae and juvenile, the larvae images are enhanced by three enhancement methods, and segmentation of larvae and juvenile is performed by orientation associated with each edge pixel of region growing segmentation method. The two vital tasks in image analysis are recognition and extraction of larvae and juvenile from an image. When these tasks are manually performed, it calls for human experts, making them more time consuming, more expensive and highly constrained. These negative factors led to the development of various computer systems performing an automatic recognition and extraction of visual information to bring consistency, efficiency and accuracy in image analysis. This main objective of this paper is to study on the various existing automated approaches for recognition and extraction of objects from an image in various scientific and engineering applications. In this study, a categorization is made based on the four principle factors (Input, Segment the larvae, Recognition, Counting) with which each approach is drive .The achieved result of recognition and classification of larvae is around 85%.All the results achieved through matlab implementation are discussed in this paper are proved to work efficiently in real environment.

Insider threat is a most worrying threat that haunts many organizations today that cause enormous financial losses and damages. As a frontline, Information Technology (IT) organizations has to implement necessary countermeasures to protect critical infrastructure. Although, many approaches proposed before to detect and mitigate insider threat, significant rise of cases in past few years and unavailability of a widely accepted solution paves way to conduct more researches. Moreover, the pandemic situation has brought in a new challenge for IT organizations to review the existing safeguards. This paper aims to contribute an interdisciplinary approach at proposing a multidimensional model that scrutinize factors from multiple dimensions such as psychological, behavioral, technological, organizational and environmental dimension that triggers insider threat. The constructed model coordinates organizations to counter insider threat by addressing issues in more effective and efficient way by applying the multidimensional approach for mitigation.

The data quality evaluation is essential towards designing a data assessment method for any company because data is an important asset. Therefore, the purpose of this study is to develop the data quality evaluation method for a transportation agency in Malaysia in order to quantify the quality of data in the SIKAP licensing system. This can benefit the transportation agency to improve the quality of data for the use of reporting, forecasting business operations and data integration with other agency's systems. The relevant data evaluation dimensions have been identified from literature study and relative data evaluation framework which are necessarily required by the transportation agency to maintain high data quality in the SIKAP system. The process design for the proposed method involves data dimension identification, capturing the relevant database structure, subjective evaluation with a questionnaire and objective evaluation with data profiling. From the design process, the result shows that data evaluation method for a transportation agency must have a minimum of six data quality dimensions. SIKAP, the legacy system is in the process to revamp into a new system. Thus, this research contributes to enhance the current system's data quality during revamping process and data migration into the new system.

The most common problem in the daily lives of men and woman is the occurrence of kidney stone, which is named as renal calculi, due to living nature of the people. These calculi can be occurred in kidney, urethra or in the urinary bladder. Most of the existing study in the diagnosis of ultrasound image of the kidney stone identifies the presence or absence of stone in the kidney. The main objective of the paper is to propose a computer aided diagnosis prototype for early detection of kidney stones which helps to change the diet condition and prevention of stone formation in future. The proposed work is based image acquisition, image enhancement, segmentation, feature extraction and classification, whereas in initial stage, ultrasound of kidney image is diagnosed for the presence of renal calculi stone and its level of growth measured in sizes. Seed pixel based region growing segmentation is applied in our work to localize the intensity threshold variation, based on the different threshold variation, it is categorized into a class of images as normal, stone and stone at early stages. The proposed segmentation is based on identifying the homogeneous regions which depends on the granularity features, therefore interested structure with different dimensions are compared with speckle size and extracted. The shape and different size of the grown regions are depending on the entries in lookup table. After completing the stage of region growing, region merging is used to suppress the high frequency artifacts in the ultrasound image. Once the segmented portion of stone is extracted and statistical features are calculated, which can be feed as feature selection by principle component analysis method. The extracted features are in input for artificial neural network classifier for achieving the improved accuracy compared to previous works. The expected output findings are based on texture feature values, threshold variations, size of the stone from the ultrasound kidney image samples with the support of clinical research center. The findings in our study and observation are based on correct estimate size of the stones, position of the stones in location of kidney; these findings are not performed in previous work. The enhanced seed pixel region growing segmentation and ANN classification helps to diagnose the presence or absence of renal calculi kidney stones, which leads to an early detection stone formation in the kidney and improve the accuracy rate of classification.

The emphasis on data protection is improved in particular with respect to the transmission protocols utilized. Different research on numerous data protection areas such as authentication, encryption, hiding of data and validation were performed. In addition, a cybersecurity standard, such as IP-SEC, and secure sockets layer (SSL), were introduced to solve privacy infringement problems by applying encryption, authorization and protection to data exchanged and data stored in the cloud. This study suggests a new steganography algorithm, a data protection tool used to conceal massive amounts of data from graphic and statistic attacks in color images. The proposed algorithm is a multi-level steganography modified deep hiding/extracting technique (MDHET), which implements a selected least signified bit (SLSB) for color picture dispersal of the information. In addition, an accurate pixel location randomization feature has been applied. After MDHET, the predicted results will effectively conceal data up to 6 bpp (bit per pixel) with high safety levels by improving the quality of images. In addition, MDHET can be useful for encoding a deep series of images into one in which the testing procedure is carried out using regular reference images used in color image processing and compression analysis from different institutions.

This paper introduces the concept of Smart-E-Service to be the kernel building block of the Smart e-Government. The presented Smart-E-Service has many privileges over the traditional E-Service such as fully cross-platform, social, liable, negotiable, autonomous and mobile. Smart-E-Service is implemented in this paper as a deliverable mobile agent that complies with HTML5 as a frontend and Node. js modules to interface JADE platform at the backend. The presented Smart-E-Service is preemptive behavior rather than reactive or even proactive; preemptive interpreted as actions based upon hard domain intelligence. HTML5 terminologies such as Real Multi-Threading and Web Sockets have been exploited and deployed to efficiently increase the performance of Smart-E-Service and sustain its attributes.