Firmware code instrumentation technology for internet of things-based services

With the rapid development of electronic and information technology, Internet of Things (IoT) devices have become extensively utilised in various fields. Increasing attention has been paid to the performance and security analysis of IoT-based services. Dynamic instrumentation is a common process in software analysis for acquiring runtime information. However, due to the limited software and hardware resources in IoT devices, most dynamic instrumentation tools do not support IoT-based services. In this paper, we provide an analysis tool, IoTDIT, to solve the current problem of runtime detection in IoT-based services. IoTDIT employs static analysis and ptrace system calls to obtain dynamic firmware information, which can aid in firmware performance analysis and security detection. We perform experiments to verify the performance and effectiveness of the proposed instrumentation tool.

A Methodology for Runtime Detection and Extraction of Threat Patterns

As the confidentiality and integrity of modern health infrastructures is threatened by intrusions and real-time attacks related to privacy and cyber-security, there is a need for proposing novel methodologies to predict future incidents and identify new threat patterns. The main scope of this article is to propose an advanced extension to current Intrusion Detection System (IDS) solutions, which (i) harvests the knowledge out of health data sources or network monitoring to construct models for new threat patterns and (ii) encompasses methods for detecting threat patterns utilizing also advanced unsupervised machine learning data analytic methodologies. Although the work is motivated by the health sector, it is developed in a manner that is directly applicable to other domains.

WORK DONE ON MIDDLEWARE INFRASTRUCTURE FOR PREDICATE DETECTION

Context-awareness is an essential feature of pervasive applications, and runtime detection of contextual properties is one of the primary approaches to enabling context awareness. However, existing context-aware middleware does not provide sufficient support for detection of contextual properties in asynchronous environments. The contextual activities usually involve multiple context collecting devices, which are fully-decentralized and interact in an asynchronous manner. However, existing context consistency checking schemes do not work in asynchronous environments, since they implicitly assume the availability of a global clock or relay on synchronized interactions. To this end, we present the Middleware Infrastructure for Predicate detection in Asynchronous environments (MIPA), which supports context awareness based on logical time. Design and Structure of MIPA are explained in detail.