The Internet of Things (IoT) envisions pervasive, connected, and smart nodes interacting autonomously while offering all sorts of services. Wide distribution, openness and relatively high processing power of IoT objects made them an ideal target for cyber attacks. Moreover, as many of IoT nodes are collecting and processing private information, they are becoming a goldmine of data for malicious actors. Therefore, security and specifically the ability to detect compromised nodes, together with collecting and preserving evidences of an attack or malicious activities emerge as a priority in successful deployment of IoT networks. In this paper, we first introduce existing major security and forensics challenges within IoT domain and then briefly discuss about papers published in this special issue targeting identified challenges.

No abstract

No abstract

Recent results of forgery detection by implementing biometric signature verification methods are promising. At present, forensic signature verification in daily casework is performed through visual examination by trained forensic handwriting experts, without reliance on computerassisted methods. With this competition on on-and offline skilled forgery detection, our objective is to make a first step towards bridging the gap between automated biometric performances and expert-based visual comparisons. We intent to combine realistic forensic casework with automated methods by testing systems on a forensic-like new dataset. The results achieved by the participating systems are promising: 2.85% Equal Error Rate (EER) on the online data and 9.15% on the offline data. From these results we indicate that automated methods might be able to support forensic handwriting experts (FHEs) to formulate the strength of evidence that needs to be reported in court in the future.

This paper presents an approach for ink type recognition. Ink type classes will be derived from the physical properties of ink. Ink specific trace morphologies are considered as textures. From these discriminant texture features of the co-occurrence matrix will be derived. The proposed method for automated ink type recognition was tested using 62 different kinds of pens and refills. The achieved recognition result of 99.7% for 600 dpi and 98.4% for 300 dpi handwritings further promotes the study of trace morphologies in particular for application in forensic writer identification