Biblio

Security incident handling and response are essen-tial parts of every organization's information and cyber security. Security incident handling consists of several phases, among which digital forensic analysis has an irreplaceable place. Due to particular digital evidence being recorded at a specific time, timelines play an essential role in analyzing this digital evidence. One of the vital tasks of the digital forensic investigator is finding relevant records in this timeline. This operation is performed manually in most cases. This paper focuses on the possibilities of automatically identifying digital evidence pertinent to the case and proposes a model that identifies this digital evidence. For this purpose, we focus on Windows operating system and the NTFS file system and use outlier detection (Local Outlier Factor method). Collected digital evidence is preprocessed, transformed to binary values, and aggregated by file system inodes and names. Subsequently, we identify digital records (file inodes, file names) relevant to the case. This paper analyzes the combinations of attributes, aggregation functions, local outlier factor parameters, and their impact on the resulting selection of relevant file inodes and file names.

In this paper, we discuss the digital forensic procedure and techniques for analyzing the local artifacts from four popular Instant Messaging applications in Android. As part of our findings, the user chat messages details and contacts were investigated for each application. By using two smartphones with different brands and the latest Android operating systems as experimental objects, we conducted digital investigations in a forensically sound manner. We summarize our findings regarding the different Instant Messaging chat modes and the corresponding encryption status of artifacts for each of the four applications. Our findings can be helpful to many mobile forensic investigations. Additionally, these findings may present values to Android system developers, Android mobile app developers, mobile security researchers as well as mobile users.

This article describes a framework for semantic annotation of texts that are submitted for forensic analysis, based on Frame Semantics, and a knowledge base of Forensic Frames - FrameFOR. We demonstrate through experimental evaluations that the application of the Semantic Role Labeling (SRL) techniques and Natural Language Processing (NLP) in digital forensic increases the performance of the forensic experts in terms of agility, precision and recall.

Volume anomaly such as distributed denial-of-service (DDoS) has been around for ages but with advancement in technologies, they have become stronger, shorter and weapon of choice for attackers. Digital forensic analysis of intrusions using alerts generated by existing intrusion detection system (IDS) faces major challenges, especially for IDS deployed in large networks. In this paper, the concept of automatically sifting through a huge volume of alerts to distinguish the different stages of a DDoS attack is developed. The proposed novel framework is purpose-built to analyze multiple logs from the network for proactive forecast and timely detection of DDoS attacks, through a combined approach of Shannon-entropy concept and clustering algorithm of relevant feature variables. Experimental studies on a cyber-range simulation dataset from the project industrial partners show that the technique is able to distinguish precursor alerts for DDoS attacks, as well as the attack itself with a very low false positive rate (FPR) of 22.5%. Application of this technique greatly assists security experts in network analysis to combat DDoS attacks.

Digital Forensics is an area of Forensics Science that uses the application of scientific method toward crime investigation. The thwarting of forensic evidence is known as anti-forensics, the aim of which is ambiguous in the sense that it could be bad or good. The aim of this project is to simulate digital crimes scenario and carry out forensic and anti-forensic analysis to enhance security. This project uses several forensics and anti-forensic tools and techniques to carry out this work. The data analyzed were gotten from result of the simulation. The results reveal that although it might be difficult to investigate digital crime but with the help of sophisticated forensic tools/anti-forensics tools it can be accomplished.

An aspect of database forensics that has not received much attention in the academic research community yet is the presence of database triggers. Database triggers and their implementations have not yet been thoroughly analysed to establish what possible impact they could have on digital forensic analysis methods and processes. Conventional database triggers are defined to perform automatic actions based on changes in the database. These changes can be on the data level or the data definition level. Digital forensic investigators might thus feel that database triggers do not have an impact on their work. They are simply interrogating the data and metadata without making any changes. This paper attempts to establish if the presence of triggers in a database could potentially disrupt, manipulate or even thwart forensic investigations. The database triggers as defined in the SQL standard were studied together with a number of database trigger implementations. This was done in order to establish what aspects might have an impact on digital forensic analysis. It is demonstrated in this paper that some of the current database forensic analysis methods are impacted by the possible presence of certain types of triggers in a database. Furthermore, it finds that the forensic interpretation and attribution processes should be extended to include the handling and analysis of database triggers if they are present in a database.