Honeypots are computers specifically deployed to be a resource that is expected to be attacked or compromised. While the attacker is distracted with the decoy computer system we learn about the attacker and their methods of attack. From the information gained about the attacks we can then review and harden out security systems. Compared to an Intrusion Detection System (IDS) which may trigger false positives, we take the standpoint that nobody ought to be interacting with the decoy computer; therefore we regard all interactions to be of value and worth investigation. A sample of honeypots are evaluated and one selected to collect attacks. The captured attacks reveal the source IP address of the attacker and the service port under attack. Attacks where the exploit attempts to deploy a binary can capture the code, and automatically submit it for analysis to sandboxes such as VirusTotal.

Imbalanced classes in multi-classed datasets is one of the most salient hindrances to the accuracy and dependable results of predictive modeling. In predictions, there are always majority and minority classes, and in most cases it is difficult to capture the members of item belonging to the minority classes. This anomaly is traceable to the designs of the predictive algorithms because most algorithms do not factor in the unequal numbers of classes into their designs and implementations. The accuracy of most modeling processes is subjective to the ever-present consequences of the imbalanced classes. This paper employs the variance ranking technique to deal with the real-world class imbalance problem. We augmented this technique using one-versus-all re-coding of the multi-classed datasets. The proof-of-concept experimentation shows that our technique performs better when compared with the previous work done on capturing small class members in multi-classed datasets.

The rapid advancement of information technology has introduced a noticeable shift from traditional offline practices to more efficient and interconnected online environments. This transition, while offering convenience, has also increased exposure to various cyber threats such as identity theft, impersonation, and phishing scams. Reconnaissance, or briefly known as information gathering, is a key stage for threat actors, often relying on open-source intelligence (OSINT) to collect sensitive and extensive data on targets. In response to this challenge, this study introduces reconCTI, a command-line tool built using Python for Linux systems. The tool is designed to search for sensitive data leaks across both surface web and dark web platforms. It allows users to input specific keywords, scan multiple sites at once, and then assess the findings by referencing the MITRE ATT&CK framework. The results are compiled into a threat report that also includes possible mitigation strategies. reconCTI is intended to support both cybersecurity professionals and individuals in identifying risks early and taking appropriate action.