Purpose-The purpose of this paper is to formulate a novel model for enhancing the effectiveness of existing Digital Forensic Readiness (DFR) schemes by leveraging the capabilities of cyber threat information sharing. Design/methodology/approach-This paper employs a quantitative methodology to identify the most popular Cyber Threat Intelligence (CTI) elements and introduces a lightweight approach to correlate those with potential forensic value resulting in the quick and accurate triaging and identification of patterns of malicious activities. Findings-While threat intelligence exchange steadily becomes a common practice for the prevention or detection of security incidents, the proposed approach highlights its usefulness for the Digital Forensics (DF) domain. Originality/value-The proposed model can help organizations to improve their digital forensic readiness posture and thus minimize the time and cost of cybercrime incidents.

The complication of information technology and the proliferation of heterogeneous security devices that produce increased volumes of data coupled with the ever-changing threat landscape challenges have an adverse impact on the efficiency of information security controls and digital forensics, as well as incident response approaches. Cyber Threat Intelligence (CTI)and forensic preparedness are the two parts of the so-called managed security services that defendants can employ to repel, mitigate or investigate security incidents. Despite their success, there is no known effort that has combined these two approaches to enhance Digital Forensic Readiness (DFR) and thus decrease the time and cost of incident response and investigation. This paper builds upon and extends a DFR model that utilises actionable CTI to improve the maturity levels of DFR. The effectiveness and applicability of this model are evaluated through a series of experiments that employ malware-related network data simulating real-world attack scenarios. To this extent, the model manages to identify the root causes of information security incidents with high accuracy (90.73%), precision (96.17%) and recall (93.61%), while managing to decrease significantly the volume of data digital forensic investigators need to examine. The contribution of this paper is twofold. First, it indicates that CTI can be employed by digital forensics processes. Second, it demonstrates and evaluates an efficient mechanism that enhances operational DFR.

In this article, a DFR framework is proposed focusing on the prioritization, triaging and selection of Indicators of Compromise (IoC) to be used when investigating of security incidents. A core component of the framework is the contextualization of the IoCs to the underlying organization, which can be achieved with the use of clustering and classification algorithms and a local IoC database.

In this article, a DFR framework is proposed focusing on the prioritization, triaging and selection of Indicators of Compromise (IoC) to be used when investigating of security incidents. A core component of the framework is the contextualization of the IoCs to the underlying organization, which can be achieved with the use of clustering and classification algorithms and a local IoC database.

In this article, a DFR framework is proposed focusing on the prioritization, triaging and selection of Indicators of Compromise (IoC) to be used when investigating of security incidents. A core component of the framework is the contextualization of the IoCs to the underlying organization, which can be achieved with the use of clustering and classification algorithms and a local IoC database.