Detection of Ponzi scheme on Ethereum using machine learning algorithms.

Security threats posed by Ponzi schemes present a considerably higher risk compared to many other online crimes. These fraudulent online businesses, including Ponzi schemes, have witnessed rapid growth and emerged as major threats in societies like Nigeria, particularly due to the high poverty rate. Many individuals have fallen victim to these scams, resulting in significant financial losses. Despite efforts to detect Ponzi schemes using various methods, including machine learning (ML), current techniques still face challenges, such as deficient datasets, reliance on transaction records, and limited accuracy. To address the negative impact of Ponzi schemes, this paper proposes a novel approach focusing on detecting Ponzi schemes on Ethereum using ML algorithms like random forest (RF), neural network (NN), and K-nearest neighbor (KNN). Over 20,000 datasets related to Ethereum transaction networks were gathered from Kaggle and preprocessed for training the ML models. After evaluating and comparing the three models, RF demonstrated the best performance with an accuracy of 0.94, a class-score of 0.8833, and an overall-score of 0.96667. Comparative evaluations with previous models indicate that our model achieves high accuracy. Moreover, this innovative work successfully detects key fraud features within the Ponzi scheme dataset, reducing the number of features from 70 to only 10 while maintaining a high level of accuracy. The main strength of this proposed method lies in its ability to detect clever Ponzi schemes from their inception, offering valuable insights to combat these financial threats effectively.

A Multi-Layer Semantic Approach for Digital Forensics Automation for Online Social Networks.

Currently, law enforcement and legal consultants are heavily utilizing social media platforms to easily access data associated with the preparators of illegitimate events. However, accessing this publicly available information for legal use is technically challenging and legally intricate due to heterogeneous and unstructured data and privacy laws, thus generating massive workloads of cognitively demanding cases for investigators. Therefore, it is critical to develop solutions and tools that can assist investigators in their work and decision making. Automating digital forensics is not exclusively a technical problem; the technical issues are always coupled with privacy and legal matters. Here, we introduce a multi-layer automation approach that addresses the automation issues from collection to evidence analysis in online social network forensics. Finally, we propose a set of analysis operators based on domain correlations. These operators can be embedded in software tools to help the investigators draw realistic conclusions. These operators are implemented using Twitter ontology and tested through a case study. This study describes a proof-of-concept approach for forensic automation on online social networks.

A deeper look into cybersecurity issues in the wake of Covid-19: A survey.

This study analyzed the Coronavirus (COVID-19) crisis from the angle of cyber-crime, highlighting the wide spectrum of cyberattacks that occurred around the world. The modus operandi of cyberattack campaigns was revealed by analyzing and considering cyberattacks in the context of major world events. Following what appeared to be substantial gaps between the initial breakout of the virus and the first COVID-19-related cyber-attack, the investigation indicates how attacks became significantly more frequent over time, to the point where three or four different cyber-attacks were reported on certain days. This study contributes in the direction of fifteen types of cyber-attacks which were identified as the most common pattern and its ensuing devastating events during the global COVID-19 crisis. The paper is unique because it covered the main types of cyber-attacks that most organizations are currently facing and how to address them. An intense look into the recent advances that cybercriminals leverage, the dynamism, calculated measures to tackle it, and never-explored perspectives are some of the integral parts which make this review different from other present reviewed papers on the COVID-19 pandemic. A qualitative methodology was used to provide a robust response to the objective used for the study. Using a multi-criteria decision-making problem-solving technique, many facets of cybersecurity that have been affected during the pandemic were then quantitatively ranked in ascending order of severity. The data was generated between March 2020 and December 2021, from a global survey through online contact and responses, especially from different organizations and business executives. The result show differences in cyber-attack techniques; as hacking attacks was the most frequent with a record of 330 out of 895 attacks, accounting for 37%. Next was Spam emails attack with 13%; emails with 13%; followed by malicious domains with 9%. Mobile apps followed with 8%, Phishing was 7%, Malware 7%, Browsing apps with 6%, DDoS has 6%, Website apps with 6%, and MSMM with 6%. BEC frequency was 4%, Ransomware with 2%, Botnet scored 2% and APT recorded 1%. The study recommends that it will continue to be necessary for governments and organizations to be resilient and innovative in cybersecurity decisions to overcome the current and future effects of the pandemic or similar crisis, which could be long-lasting. Hence, this study's findings will guide the creation, development, and implementation of more secure systems to safeguard people from cyber-attacks.

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities.

Specialized data preparation techniques, ranging from data cleaning, outlier detection, missing value imputation, feature selection (FS), amongst others, are procedures required to get the most out of data and, consequently, get the optimal performance of predictive models for classification tasks. FS is a vital and indispensable technique that enables the model to perform faster, eliminate noisy data, remove redundancy, reduce overfitting, improve precision and increase generalization on testing data. While conventional FS techniques have been leveraged for classification tasks in the past few decades, they fail to optimally reduce the high dimensionality of the feature space of texts, thus breeding inefficient predictive models. Emerging technologies such as the metaheuristics and hyper-heuristics optimization methods provide a new paradigm for FS due to their efficiency in improving the accuracy of classification, computational demands, storage, as well as functioning seamlessly in solving complex optimization problems with less time. However, little details are known on best practices for case-to-case usage of emerging FS methods. The literature continues to be engulfed with clear and unclear findings in leveraging effective methods, which, if not performed accurately, alters precision, real-world-use feasibility, and the predictive model's overall performance. This paper reviews the present state of FS with respect to metaheuristics and hyper-heuristic methods. Through a systematic literature review of over 200 articles, we set out the most recent findings and trends to enlighten analysts, practitioners and researchers in the field of data analytics seeking clarity in understanding and implementing effective FS optimization methods for improved text classification tasks.

HoneyDetails: A prototype for ensuring patient's information privacy and thwarting electronic health record threats based on decoys.

Advancements in electronic health record system allow patients to store and selectively share their medical records as needed with doctors. However, privacy concerns represent one of the major threats facing the electronic health record system. For instance, a cybercriminal may use a brute-force attack to authenticate into a patient's account to steal the patient's personal, medical or genetic details. This threat is amplified given that an individual's genetic content is connected to their family, thus leading to security risks for their family members as well. Several cases of patient's data theft have been reported where cybercriminals authenticated into the patient's account, stole the patient's medical data and assumed the identity of the patients. In some cases, the stolen data were used to access the patient's accounts on other platforms and in other cases, to make fraudulent health insurance claims. Several measures have been suggested to address the security issues in electronic health record systems. Nevertheless, we emphasize that current measures proffer security in the short-term. This work studies the feasibility of using a decoy-based system named HoneyDetails in the security of the electronic health record system. HoneyDetails will serve fictitious medical data to the adversary during his hacking attempt to steal the patient's data. However, the adversary will remain oblivious to the deceit due to the realistic structure of the data. Our findings indicate that the proposed system may serve as a potential measure for safeguarding against patient's information theft.

State-of-the-art in artificial neural network applications: A survey.

This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.