Botnet attacks are a major concern in the cyber-security field. Cyber-attacks involving malware and bots are on the rise infecting more and more computers daily. While numerous approaches exist for detecting malicious traffic generated by bots, the ever-growing diversity of botnet categories continues to pose a significant challenge. As a result, a generalized approach is necessary. Differentiating between benign and malicious traffic largely depends on the traffic features that are being used to feed in the detection process. In this paper, we propose a new and better approach of selecting the best features for effective botnet detection. At first, we modify an existing metaheuristics-based algorithm, namely, binary particle swarm optimization (BPSO), apply it for feature selection and then compare its performance with existing feature selection algorithms. Moreover, we compared our modified BPSO algorithm’s performance with the classical version of it. Through experiments, we show the efficacy of our proposed feature selection algorithm. The results demonstrate that our modified BPSO algorithm outperforms state-of-the-art feature selection algorithms in multiple aspects. Additionally, it surpasses the classical BPSO variant, further highlighting its effectiveness and improvements.

In today's digital economy, organisations build soft-ware systems to support their Collaborative Business Processes (CBPs). Building such systems is a diligent and time-consuming task that requires both business and technical skills. Service-Oriented Architecture (SOA) offers design principals based on the service concept to help align CBPs with their supporting systems while helping organisations achieve their business goals. This paper aims to design a model-driven method that bridges the gap between CBPs and the systems that support them. Its objective is two-fold: i) design SOA-based software solutions that support CBPs expressed in BPMN and ii) build a set of goal models that link each service to the collaborative activity it automates and the business goals it satisfies.

Abstract Robotic Process Automation (RPA) is an emerging software technology for automating business processes. RPA uses software robots to perform repetitive and error‐prone tasks previously done by human actors quickly and accurately. These robots mimic humans by interacting with existing software applications through user interfaces (UI). The goal of RPA is to relieve employees from repetitive and tedious tasks to increase productivity and to provide better service quality. Yet, despite all the RPA benefits, most organizations fail to adopt RPA. One of the main reasons for the lack of adoption is that organizations are unable to effectively identify the processes that are suitable for RPA. This paper proposes a new method, called Rule‐based robotic process analysis (RRPA), that assists process automation practitioners to classify business processes according to their suitability for RPA. The RRPA method computes a suitability score for RPA using a combination of two RPA goals: (i) the RPA feasibility, which assesses the extent to which the process or the activity lends itself to automation with RPA and (ii) the RPA relevance, which assesses whether the RPA automation is worthwhile. We tested the RRPA method on a set of 13 processes. The results showed that the method is effective at 82.05% and efficient at 76.19%.

By suppressing and cancelling the self-interference, the wireless full-duplex (FD) radios have shown promising results. Although theoretically the network throughput is assumed to be doubled in FD channels, the system throughput is far below 2 in reality due to the existence of hidden station problems. Therefore, a carefully designed MAC protocol is needed to avail full benefit of full-duplex radios. In this paper, we propose a novel FRTS/FCTS-based MAC protocol to further mitigate hidden station problems and improve the delay and throughput performance by introducing tertiary transmissions (TT) besides the previously known primary and secondary transmissions. Through realistic simulations in ns-3, we show that the proposed FD MAC protocol outperforms the conventional half-duplex MAC protocol for wireless networks in terms of system through-put. Also, the proposed FD MAC outperforms its counterpart busy tone-based FD MAC in terms of average delay.

Summary We present a case study inmodel‐driven developmentof ane‐tourism portalthat we chose to develop throughgenerationfrom adomain modelencoded as anontology. We present (1) the requirements of e‐tourism portal, which dictated its high‐level design; (2) the principles behind our implementation strategy, including the use of adomain ontologyas a starting model within the context of a model‐driven transformational approach; (3) the ontology development process and the code generation strategy used; and (4) the lessons learned. In particular, we compare our experiences to those reported in the model‐driven engineering (MDE) literature along 3 dimensions, ie, (1) the impact of MDE on the development process, (2) the choice of the modeling approach, and (3) the impact of code generation on design and code quality and testing. Overall, our experiences corroboratedsomeof thetheoretical claimsandmanyof thepractical experienceswith MDE. Key findings include (1) model‐driven development makes maintenance, not development, more efficient; (2) it does require a higher skill level than traditional development; (3) clients and managers need to be educated into whatincrementalitymeans in agenerative approach; (4) UML is neither necessary nor sufficient to handle the required representational flexibility; (5) it is difficult to build models that are good for both human consumption and code generation; and (6) it is difficult to generate code that is, simultaneously, efficient, pretty, and easy to maintain. We conclude by summarizing the findings of the paper.

Flexible processes emerged to provide flexibility to business process execution. A flexible process is not static and can have several different executions, that is influenced by the current situation. In this context, the decision-making is placed in the hands of any knowledge worker during the execution, who decides which tasks and in which order they will be executed. Two approaches for flexible processes are discussed in this paper: case management and declarative processes. In particular we use the CMMN standard and the ConDec language for the two approaches, respectively. We compare them based on scope, model representation, formal semantics, and limitations. Our goal is to present commonalities and differences between the languages in order to identify potential extensions to make them more complete to attain more flexible process examples.

Organizations build information systems to support their business processes. Some of these business processes are industry or organization-specific, but most are common to many industries and are used as is, modulo a few modifications. Our work tries to capitalize on these similarities to develop a methodology and tools that help business analysts generate organization-specific process models from a catalog of generic business processes. We developed a framework for representing and classifying business processes that supports process variability management by, 1) navigating a repository of generic processes, and 2) automatically generating new process variants around key process variation points. We use business patterns from the Resource Event Agent ontology to identify variation points, and to codify the model transformations inherent in the generation of the process variants. We developed a prototype, showing the computational feasibility of the approach, and validated the relevance of the variation points, and the correctness of corresponding transformations in the context of ERP key processes, showing the conceptual soundness of the approach.