Search

The number of registered SIM cards and active mobile phone subscribers in Zambia in 2020 surpassed the population of the country. This development and the integration of mobile phone systems with financial payment systems has not come without a cost. Cyberattackers, using various social engineering techniques have jumped onto the bandwagon to defraud unsuspecting users. Considering the aforesaid, this paper presents a high-order analytical approach towards mobile phone-based social engineering cyberattacks (phishing, SMishing, and vishing) in Zambia which seek to defraud benign victims. This paper presents a baseline study to reiterate the problem at hand. The research used a mixed-methods approach, combining quantitative and qualitative data, and adopted a hybrid descriptive research design. Furthermore, an attack model and an evaluation framework were devised to ascertain the most prevalent types of mobile phone-based cyberattacks. Based on logistic regression analysis, the findings indicate that the most prevalent type of mobile phone-based social engineering cyberattack in Zambia is SMishing. Based on the results and observed insights, recommendations to mitigate these emergent social engineering cyberattacks were suggested. This research serves as a valuable baseline for understanding and addressing the growing challenges posed by mobile phone-based cyberattacks in Zambia's evolving technological landscape. [ABSTRACT FROM AUTHOR]

As mobile services proliferate to include financial transactions, the threat of phishing attacks targeting users has equally been escalating. Attackers have been using different kinds of phishing techniques, especially in third world where mobile services are prevalent. As such, this paper presents a Smishing (SMS phishing) Detection model leveraging Natural Language Processing (NLP) and Machine Learning (ML) techniques. It aims to detect smishing threats in real-time by the integrating NLP with ML The developed model harnesses NLP algorithms to analyse text-based messages, scrutinizing linguistic patterns and contextual clues indicative of smishing attempts. Through ML algorithms, the model learns to distinguish between legitimate (Non-Smishing) and fraudulent messages (Smishing), adapting dynamically to evolving smishing tactics. The model's efficacy is evaluated through comprehensive testing, demonstrating promising results of precision, recall, and accuracy with F-1 measure at 0.902 and AUC at 0.95. The Model stands as a proactive defence mechanism against smishing in mobile money environments, contributing to enhanced user security and trust in financial transactions. [ABSTRACT FROM AUTHOR]

Despite flushable toilet invention being helpful to humans, especially in towns and cities, flushing waste has become very expensive both at household and public places coupled with health risks of contracting some infectious diseases like streptococcus, diahoerria, etc, due to its use and these are mostly spread in public toilets such as hospitals, schools, bus stations etc. Global warming and climate change have caused water management to become an issue that requires a concerted effort from all users, if not well managed at household and public levels, the monthly cost becomes high as some people tend to flush urine with the same amount of water as if it were stool by emptying the entire cistern into the bowl which is a waste of water hence pausing an economic challenge at both household and public places. Many people do not sanitize the toilet seat before use which if not well handled may harbor germs which intern may infect users. Therefore, the developed system ensures that there is automated flushing of waste with a distinct amount of water for urine and stool. The developed system also automatically sanitizes the toilet seat before someone sits on it hence reducing infectious diseases like covid-19. The system was developed using technologies and frameworks like Arduino Microcontroller which controls how the various toilet parts operate automatedly and C programming language was used for code. [ABSTRACT FROM AUTHOR]

today, the term ransomware is frequently used in cybercrime headlines, its consequences have been on the rise leaving a trail of terrible losses in its wake. Both people and businesses have been victimized by ransomware, costing the victims millions of dollars in ransom payments. In addition, victims who were unable to pay the ransom or decrypt the data experienced data losses. This study uses dynamic malware analysis artifacts and supervised machine learning to detect ransomware at the host level. It takes on a thorough examination of the operational specifics of ransomware and suggests a supervised machine learning approach to detection using various ransomware features derived from a dynamic malware analysis. According to the findings, a Logistic Regression algorithm model with a 97.7% accuracy score offers a 99% success rate in ransomware detection. This demonstrates how well machine learning and dynamic malware analysis work together to detect ransomware activity at the host level. Systems security administrators can mitigate security risks by using this method. [ABSTRACT FROM AUTHOR]

Network management consists of monitoring and controlling activities that ensure the network devices are delivering value. In traditional networks, network management is primarily performed manually via the command-line interface using SSH. This approach has several challenges, configuring large networks via the CLI is inefficient, it leads to configuration drift, and it's error-prone. This paper proposes an SDN approach that implements Ansible and Git in centralized network architecture to address the aforementioned problem. The paper shows how network management using an Ansible playbook that dynamically configures multiple devices and push's backup files to Git for version control can mitigate the highlighted challenges. [ABSTRACT FROM AUTHOR]

Recently, ransomware has earned itself an infamous reputation as a force to reckon with in the cybercrime landscape. However, cybercriminals are adopting other unconventional means to seamlessly attain proceeds of cybercrime with little effort. Cybercriminals are now acquiring cryptocurrencies directly from benign Internet users without the need to extort a ransom from them, as is the case with ransomware. This paper investigates advances in the cryptovirology landscape by examining the state-of-the-art cryptoviral attacks. In our approach, we perform digital autopsy on the malware's source code and execute the different malware variants in a contained sandbox to deduce static and dynamic properties respectively. We examine three cryptoviral attack structures: browser-based crypto mining, memory resident crypto mining and cryptoviral extortion. These attack structures leave a trail of digital forensics evidence when the malware interacts with the file system and generates noise in form of network traffic when communicating with the C2 servers and crypto mining pools. The digital forensics evidence, which essentially are IOCs include network artifacts such as C2 server domains, IPs and cryptographic hash values of the downloaded files apart from the malware hash values. Such evidence can be used as seed into intrusion detection systems for mitigation purposes. [ABSTRACT FROM AUTHOR]

Ransomware has emerged as a resilient type of malware to reckon with which has inadvertently forced security researchers and companies into a game of cat and mouse. The media has not helped matters even as the inaccuracies and myths surrounding ransomware continue to deepen. In this paper, we endeavor to debunk WannaCry ransomware through its network interactions. We employ reverse engineering to perform an autopsy on the underlying malware program logic and further use dynamic analysis to capture the corresponding network actions associated with such logic. Based on the uncovered artifacts, we recommend preventative measures to thwart such attacks. [ABSTRACT FROM AUTHOR]