Comparing measures of sensitivity for vigilance tasks: Bayesian-estimated and log-linear corrected measures outperform A!

Signal detection analyses often attribute the vigilance decrement to a combination of bias shifts and sensitivity losses. In many vigilance experiments, however, false alarm rates are at or near zero, complicating the analysis of sensitivity. Here, we report Monte Carlo simulations comparing three measures of sensitivity that can be calculated even with extreme hit and false alarm rates: A’, an estimate of the area under the curve that is commonly but mistakenly described as nonparametric; Az calculated using the log-linear correction, a statistic that adjusts individual observers’ data to protect against low false alarm rates; and, 4z estimated using a Bayesian hierarchical procedure, a measure that protects against extreme false alarm rates by sharing information between observers. Results confirm that bias shifts produce spurious changes in A’, and demonstrate that, 4z estimated with either a log-linear correction or through hierarchical Bayesian modeling is more robust against low false alarm rates.

Flow based intrusion Detection system with Whale Optimization and Evolutionary Algorithms (EA) for Diversified Traffic Streams

The technological growth and advances in the internet led to the generation of huge volume of data that networks must be capable of transmitting. Providing security to this data is a challenging task. The development in the internet attracts several vulnerable attacks. The researchers in the literature proposed several machine learning, Deep learning and ANN based approaches for efficient attack detection. However, these approaches are prone to high false alarm rates and exhibits poor performance for diversified incoming traffic, because these methodologies relay on the packet level or transaction level features. The performance is inversely proposal to the diversity ratio of packet level features. To handle this, we introduced a combination of high-performed evolutionary algorithms and neural networks for attack classification at flow level with low false alarm rates and high detection accuracy. A unique set of flow features are defined to handle the traffic at flow level and optimal feature selection using whale Optimization Algorithm (WOA). The gravitational search (GS), and particle swarm optimization (PSO) combinations are used in attack detection phase to train the ANN and results proposed model as GSPSO-ANN with WOA. The performance of the proposed model is evaluated with NSL-KDD and CSE-CIC-IDS2018 datasets. The results are compared with other ANN based conventional methods. The results inferred that the proposed GSPSO-ANN with WOA attained maximum detection accuracy with low false alarm rates and processing time and also maintained consistency in the performance for diversified traffic.

Doorways do not always cause forgetting: a multimodal investigation

Background The ‘doorway effect’, or ‘location updating effect’, claims that we tend to forget items of recent significance immediately after crossing a boundary. Previous research suggests that such a forgetting effect occurs both at physical boundaries (e.g., moving from one room to another via a door) and metaphysical boundaries (e.g., imagining traversing a doorway, or even when moving from one desktop window to another on a computer). Here, we aimed to conceptually replicate this effect using virtual and physical environments. Methods Across four experiments, we measured participants’ hit and false alarm rates to memory probes for items recently encountered either in the same or previous room. Experiments 1 and 2 used highly immersive virtual reality without and with working memory load (Experiments 1 and 2, respectively). Experiment 3 used passive video watching and Experiment 4 used active real-life movement. Data analysis was conducted using frequentist as well as Bayesian inference statistics. Results Across this series of experiments, we observed no significant effect of doorways on forgetting. In Experiment 2, however, signal detection was impaired when participants responded to probes after moving through doorways, such that false alarm rates were increased for mismatched recognition probes. Thus, under working memory load, memory was more susceptible to interference after moving through doorways. Conclusions This study presents evidence that is inconsistent with the location updating effect as it has previously been reported. Our findings call into question the generalisability and robustness of this effect to slight paradigm alterations and, indeed, what factors contributed to the effect observed in previous studies.

Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine

Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates.