Engineering Headway Vol. 35

Abstract: This study addresses common vulnerabilities in file, folder, and email exchanges within private cloud (PriCld) environments, focusing on threats like password guessing, man-in-the-middle attacks (MaMiAtk), and exploitation by automated scanners. To counter these issues, we propose a log-based analysis approach to enhance resilience against attacks such as MaMiAtk, Denial of Service (DoS), and unauthorized password access attempts. Additionally, encrypted communication channels are implemented to further mitigate the risk of interception in unencrypted connections. The proposed approach aims to strengthen private cloud security and ensure reliable, secure data exchanges.

Abstract: Blockchain technology is a distributed, decentralized public ledger that allows peer-to-peer transactions in a secured way without any third party. One important purpose of using the blockchain is encrypted the currency, such as bitcoin. This paper proposed a new purpose for the blockchain, which is to protect used algorithms from modification. To address the issues with centralized applications, data centralization has caused many problems, such as loss of data and information, in addition to which it may be attacked. Many types of nature reserves were lost and faced extinction. For this reason, a decentralized application based on the Ethereum network was designed that provides, decentralization, transparency, security, no middlemen, and incentives. The proposed framework in this thesis goes through two stages. The first stage is to build a smart contract, referred to as Simulation of Nature Reserved (SONR), that encodes the genetic algorithm to simulate a nature reserve for a set of genetic sequences of endangered animals. The second stage is how to protect this algorithm by creating a decentralized application that runs within the blockchain environment and on the Ethereum network. Lastly, a decentralized application was created to implement a genetic algorithm as a smart contract and then deployed on two test networks for the Ethereum platform. Our recommendations have improved both gas and time consumption. Dapp was run on the Goorli network for a period of approximately 25 seconds at a gas cost of 0.0069937539 ETH. Dapp execution time on the Sepolia network was approximately 12 seconds, with a gas cost of 0.0024745325 ETH.

Abstract: Smart homes continuously grow in popularity and effectiveness, however, the interconnection of devices raises concerns over intrusion detection. The amount of smart devices continues to expand and the complexity of the intrusion detection systems needs to equally match these wide intruder capable environments. This article focused on this need by proposing a new solution, which is a hybrid intrusion detection system consisting of autoencoder based feature extraction and Long Short-Term Memory (LSTM) neural network in its classification phase. The entire process included a total data preprocessing stage that had various processes such as the normalization, data shuffling, data partitioning, data cleaning and label splitting and others. These processes were effective in achieving the objectives of the study. The proposed methodology was demonstrated through the application of the famous ‘NSL-KDD’ dataset where training achieved a significant accuracy of 99.467% while the test dataset produced an accuracy of 99.391%. This research is important not only because of its high accuracy but also because of the robust added value it provides to smart home protection. Enabling an autoencoder on a data set allowed us to emphasize specific features and therefore lower the dimensionality space remaining useful information. In addition, the further use of trained LSTM networks allowed the system to understand even more complex sequences of normal and hostile actions done by users improving the prediction. Furthermore, the strictly followed data preprocessing steps made sure that the model was exposed to good and reasonable data, which is an important factor for the efficiency and applicability of any IDS.

Abstract: As the prevalence of cybercrime escalates, law enforcement agencies are increasingly challenged to keep pace with evolving digital technologies. This necessitates the development of robust tools for scanning digital devices to uncover pertinent evidence. This research paper delves into the MD5 cryptographic hash tool, elucidating its significant applications within digital forensics. We examine the strengths and limitations of MD5 alongside its critical role in various forensic investigations. Additionally, the paper highlights two primary applications of MD5 in the field, summarizing relevant findings from existing literature. Furthermore, we address the challenges associated with MD5, particularly concerning segmentation issues in forensic analysis. Our findings aim to provide insights into the effective utilization of MD5 in digital crime investigations, emphasizing its importance in ensuring the integrity and authenticity of digital evidence.

Abstract: The dynamic nature of cyber threats and the growing intricacy of network traffic frequently provide challenges for traditional approaches to network security and traffic regulation. In this study, we suggest using deep learning systems as a potentially effective way to improve traffic control and network security. Three deep learning architectures the Long Short-Term Memory Convolutional Neural Network (LSTM-CNN), the Convolutional Neural Network (CNN), and the Deep Neural Network (DNN) are thoroughly analysed here. The capacity of these systems tо categorise network traffic and identify unusual activity is assessed. Our tests carried out оn actual network datasets show how well these deep learning models perform іn precisely categorising network traffic and spotting any security risks. Additionally, we look into how well these models work in scenarios involving the source domain and the target domain. While the target domain assessment measures the models' ability tо generalise tо new data, the source domain evaluation evaluates the models' performance оn the training set. Our findings show that, in both domains, the LSTM-CNN design outperforms the CNN and DNN structures, achieving maximum accuracy and resilience. Our research indicates that deep learning systems in particular, LSTM-CNN architecture have a lot оf potential to enhance traffic control and network security. Network managers and cybersecurity experts may strengthen their networks' defences against online attacks and guarantee the uninterrupted operation оf vital network infrastructure by utilising deep learning.