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.

Security Incident and Event Manager (SIEM) is a security management approach designed to identify possible threats within a real-time enterprise environment. The main challenge for SIEM is to find critical security incidents among a huge number of less critical alerts coming from separate security products. The continuously growing number of internet-connected devices has led to the alert fatigue problem, which is defined as the inability of security operators to investigate each incoming alert from intrusion detection systems. This fatigue can lead to human errors and leave many alerts being not investigated. Aiming at reducing the number of less important threat alerts presented to security operators, this paper presents a new method for highlighting critical alerts with a minimal number of false negatives. The proposed method employs isolation forest to ensure unsupervised performance and adaptability to different types of networks. Furthermore, it takes the advantage of day-forward-chaining analysis to ensure the detection of highly important alerts in real time. The number of false positive cases is reduced by employing an autoencoder. The proposed method achieved a recall score of 95.89% and a false positive rate of 5.86% on a dataset comprising more than half a million alerts collected in a real-world enterprise environment over ten months. This study highlights the importance of addressing the alert fatigue problem and validates the effectiveness of unsupervised learning in filtering out less important threat alerts.

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 cybersecurity information exchange framework, known as CYBEX, is currently undergoing its first iteration of standardization efforts within ITU-T. The framework describes how cybersecurity information is exchanged between cybersecurity entities on a global scale and how the exchange is assured. The worldwide implementation of the framework will eventually minimize the disparate availability of cybersecurity information. This paper provides a specification overview, use cases, and the current status of CYBEX.

As our cyber society develops and expands, the importance of cybersecurity operations is growing in response to cybersecurity threats coming from beyond national borders. Efficient cybersecurity operations require information exchanges that go beyond organizational borders. Various industry specifications defining information schemata for such exchanges are thus emerging. These specifications, however, define their own schemata since their objectives and the types of information they deal with differ, and desirable schemata differ depending on the purposes. They need to be organized and orchestrated so that individual organizations can fully exchange information and collaborate with one another. To establish the foundations of such orchestration and facilitate information exchanges, this paper proposes a reference ontology for cybersecurity operational information. The ontology structures cybersecurity information and orchestrates industry specifications. We built it from the standpoint of cybersecurity operations in close collaboration with cybersecurity organizations including security operation centers handling actual cybersecurity operations in the USA, Japan and South Korea. This paper demonstrates its usability by discussing the coverage of industry specifications. It then defines an extensible information structure that collaborates with such specifications by using the ontology and describes a prototype cybersecurity knowledge base we constructed that facilitates cybersecurity information exchanges among various parties. Finally, it discusses the usage scenarios of the ontology and knowledge base in cybersecurity operations. Through this work, we wish to contribute to the advancement of cybersecurity information exchanges.