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.

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.