The Internet’s expansion has changed how the services accessed and businesses operate. Blockchain is an innovative technology that emerged after the rise of the Internet. In addition, it maintains transactions on encrypted databases that are distributed among many computer networks, much like digital ledgers for online transactions. This technology has the potential to establish a decentralized marketplace for Internet retailers. Sensitive information, like customer data and financial statements, should be routinely transferred via e-commerce. As a result, the system becomes a prime target for cybercriminals seeking illegal access to data. As e-commerce increases, so does the frequency of hacker attacks that raise concerns about the safety of e-commerce platforms’ databases. Owing to the sensitivity of customer data, employee records, and customer records, organizations must ensure their protection. A data breach not only affects an enterprise’s financial performance but also erodes clients’ confidence in the platform. Currently, e-commerce businesses face numerous challenges, including the security of the e-commerce system, transparency and trust in its effectiveness. A solution to these issues is the application of blockchain technology in the e-commerce industry. Blockchain technology simplifies fraud detection and investigation by recording transactions and accompanying data. Blockchain technology enables transaction tracking by creating a detailed record of all the related data, which can assist in identifying and preventing fraud in the future. Using blockchain cryptocurrency will record the sender’s address, recipient’s address, amount transferred, and timestamp, which creates an immutable and transparent ledger of all transaction data.

Handwritten character recognition complexity varies among different languages due to distinct shapes, strokes and number of characters. Numerous works in handwritten character recognition are available for English with respect to other major languages such as Bangla. Existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, Convolutional Neural Network (CNN) is found efficient for English handwritten character recognition. In this paper, a CNN based Bangla handwritten character recognition is investigated. The proposed method normalizes the written character images and then employ CNN to classify individual characters. It does not employ any feature extraction method like other related works. 20000 handwritten characters with different shapes and variations are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed some other prominent exiting methods.

Given the widespread use of the internet at the individual, governmental, and nongovernmental levels, and the opportunities it offers, such as online shopping, security concerns may arise. Cyber criminals are responsible for stopping organizations’ access to internet, for stealing valuable and confidential data, and causing other damage. Therefore, the network must be protected and meet security requirements. Network penetration testing is a type of security assessment used to find risk areas and vulnerabilities that threaten the security of a network. Thus, network penetration testing is designed to provide prevention and detection controls against attacks in the network. A tester looks for security issues in the network operation, design, or implementation of the particular company or organization. Thus, it is important to identify the vulnerabilities and identify the threats that may exploit them in order to find ways to reduce their dangers.The ports at risk are named and discussed in this study. Furthermore, we discuss the most common tools used for network penetration testing. Moreover, we look at potential attacks and typical remediation strategies that can be used to protect the vulnerable ports by reviewing the related publications. In conclusion, it is recommended that researchers in this field focus on automated network penetration testing. In the future, we will use machine learning in WLAN penetration testing, which provides new insight and high efficiency in performance. Moreover, we will train machine learning models to detect a wide range of vulnerabilities in order to find solutions to mitigate the risks in a short amount of time rather that through manual WLAN penetration testing, which consumes a lot of time. This will lead to improving security and reducing loss prevention.

This systematic literature reviewexamines existing literature to determine howDigital Twin (DT) technology can enhance security, with an emphasis on emerging issues, approaches, and proposals in cybersecurity. The review categorizes selected studies based on the techniques used to protect DTs: Machine Learning (ML) and non-machine learning techniques. DTs are classified into five types: component level, process level, asset level, system level, and network of systems level, each with unique applications and corresponding cybersecurity threats. The main conclusions highlight medium to very high perceived risks depending on the DT type and industry implementation. Results identify the most common types of attacks on DTs, various security enhancement techniques, and the sectors that utilize DTs. Future research should focus on developing more effective security paradigms, technologies, and integration schemes to reduce risks and maximize DT technology’s potential. This review presents the current status of DT-related cybersecurity and explores ways to enhance security against emerging cyber risks.

The Internet of Things (IoT) is a rapidly growing network that shares information over the Internet via interconnected devices. In addition, this network has led to new security challenges in recent years. One of the biggest challenges is the impact of denial-of-service (DoS) attacks on the IoT. The Information-Centric Network (ICN) infrastructure is a critical component of the IoT. The ICN has gained recognition as a promising networking solution for the IoT by supporting IoT devices to be able to communicate and exchange data with each other over the Internet. Moreover, the ICN provides easy access and straightforward security to IoT content. However, the integration of IoT devices into the ICN introduces new security challenges, particularly in the form of DoS attacks. These attacks aim to disrupt or disable the normal operation of the ICN, potentially leading to severe consequences for IoT applications. Machine learning (ML) is a powerful technology. This paper proposes a new approach for developing a robust and efficient solution for detecting DoS attacks in ICN-IoT networks using ML technology. ML is a subset of artificial intelligence (AI) that focuses on the development of algorithms. While several ML algorithms have been explored in the literature, including neural networks, decision trees (DTs), clustering algorithms, XGBoost, J48, multilayer perceptron (MLP) with backpropagation (BP), deep neural networks (DNNs), MLP-BP, RBF-PSO, RBF-JAYA, and RBF-TLBO, researchers compare these detection approaches using classification metrics such as accuracy. This classification metric indicates that SVM, RF, and KNN demonstrate superior performance compared to other alternatives. The proposed approach was carried out on the NDN architecture because, based on our findings, it is the most used one and has a high percentage of various types of cyberattacks. The proposed approach can be evaluated using an ndnSIM simulation and a synthetic dataset for detecting DoS attacks in ICN-IoT networks using ML algorithms.

The email system is a globally distributed communication infrastructure service that involves multiple actors playing different roles to ensure end-to-end mail delivery. It is an indispensable method of communicating that is changing how people share data and information. As a result, it facilitates effective and efficient communication, especially in business, as well as convenience, accessibility, and replication. Today, email can send more than just text files; it can also send audio, video, photos, and other files with extensions. With email becoming ubiquitous in all aspects of our lives, enhancing its security, operating procedures, and forensic investigation has become essential. The purpose of this paper is to review some real email forensic incidents and the tools and techniques that have been proposed. A discussion of the major threats to email as well as techniques to mitigate them will also be provided. A comparison study was made of several techniques and analysis tools used in email forensics. In addition, this paper compares the available software tools for email forensics based on factors such as language interface, user interface, programming language, creation of image files, calculation of hash value, cost, and advantages.