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Scrubbing sensitive data before releasing memory is a widely accepted but often ignored programming practice for developing secure software. Consequently, confidential data such as cryptographic keys, passwords, and personal data, can remain in memory indefinitely, thereby increasing the risk of exposure to hackers who can retrieve the data using memory dumps or exploit vulnerabilities such as Heartbleed and Etherleak. We propose an approach for detecting a specific memory safety bug called Improper Clearing of Heap Memory Before Release, also known as Common Weakness Enumeration 244, in C programs. The CWE-244 bug in a program allows the leakage of confidential information when a variable is not wiped before heap memory is freed. Our approach combines taint analysis and model checking to detect this weakness. We have three main phases: (1) perform a coarse flow-insensitive inter-procedural static analysis on the program to construct a set of pointer variables that could point to sensitive data; (2) instrument the program with required dynamic variable tracking, and assertion logic for memory wiping before deallocation; and (3) invoke a model checker, the C-Bounded Model Checker (CBMC) in our case, to detect assertion violation in the instrumented program. We develop a tool, \toolname, implementing our instrumentation based algorithm, and we provide experimental validation on the Juliet Test Suite --- the tool is able to detect all the CWE-244 instances present in the test suite. To the best of our knowledge, this is the first work which presents a solution to the problem of detecting unscrubbed secure memory deallocation violations in programs. 

The Internet-of-Things (IoT) has brought in new challenges in device identification --what the device is, and authentication --is the device the one it claims to be. Traditionally, the authentication problem is solved by means of a cryptographic protocol. However, the computational complexity of cryptographic protocols and/or problems related to key management, render almost all cryptography based authentication protocols impractical for IoT. The problem of device identification is, on the other hand, sadly neglected. Almost always an artificially created identity is softly associated with the device. We believe that device fingerprinting can be used to solve both these problems effectively. In this work, we present a methodology to perform IoT device behavioral fingerprinting that can be employed to undertake strong device identification. A device behavior is approximated using features extracted from the network traffic of the device. These features are used to train a machine learning model that can be used to detect similar device-types. We validate our approach using five-fold cross validation; we report a identification rate of 93-100 and a mean accuracy of 99%, across all our experiments. Furthermore, we show preliminary results for fingerprinting device categories, i.e., identifying different devices having similar functionality. 

Rapid advances in the Internet‐of‐Things (IoT) domain have led to the development of several useful and interesting devices that have enhanced the quality of home living and industrial automation. The vulnerabilities in the IoT devices have rendered them susceptible to compromise and forgery. The problem of device authentication, that is, the question of whether a device's identity is what it claims to be, is still an open problem. Device fingerprinting seems to be a promising authentication mechanism. Device fingerprinting profiles a device based on information available about the device and generate a robust, verifiable and unique identity for the device. Existing approaches for device fingerprinting may not be feasible or cost‐effective for the IoT domain due to the resource constraints and heterogeneity of the IoT devices. Due to resource and cost constraints, behavioral fingerprinting provides promising directions for fingerprinting IoT devices. Behavioral fingerprinting allows security researchers to understand the behavioral profile of a device and to establish some guidelines regarding the device operations. In this article, we discuss existing approaches for behavioral fingerprinting of devices in general and evaluate their applicability for IoT devices. Furthermore, we discuss potential approaches for fingerprinting IoT devices and give an overview of some of the preliminary attempts to fingerprint IoT devices. We conclude by highlighting the future research directions for fingerprinting in the IoT domain.

This article is categorized under:

Application Areas > Science and Technology

Application Areas > Internet

Technologies > Machine Learning

Application Areas > Industry Specific Applications