Contemporary security information and event management (SIEM) solutions struggle to identify critical security incidents effectively due to the overwhelming number of false alerts generated by disparate security products, which results in significant alert fatigue and hinders effective incident response. To overcome this challenge, we propose a next-generation SIEM framework that integrates security orchestration automation and response capabilities and utilizes a divide-and-conquer strategy to mitigate the impact of low-quality IDS alerts. The proposed framework leverages advanced machine learning and data visualization tools—including a cost-sensitive learning method and an event segmenting algorithm—to filter and correlate alerts plus an augmented visualization tool to expedite the triage process. The proposed framework was evaluated experimentally on a dataset collected from a real-world enterprise network, and we report highly convincing results. The alert screening scheme demonstrates significant potential for real-world security operations. We believe that our findings will contributing to the development of a next-generation SIEM system that effectively addresses alert fatigue and lays the foundation for future research in this field.

The simple implementation and monotonous operation features make Internet of Things (IoT) vulnerable to malware attacks. In order to understand the behavior of IoT malware for further mitigation and prevention, static analysis on executable files of IoT malware is a feasible approach. However, current analysis approaches based on opcode or call-graph usually do not work well with the diversity in CPU architectures and are often resource demanding. In this paper, we propose an efficient scheme to detect and classify IoT malware programs leveraging machine learning methods. We show that reliable and efficient detection and classification can be implemented by exploring the essential discriminant information stored in the byte sequences at the entry points of executable programs. We demonstrate the performance of the proposed scheme using a large scale dataset composed of 111K benignware and 111K malware programs from 7 CPU architectures. The proposed scheme achieves near optimal generalization performance for malware detection (99.9% in accuracy) and for malware family classification (98.4% in accuracy). Moreover, when the CPU architecture information is considered in the learning, the proposed scheme combined with SVM classifiers can yield even higher generalization performance using fewer bytes from the executable files.

The extraordinary number of alerts generated by network intrusion detection systems (NIDS) can desensitize security analysts tasked with incident response. Security information and event management systems (SIEMs) perform some rudimentary automation but cannot replicate the decision-making process of a skilled analyst. Machine learning and artificial intelligence (AI) can detect patterns in data with appropriate training. In practice, the majority of the alert data comprises false alerts, and true alerts form only a small proportion. Consequently, a naive engine that classifies all security alerts into the majority class can yield a superficial high accuracy close to 100%. Without any correction for the class imbalance, the false alerts will dominate algorithmic predictions resulting in poor generalization performance. We propose a machine-learning approach to address the class imbalance problem in multi-appliance security alert data and automate the security alert analysis process performed in security operations centers (SOCs). We first used the neighborhood cleaning rule (NCR) to identify and remove ambiguous, noisy, and redundant false alerts. Then, we applied the support vector machine synthetic minority oversampling technique (SVMSMOTE) to generate synthetic training true alerts. Finally, we fit and evaluated the decision tree and random forest classifiers. In the experiments, using alert data from eight security appliances, we demonstrated that the proposed method can significantly reduce the need for manual auditing, decreasing the number of uninspected alerts and achieving a performance of 99.524% in recall.