Semantic similarity detection mainly relies on the availability of laboriously curated ontologies, as well as of supervised and unsupervised neural embedding models. In this paper, we present two domain-specific sentence embedding models trained on a natural language requirements dataset in order to derive sentence embeddings specific to the software requirements engineering domain. We use cosine-similarity measures in both these models. The result of the experimental evaluation confirm that the proposed models enhance the performance of textual semantic similarity measures over existing state-of-the-art neural sentence embedding models: we reach an accuracy of 88.35%-which improves by about 10% on existing benchmarks.

Part 2: Security, Access Control and Intrusion DetectionInternational audienceThis paper aims to propose a new trust-based Intrusion Detection system (IDS) for wireless, ad-hoc networks with or without mobility of nodes. In fact, the proposed solution not only detects intrusions but also proactively responds towards route setup avoiding the compromised nodes. It could be extended for mesh or hybrid networking environment too. Trust is evaluated as the weighted sum of direct evaluation of the neighboring nodes as well as from the indirect references. A sliding window is defined on the time scale and the IDS is to be evoked after every time slice. Indirect reference is derived from the recommendations of those 1–hop neighbors of the target node that are also neighbors of the evaluating node. The performance of the proposed algorithm has been evaluated using the Qualnet network simulator. Simulation results also establish superiority of the proposed algorithm over HIDS, another recent trust-based IDS for wireless ad-hoc network

Abstr act. An Intrusion Detection System (IDS) detects malicious and selfish nodes in a network. Ad hoc networks are often secured by using either intrusion detection or by secure routing. Designing efficient IDS for wireless ad-hoc networks that would not affect the performance of the network significantly is indeed a challenging task. Arguably, the most common thing in a review paper in the domain of wireless networks is to compare the performances of different solutions using simulation results. However, variance in multiple configuration aspects including that due to different underlying routing protocols, makes the task of simulation based comparative evaluation of IDS solutions somewhat unrealistic. In stead, the authors have followed an analytic approach to identify the gaps in the existing IDS solutions for MANETs and wireless mesh networks. The paper aims to ease the job of a new researcher by exposing him to the state of the art research issues on IDS. Nearly 80% of the works cited in this paper are published with in last 3 to 4 years.

In the design of autonomous systems, it is important to consider the preferences of the interested parties to improve the user experience. These preferences are often associated with the contexts in which each system is likely to operate. The operational behavior of a system must also meet various non-functional requirements (NFRs), which can present different levels of conflict depending on the operational context. This work aims to model correlations between the individual contexts and the consequent conflicts between NFRs. The proposed approach is based on analyzing the system event logs, tracing them back to the leaf elements at the specification level and providing a contextual explanation of the system’s behavior. The traced contexts and NFR conflicts are then mined to produce Context-Context and Context-NFR conflict sequential rules. The proposed Contextual Explainability (ConE) framework uses BERT-based pre-trained language models and sequential rule mining libraries for deriving the above correlations. Extensive evaluations are performed to compare the existing state-of-the-art approaches. The best-fit solutions are chosen to integrate within the ConE framework. Based on experiments, an accuracy of 80%, a precision of 90%, a recall of 97%, and an F1-score of 88% are recorded for the ConE framework on the sequential rules that were mined.

Non-functional requirements (NFRs) play a significant role in the software development process. However, the classical requirement prioritization methods for incremental software development, typically, consider the attributes of functional features only, often neglecting the non-functional constraints. This might lead to catastrophic defects in the system design, as the conflicts among the NFRs are ignored. In this paper, a novel prioritization approach for functional requirements (FRs) is introduced within the incremental software development process. The proposed approach considers the conflicts among NFRs, in the prioritization process, to minimize the incon sistencies of software development. The devised NFR-aware prioritization algorithm can be tuned according to the weights that the analyst assigns to NFR conflicts and FR-NFR dependencies. We have assessed our prioritization approach using available requirements data sets and have compared the results in different scenarios.