As medical devices more and more use Internet of Things based technologies, serious concerns are raised about their security and the privacy of patient's personal health data. To address these concerns, while maintaining reasonable overheads, designers of medical devices need to take security into account from the beginning until the completion of their designs. In this work we identify the relevant security domains and focus to the Hardware Security perspective. Additionally, we present a secure design and evaluation framework which can assist designers towards more secure medical devices. The framework integrates a complete insulin pump architecture containing all the basic components used in such applications. To illustrate the advantages of the proposed framework we perform a Side Channel Analysis attack against the embedded encryption algorithm of the device to obtain the secret encryption key. Then, we make use of the framework to identify all the components of the system which are either directly or indirectly affected by the attack. This analysis leads us to determine more complex combined attacks which may complement the SCA attack into compromising the overall security of the system.

During the past years, deep packet inspection has been prevalent in network intrusion detection systems. Most solutions employ complex algorithms to analyze the intended behaviour and underlying characteristics of packets and their payloads, in an effort to detect and prevent malicious users and software from communicating over business intranets and wider networks. Still, there are multiple issues that inhibit their success rate. Most signature-based security software is plagued by false positives and/or false negatives. On the other hand, behavioral-based solutions achieve better detection rates but need to analyze large amounts of traffic. In this article, we present a real-time network traffic monitoring system that implements machine learning over side channel characteristics of TCP network packets to distinguish normal from malicious TCP sessions, even when encryption is in place. We test in university networks and test multiple different types of traffic. We show that, our approach (i) requires notably less information to achieve similar (if not better) detection rates, (ii) works over encrypted traffic as well, and (iii) has notably low false positives and false negatives in everyday case study scenarios.

Context: Modern automated source code analysis techniques can be very successful in detecting a priori de- fined defect patterns and security vulnerabilities. Yet, they cannot detect flaws that manifest due to erroneous translation of the software’s functional requirements into the source code. The automated detection of logical errors that are attributed to a faulty implementation of applications’ functionality, is a relatively uncharted territory. In previous research, we proposed a combination of automated analyses for logical error detection. In this paper, we develop a novel business-logic oriented method able to filter mathematical depictions of software logic in order to augment logical error detection, eliminate previous limitations in analysis and provide a formal tested logical error detection classification without subjective discrepancies. As a proof of concept, our method has been implemented in a prototype tool called PLATO that can detect various types of logical errors. Potential logical errors are thus detected that are ranked using a fuzzy logic system with two scales characterizing their impact: (i) a Severity scale, based on the execution paths’ characteristics and Information Gain, (ii) a Reliability scale, based on the measured program’s Computational Density. The method’s effectiveness is shown using diverse experiments. Albeit not without restrictions, the proposed automated analysis seems able to detect a wide variety of logical errors, while at the same time limiting the false positives.

Abstract: Recent advances in static and dynamic program analysis resulted in tools capable to detect various types of security bugs in the Applications under Test (AUTs). However, any such analysis is designed for a priori specified types of bugs and it is characterized by some rate of false positives or even false negatives and certain scalability limitations. We present a new analysis and source code classification technique, and a pro-totype tool aiming to aid code reviews in the detection of general information flow dependent bugs. Our approach is based on classifying the criticality of likely exploits in the source code using two measuring functions, namely Severity and Vulnerability. For an AUT, we analyse every single pair of input vector and program sink in an execution path, which we call an Information Block (IB). A classification technique is introduced for quantifying the Severity (danger level) of an IB by static analysis and computation of its En-tropy Loss. An IB’s Vulnerability is quantified using a tainted object propagation analysis along with a Fuzzy Logic system. Possible exploits are then characterized with respect to their Risk by combining the computed Severity and Vulnerability measurements through an aggregation operation over two fuzzy sets. An IB is characterized of a high risk, when both its Severity and Vulnerability rankings have been found to be above the low zone. In this case, a detected code exploit is reported by our prototype tool, called Entroine. The effectiveness of our approach has been tested by analysing 45 Java programs of NIST’s Juliet Test Suite, which implement three different common weakness exploits. All existing code exploits were detected without any false positive. 1