Hardware constraints, energy efficiency, and reliability requirements make building a confidential and mutually authenticating service for a WBAN challenging. It is possible for an adversary to gather information about the user’s medical conditions if they are able to distinguish the source of the data from other data sources. It is, therefore, important, in addition to confidentiality and mutual authentication, to also provide anonymity and untraceability of the sensor nodes sending the personal (i.e., medical) data. This paper proposes a new authentication and key agreement scheme, with anonymity and untraceability. The new scheme is shown to be efficient, and its security has been tested using the informal analysis tool Scyther, AVISPA and proven formally using BAN logic.
COBISS.SI-ID: 21864982
The basis of blockchain-related data, stored in distributed ledgers, are digitally signed transactions. Data can be stored on the blockchain ledger only after a digital signing process is performed by a user with a blockchain-based digital identity. However, this process is time-consuming and not user-friendly, which is one of the reasons blockchain technology is not fully accepted. In this paper, we propose a machine learning-based method, which introduces automated signing of blockchain transactions, while including also a personalized identification of anomalous transactions. In order to evaluate the proposed method, an experiment and analysis were performed on data from the Ethereum public main network. The analysis shows promising results and paves the road for a possible future integration of such a method in dedicated digital signing software for blockchain transactions.
COBISS.SI-ID: 22899734
The main purpose of business process diagrams is to make the communication between process-related stakeholders more effective. To this end, they need to be simple to read, which is often challenging to achieve. In this manner, the complexity of business process diagrams can negatively affect their correctness and understandability. The goal of this paper was to investigate an approach that makes business process diagrams appear less complex, without changing the corresponding notation. This was done by manipulating one of the properties of the notation’s elements, namely opacity. Firstly, a literature overview was performed in order to obtain the theoretical foundations. Secondly, an exploratory case study was conducted and the results were applied in practice. Finally, the proposed solution was implemented in the form of a prototype soft-ware solution. Our analysis demonstrated that the structural complexity of the diagrams decreases when applying the proposed solution.
COBISS.SI-ID: 20748566
Different challenges arise while detecting deficient software source code. Usually a large number of potentially problematic entities are identified when an individual software metric or individual quality aspect is used for the identification of deficient program entities. Additionally, a lot of these entities quite often turn out to be false positives, i.e., the metrics indicate poor quality whereas experienced developers do not consider program entities as problematic. The number of entities identified as potentially deficient does not decrease significantly when the identification of deficient entities is carried out by applying code smell detection rules. Moreover, the intersection of entities identified as allegedly deficient among different code smell detection tools is small, which suggests that the implementation of code smell detection rules are not consistent and uniform. To address these challenges, a novel approach for identifying deficient entities that is based on applying the majority function on the combination of software metrics was proposed. Program entities are assessed according to selected quality aspects that are evaluated with a set of software metrics and corresponding threshold values derived from benchmark data, considering the statistical distributions of software metrics values. The proposed approach was implemented and validated on projects developed in Java, C++ and C#. The validation of the proposed approach was done with expert judgment, where software developers and architects with multiple years of experiences assessed the quality of the software classes. Using a combination of software metrics as the criteria for the identification of deficient source code, the number of potentially deficient object-oriented program entities proved to be reduced. The results show the correctness of quality ratings determined by the proposed identification approach, and most importantly, confirm the absence of false positive entities.
COBISS.SI-ID: 21779990
Self-admitted technical debt (SATD) is annotated in source code comments by developers and has been recognized as a great source of discovering flawed software. To reduce manual effort, some recent studies have focused on automated detection of SATD using text classification methods. To train their classifier, these methods need labeled samples, which also require a lot of effort to obtain. We developed a new SATD identification method, which takes advantage of a large corpus of unlabeled code comments, extracted from open source projects, to train a word embedding model. After applying feature selection, the pre-trained word embedding is used for discovering semantically similar features in source code comments to enhance the original feature set. By using such enhanced feature set for classification, we wanted to improve the identification of SATD when compared to existing methods. The developed feature enhancement method was used with different feature selection methods and various well-known text classification algorithms on multiple open source projects. The developed method achieved a significant improvement in SATD identification over the compared methods. With an achieved 82% of correct predictions of SATD, it seems to be a good candidate to be adopted in software engineering practice.
COBISS.SI-ID: 22534934