Several B+-tree variants have been developed to exploit the byte-addressable non-volatile memory (NVM). We attentively investigate the properties of B+-tree and find that, a conventional B+-tree node is a linear structure in which key-value (KV) pairs are maintained from the zero offset of a node. These KV pairs are shifted in a unidirectional fashion for insertions and deletions. Inserting and deleting one KV pair may inflict a large amount of write amplifications due to shifting existing KV pairs. This badly impairs the performance of in-NVM B+-tree. In this article, we propose a novel circular design for B+-tree. With regard to NVM's byte-addressability, our Circ-Tree embraces tree nodes in a circular structure without a fixed base address, and bidirectionally shifts KV pairs for insertions and deletions to minimize write amplifications. We have implemented a prototype for Circ-Tree and conducted extensive experiments. Experimental results show that Circ-Tree significantly outperforms two state-of-the-art in-NVM B+-tree variants, i.e., NV-tree and FAST+FAIR, by up to 1.6× and 8.6×, respectively, in terms of write performance. The end-to-end comparison by running YCSB to KV stores built on NV-tree, FAST+FAIR, and Circ-Tree reveals that Circ-Tree yields up to 29.3 and 47.4 percent higher write performance, respectively, than NV-tree and FAST+FAIR.

We present LKRDet: a framework based on a Trusted Execution Environment to detect Kernel rootkits in IoT devices. LKRDet checks the consistency of hardware events, occurring in specific system call routines, to detect abnormalities caused by the kernel rootkits. LKRDet relies on Hardware Performance Counters to efficiently and safely count the hardware events occurring in the system. We implement a prototype of LKRDet for the ARM TrustZone architecture, on top of the Open Portable Trusted Execution Environment and evaluate our prototype with four popular rootkits. Our evaluation reveals that LKRDet can accurately detect the presence of all the rootkits in the device.

Machine Learning (ML) has seen massive progress in the last decade and as a result, there is a pressing need for validating ML-based systems. To this end, we propose, design and evaluate CALLISTO- a novel test generation and data quality assessment framework. To the best of our knowledge, CALLISTO is the first black box framework to leverage the uncertainty in the prediction and systematically generate new test cases for ML classifiers. Our evaluation of CALLISTO on four real world data sets reveals thousands of errors. We also show that leveraging the uncertainty in prediction can increase the number of erroneous test cases up to a factor of 20, as compared to when no such knowledge is used for testing.CALLISTO has the capability to detect low quality data in the datasets that may contain mislabelled data. We conduct and present an extensive user study to validate the results of CALLISTO on identifying low quality data from four state-of-the-art real world datasets.

The revolutionary development of the Internet of Things has triggered a huge demand for Internet of Things devices. They are extensively applied to various fields of social activities, and concerning manufacturing, they are a key enabling concept for the Industry 4.0 ecosystem. Industrial Internet of Things (IIoT) devices share common vulnerabilities with standard IoT devices, which are increasingly exposed to the attackers. As such, connected industrial devices may become sources of cyber, as well as physical, threats for people and assets in industrial environments. In this work, we examine the attack surfaces of a networked embedded system, composed of devices representative of those typically used in the IIoT field. We carry on an analysis of the current state of the security of IIoT technologies. The analysis guides the identification of a set of attack vectors for the examined networked embedded system. We set up the corresponding concrete attack scenarios to gain control of the system actuators and perform some hazardous operations. In particular, we propose a couple of variations of Mirai attack specifically tailored for attacking industrial environments. Finally, we discuss some possible