The Internet of Things (IoT) is ubiquitous in modern life and is being used very widely in industrial control systems, smart grids, home appliances and many more. IoT devices are used to get information from sensors, process information, and send signals to actuators and controllers. In general these devices form a distributed computing network while in operation. Malware in IoT or any embedded devices is a potential security threat. Detecting malware in such a setting while in operation is non-trivial, because these low power devices may not have the computational ability to perform traditional security operations. Additionally, an infected device may cause other machines to misbehave by interfering with the data they receive. Remote Attestation is a security service designed to detect an infection in a device well before the malware detonates. Recent works have turned their attention to service attestation, or attesting the service that a network provides, rather than the individual devices themselves. Traditional remote attestation schemes use cryptographic hashing algorithms as evidence, but this approach generates exponentially more hashes as heterogeneous IoT devices are added to the network and their jobs’ complexity increases. In this work, we propose an approach to collect the contents of executable virtual memory from an IoT device. We develop a protocol based on our approach that can build a profile of a process running on an IoT device, such evidence can be analyzed automatically with high granularity. We validate our protocol by testing on both a personal computer, and a real-world Industrial IoT device under process injection attacks. Our results show that our protocol will be able to detect small changes to process memory over time, and that an injection as small as one word can be detected and read.

Context: Supervised learning-based projects (SLPs), i.e., software projects that use supervised learning algorithms, such as decision trees are useful for performing classification-related tasks. Yet, security weaknesses, such as the use of hard-coded passwords in SLPs, can make SLPs susceptible to security attacks. A characterization of security weaknesses in SLPs can help practitioners understand the security weaknesses that are frequent in SLPs and adopt adequate mitigation strategies. Objective: The goal of this paper is to help practitioners se-curely develop supervised learning-based projects by conducting an empirical study of security weaknesses in supervised learning-based projects. Methodology: We conduct an empirical study by quantifying the frequency of security weaknesses in 278 open source SLPs. Results: We identify 22 types of security weaknesses that occur in SLPs. We observe ‘use of potentially dangerous function’ to be the most frequently occurring security weakness in SLPs. Of the identified 3,964 security weaknesses, 23.79 % and 40.49 % respectively, appear for source code files used to train and test models. We also observe evidence of co-location, e.g., instances of command injection co-locates with instances of potentially dangerous function. Conclusion: Based on our findings, we advocate for a shift left approach for SLP development with security-focused code reviews, and application of security static analysis.

Abstract Requirements change both during and after a phase of development for a variety of reasons, including error correction and feature changes. Requirements change management is one of the most complex and difficult problems to deal with in requirements elicitation and tracking. It is generally not understood how a specific change propagates through the specification and into the code. In this paper we capture requirements changes as series of atomic changes in specifications. Using a rigorous specification method called sequence‐based specification, we propose a set of algorithms for managing all possible atomic requirements changes. The algorithms have been formulated within an axiom system for sequence‐based specification and proven for correctness. They have also been implemented in a prototype tool with which users are able to push requirements changes through to changes in specifications, maintain old specifications over time and evolve them into new specifications with the least amount of human interaction and rework. The approach of utilizing state machines to model and manage requirements changes guarantees strong evidence about the correctness and completeness of the proposed theory that will lead to more reliable software in the presence of change, especially with embedded systems and safety‐critical systems. The solution described is general enough for adoption by software and system developers, and well suited for incremental development. Copyright © 2008 John Wiley & Sons, Ltd.

Rigorous specification early in the software development process can greatly reduce the cost of later development and maintenance, as well as provide an explicit means to manage risk and identify and meet safety requirements. Sequence-based software specification is a collection of techniques for implementing rigorous, practical software specification. The primary result of this research is the sequence enumeration method of specification writing. Straightforward, systematic enumeration of all sequences to produce an arguably complete, consistent, and traceably correct specification is made practical by controlling the growth of the process.

Specification of software under the box structure method requires a complete, consistent, and traceably-correct description of behavior solely in terms of external stimuli and responses. Such a specification, also called a black box, can be derived from the requirements through straightforward, systematic enumeration of all stimulus sequences. Enumeration is made manageable by the application of techniques for controlling the growth of this inherently combinatorial process, and specifications at different levels of abstraction may be combined to refine a black box specification. This work presents a unifying framework for development of specifications and testing models, and the focus on requirements traceability provides an explicit means to manage requirements change. © 1998 John Wiley & Sons, Ltd.