Fuzzing has become one of the best-established methods to uncover software bugs. Meanwhile, the market of embedded systems, which binds the software execution tightly to the very hardware architecture, has grown at a steady pace, and that pace is anticipated to become yet more sustained in the near future. Embedded systems also benefit from fuzzing, but the innumerable existing architectures and hardware peripherals complicate the development of general and usable approaches, hence a plethora of tools have recently appeared. Here comes a stringent need for a systematic review in the area of fuzzing approaches for embedded systems, which we term “embedded fuzzing” for brevity. The inclusion criteria chosen in this article are semi-objective in their coverage of the most relevant publication venues as well as of our personal judgement. The review rests on a formal definition we develop to represent the realm of embedded fuzzing. It continues by discussing the approaches that satisfy the inclusion criteria, then defines the relevant elements of comparison and groups the approaches according to how the execution environment is served to the system under test. The resulting review produces a table with 42 entries, which in turn supports discussion suggesting vast room for future research due to the limitations noted.

During the initial stages of software development, the primary goal is to define precise and detailed requirements without concern for software realizations. Security constraints should be introduced then and must be based on the semantic aspects of applications, not on their software architectures, as it is the case in most secure development methodologies. In these stages, we need to identify threats as attacker goals and indicate what conceptual security defenses are needed to thwart these goals, without consideration of implementation details. We can consider the effects of threats on the application assets and try to find ways to stop them. These threats should be controlled with abstract security mechanisms that can be realized by abstract security patterns (ASPs), that include only the core functions of these mechanisms, which must be present in every implementation of them. An abstract security pattern describes a conceptual security mechanism that includes functions able to stop or mitigate a threat or comply with a regulation or institutional policy. We describe here the properties of ASPs and present a detailed example. We relate ASPs to each other and to Security Solution Frames, which describe families of related patterns. We show how to include ASPs to secure an application, as well as how to derive concrete patterns from them. Finally, we discuss their practical value, including their use in “security by design” and IoT systems design.

The collection of user attributes by service providers is a double-edged sword. They are instrumental in driving statistical analysis to train more accurate predictive models like recommenders. The analysis of the collected user data includes frequency estimation for categorical attributes. Nonetheless, the users deserve privacy guarantees against inadvertent identity disclosures. Therefore algorithms called frequency oracles were developed to randomize or perturb user attributes and estimate the frequencies of their values. We propose Sarve, a frequency oracle that used Randomized Aggregatable Privacy-Preserving Ordinal Response (RAPPOR) and Hadamard Response (HR) for randomization in combination with fake data. The design of a service-oriented architecture must consider two types of complexities, namely computational and communication. The functions of such systems aim to minimize the two complexities and therefore, the choice of privacy-enhancing methods must be a calculated decision. The variant of RAPPOR we had used was realized through bloom filters. A bloom filter is a memory-efficient data structure that offers time complexity of O(1). On the other hand, HR has been proven to give the best communication costs of the order of log(b) for b-bits communication. Therefore, Sarve is a step towards frequency oracles that exhibit how privacy provisions of existing methods can be combined with those of fake data to achieve statistical results comparable to the original data. Sarve also implemented an adaptive solution enhanced from the work of Arcolezi et al. The use of RAPPOR was found to provide better privacy-utility tradeoffs for specific privacy budgets in both high and general privacy regimes.

Performance/security trade-off is widely noticed in CFI research, however, we observe that not every CFI scheme is subject to the trade-off. Motivated by the key observation, we ask three questions: ➊ does trade-off really exist in different CFI schemes? ➋ if trade-off do exist, how do previous works comply with it? ➌ how can it inspire future research? Although the three questions probably cannot be directly answered, they are inspiring. We find that a deeper understanding of the nature of the trade-off will help answer the three questions. Accordingly, we proposed the GPT conjecture to pinpoint the trade-off in designing CFI schemes, which says that at most two out of three properties (fine granularity, acceptable performance, and preventive protection) could be achieved.

In recent years, the widespread applications of open-source software (OSS) have brought great convenience for software developers. However, it is always facing unavoidable security risks, such as open-source code defects and security vulnerabilities. To find out the OSS risks in time, we carry out an empirical study to identify the indicators for evaluating the OSS. To achieve a comprehensive understanding of the OSS assessment, we collect 56 papers from prestigious academic venues (such as IEEE Xplore, ACM Digital Library, DBLP, and Google Scholar) in the past 21 years. During the process of the investigation, we first identify the main concerns for selecting OSS and distill five types of commonly used indicators to assess OSS. We then conduct a comparative analysis to discuss how these indicators are used in each surveyed study and their differences. Moreover, we further undertake a correlation analysis between these indicators and uncover 13 confirmed conclusions and four cases with controversy occurring in these studies. Finally, we discuss several possible applications of these conclusions, which are insightful for the research on OSS and software supply chain.

Reading text in images automatically has become an attractive research topic in computer vision. Specifically, end-to-end spotting of scene text has attracted significant research attention, and relatively ideal accuracy has been achieved on several datasets. However, most of the existing works overlooked the semantic connection between the scene text instances, and had limitations in situations such as occlusion, blurring, and unseen characters, which result in some semantic information lost in the text regions. The relevance between texts generally lies in the scene images. From the perspective of cognitive psychology, humans often combine the nearby easy-to-recognize texts to infer the unidentifiable text. In this paper, we propose a novel graph-based method for intermediate semantic features enhancement, called Text Relation Networks. Specifically, we model the co-occurrence relationship of scene texts as a graph. The nodes in the graph represent the text instances in a scene image, and the corresponding semantic features are defined as representations of the nodes. The relative positions between text instances are measured as the weights of edges in the established graph. Then, a convolution operation is performed on the graph to aggregate semantic information and enhance the intermediate features corresponding to text instances. We evaluate the proposed method through comprehensive experiments on several mainstream benchmarks, and get highly competitive results. For example, on the SCUT-CTW1500, our method surpasses the previous top works by 2.1% on the word spotting task.

Anomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system.

This paper proposes an architecture and a protocol suite for a permissioned blockchain for a local IoT network. The architecture is based on a sealed Sequencer and a Fog Server running (post-quantum) Guy Fawkes protocols. The blocks of the blockchain are stored in networked Content Addressable Storage alongside any user data and validity proofs. We maintain that a typical IoT device can, despite its resource limitations, use our blockchain protocols directly, without a trusted intermediary. This includes posting and monitoring transactions as well as off-chain (post-quantum) emergency communications without an explicit public key.

Deep learning (DL) has exhibited its exceptional performance in fields like intrusion detection. Various augmentation methods have been proposed to improve data quality and eventually to enhance the performance of DL models. However, the classic augmentation methods cannot be applied to those DL models which exploit the system-call sequences to detect intrusion. Previously, the seq2seq model has been explored to augment system-call sequences. Following this work, we propose a gated convolutional neural network (GCNN) model to thoroughly extract the potential information of augmented sequences. Also, in order to enhance the model’s robustness, we adopt adversarial training to reduce the impact of adversarial examples on the model. Adversarial examples used in adversarial training are generated by the proposed adversarial sequence generation algorithm. The experimental results on different verified models show that GCNN model can better obtain the potential information of the augmented data and achieve the best performance. Furthermore, GCNN with adversarial training can enhance robustness significantly.