Visualization for network forensic analyses: extending the Forensic Log Investigator (FLI)

In a network attack investigation, the mountain of information collected from varying sources can be daunting. Investigators face significant challenges in being able to correlate findings from these sources, given difficulties with time synchronization. In addition, it is difficult to obtain summary or overview information for one set of data, much less the entire case. This, in turn, makes it nearly impossible to accurately identify missing information.;Identifying these information gaps is one problem, yet another is filling them in. Investigators must rely on legal processes and requests to obtain the information they need. However, it is extremely important they are aware of cases or events that cross jurisdictional boundaries. Where tools exist to assist in evidence overview, they do not contain the necessary geographic information for investigators to quickly ascertain the location of those involved.;In addition to these difficulties, investigators need to perform several types of analysis on the evidence that has been collected. Several of these analyses cannot typically be performed on data from multiple log files, since they are based on timing data. Furthermore, it is difficult to understand results from these analyses without visual representation, and there are no tools to bring them together in a single frame.;This thesis details the design and implementation of an analysis and visualization extension for the Forensic Log Investigator, or FLI. FLI is a web-based analysis and visualization architecture built on advanced technologies and enterprise infrastructure. This extension assists investigators by providing the ability to correlate evidence and analysis across traditional log file and analysis method boundaries, identify information gaps, and perform analysis in accordance with published evidence handling guidelines

Handling of advanced persistent threats and complex incidents in healthcare, transportation and energy ICT infrastructures

In recent years, the use of information technologies in Critical Infrastructures is gradually increasing. Although this brings benefits, it also increases the possibility of security attacks. Despite the availability of various advanced incident handling techniques and tools, there is still no easy, structured, standardized and trusted way to manage and forecast interrelated cybersecurity incidents. This paper introduces CyberSANE, a novel dynamic and collaborative, warning and response system, which supports security officers and operators to recognize, identify, dynamically analyse, forecast, treat and respond to security threats and risks and and it guides them to handle effectively cyber incidents. The components of CyberSANE are described along with a description of the CyberSANE data flow. The main novelty of the CyberSANE system is the fact that it enables the combination of active incident handling approaches with reactive approaches to support incidents of compound, highly dependent Critical Information Infrastructures. The benefits and added value of using CyberSANE is described with the aid of a set of cyber-attack scenarios

Data-driven decision support for optimizing cyber forensic investigations

Cyber attacks consisting of several attack actions can present considerable challenge to forensic investigations. Consider the case where a cybersecurity breach is suspected following the discovery of one attack action, for example by observing the modification of sensitive registry keys, suspicious network traffic patterns, or the abuse of legitimate credentials. At this point, the investigator can have multiple options as to what to check next to discover the rest, and will likely pick one based on experience and training. This will be the case at each new step. We argue that the efficiency of this aspect of the job, which is the selection of what next step to take, can have significant impact on its overall cost (e.g., the duration) of the investigation and can be improved through the application of constrained optimization techniques. Here, we present DISCLOSE, the first data-driven decision support framework for optimizing forensic investigations of cybersecurity breaches. DISCLOSE benefits from a repository of known adversarial tactics, techniques, and procedures (TTPs), for each of which it harvests threat intelligence information to calculate its probabilistic relations with the rest. These relations, as well as a proximity parameter derived from the projection of quantitative data regarding the adversarial TTPs on an attack life cycle model, are both used as input to our optimization framework. We show the feasibility of this approach in a case study that consists of 31 adversarial TTPs, data collected from 6 interviews with experienced cybersecurity professionals and data extracted from the MITRE ATT&CK STIX repository and the Common Vulnerability Scoring System (CVSS)

A graph oriented approach for network forensic analysis

Network forensic analysis is a process that analyzes intrusion evidence captured from networked environment to identify suspicious entities and stepwise actions in an attack scenario. Unfortunately, the overwhelming amount and low quality of output from security sensors make it difficult for analysts to obtain a succinct high-level view of complex multi-stage intrusions. This dissertation presents a novel graph based network forensic analysis system. The evidence graph model provides an intuitive representation of collected evidence as well as the foundation for forensic analysis. Based on the evidence graph, we develop a set of analysis components in a hierarchical reasoning framework. Local reasoning utilizes fuzzy inference to infer the functional states of an host level entity from its local observations. Global reasoning performs graph structure analysis to identify the set of highly correlated hosts that belong to the coordinated attack scenario. In global reasoning, we apply spectral clustering and Pagerank methods for generic and targeted investigation respectively. An interactive hypothesis testing procedure is developed to identify hidden attackers from non-explicit-malicious evidence. Finally, we introduce the notion of target-oriented effective event sequence (TOEES) to semantically reconstruct stealthy attack scenarios with less dependency on ad-hoc expert knowledge. Well established computation methods used in our approach provide the scalability needed to perform post-incident analysis in large networks. We evaluate the techniques with a number of intrusion detection datasets and the experiment results show that our approach is effective in identifying complex multi-stage attacks

Building Evidence Graphs for Network Forensics Analysis

In this paper, we present techniques for a network forensics analysis mechanism that includes effective evidence presentation, manipulation and automated reasoning. We propose the evidence graph as a novel graph model to facilitate the presentation and manipulation of intrusion evidence. For automated evidence analysis, we develop a hierarchical reasoning framework that includes local reasoning and global reasoning. Local reasoning aims to infer the roles of suspicious hosts from local observations. Global reasoning aims to identify group of strongly correlated hosts in the attack and derive their relationships. By using the evidence graph model, we effectively integrate analyst feedback into the automated reasoning process. Experimental results demonstrate the potential and effectiveness of our proposed approaches.