From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods

Over the last five years there has been an increase in the frequency and diversity of network attacks. This holds true, as more and more organisations admit compromises on a daily basis. Many misuse and anomaly based Intrusion Detection Systems (IDSs) that rely on either signatures, supervised or statistical methods have been proposed in the literature, but their trustworthiness is debatable. Moreover, as this work uncovers, the current IDSs are based on obsolete attack classes that do not reflect the current attack trends. For these reasons, this paper provides a comprehensive overview of unsupervised and hybrid methods for intrusion detection, discussing their potential in the domain. We also present and highlight the importance of feature engineering techniques that have been proposed for intrusion detection. Furthermore, we discuss that current IDSs should evolve from simple detection to correlation and attribution. We descant how IDS data could be used to reconstruct and correlate attacks to identify attackers, with the use of advanced data analytics techniques. Finally, we argue how the present IDS attack classes can be extended to match the modern attacks and propose three new classes regarding the outgoing network communicatio

Advances in Cybercrime Prediction: A Survey of Machine, Deep, Transfer, and Adaptive Learning Techniques

Cybercrime is a growing threat to organizations and individuals worldwide, with criminals using increasingly sophisticated techniques to breach security systems and steal sensitive data. In recent years, machine learning, deep learning, and transfer learning techniques have emerged as promising tools for predicting cybercrime and preventing it before it occurs. This paper aims to provide a comprehensive survey of the latest advancements in cybercrime prediction using above mentioned techniques, highlighting the latest research related to each approach. For this purpose, we reviewed more than 150 research articles and discussed around 50 most recent and relevant research articles. We start the review by discussing some common methods used by cyber criminals and then focus on the latest machine learning techniques and deep learning techniques, such as recurrent and convolutional neural networks, which were effective in detecting anomalous behavior and identifying potential threats. We also discuss transfer learning, which allows models trained on one dataset to be adapted for use on another dataset, and then focus on active and reinforcement Learning as part of early-stage algorithmic research in cybercrime prediction. Finally, we discuss critical innovations, research gaps, and future research opportunities in Cybercrime prediction. Overall, this paper presents a holistic view of cutting-edge developments in cybercrime prediction, shedding light on the strengths and limitations of each method and equipping researchers and practitioners with essential insights, publicly available datasets, and resources necessary to develop efficient cybercrime prediction systems.Comment: 27 Pages, 6 Figures, 4 Table

Combining K-Means and XGBoost Models for Anomaly Detection Using Log Datasets

Abstract: Computing and networking systems traditionally record their activity in log files, which have been used for multiple purposes, such as troubleshooting, accounting, post-incident analysis of security breaches, capacity planning and anomaly detection. In earlier systems those log files were processed manually by system administrators, or with the support of basic applications for filtering, compiling and pre-processing the logs for specific purposes. However, as the volume of these log files continues to grow (more logs per system, more systems per domain), it is becoming increasingly difficult to process those logs using traditional tools, especially for less straightforward purposes such as anomaly detection. On the other hand, as systems continue to become more complex, the potential of using large datasets built of logs from heterogeneous sources for detecting anomalies without prior domain knowledge becomes higher. Anomaly detection tools for such scenarios face two challenges. First, devising appropriate data analysis solutions for effectively detecting anomalies from large data sources, possibly without prior domain knowledge. Second, adopting data processing platforms able to cope with the large datasets and complex data analysis algorithms required for such purposes. In this paper we address those challenges by proposing an integrated scalable framework that aims at efficiently detecting anomalous events on large amounts of unlabeled data logs. Detection is supported by clustering and classification methods that take advantage of parallel computing environments. We validate our approach using the the well known NASA Hypertext Transfer Protocol (HTTP) logs datasets. Fourteen features were extracted in order to train a k-means model for separating anomalous and normal events in highly coherent clusters. A second model, making use of the XGBoost system implementing a gradient tree boosting algorithm, uses the previous binary clustered data for producing a set of simple interpretable rules. These rules represent the rationale for generalizing its application over a massive number of unseen events in a distributed computing environment. The classified anomaly events produced by our framework can be used, for instance, as candidates for further forensic and compliance auditing analysis in security management.info:eu-repo/semantics/publishedVersio

Self-Learning Algorithms for Intrusion Detection and Prevention Systems (IDPS)

Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network\u27s traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian linear functions as hidden layers display autonomous learning capabilities and are a highly accurate anomaly detection method that can be implemented in cyberattack detection and intrusion prevention with low incidence of false positives