Reduction of False Alarm Rates in Peripheral Detection Systems.

1980 ◽  
Author(s):  
Roy H. Hines
Keyword(s):  
False Alarm ◽  
Detection Systems ◽  
False Alarm Rates

scholarly journals False alarm rates of liquid explosives detection systems

2017 ◽  
Vol 10 (3-4) ◽  
pp. 145-169
Author(s):  
Elitsa Dzhongova ◽  
David Anderson ◽  
Jaap de Ruiter ◽  
Velibor Novakovic ◽  
Miguel Ruiz Oses
Keyword(s):  
False Alarm ◽  
Explosives Detection ◽  
Detection Systems ◽  
Liquid Explosives ◽  
False Alarm Rates

scholarly journals µChemLab: twenty years of developing CBRNE detection systems with low false alarm rates

2019 ◽  
Author(s):  
Joshua Whiting ◽  
Edward B. Myers ◽  
Ronald P. Manginell ◽  
Matthew W. Moorman ◽  
Kent Pfeifer ◽  
...  
Keyword(s):  
False Alarm ◽  
Detection Systems ◽  
False Alarm Rates

scholarly journals Machine Learning Approach to Combat False Alarms in Wireless Intrusion Detection System

2018 ◽  
Vol 11 (3) ◽  
pp. 67 ◽  
Author(s):  
D. Sudaroli Vijayakumar ◽  
S. Ganapathy
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Intrusion Detection Systems ◽  
False Alarms ◽  
Detection Systems ◽  
High Detection ◽  
False Alarm Rates

Wireless Networks facilitate the ease of communication for sharing the crucial information. Recently, most of the small and large-scale companies, educational institutions, government organizations, medical sectors, military and banking sectors are using the wireless networks. Security threats, a common term found both in wired as well as in wireless networks. However, it holds lot of importance in wireless networks because of its susceptible nature to threats. Security concerns in WLAN are studied and many organizations concluded that Wireless Intrusion Detection Systems (WIDS) is an essential element in network security infrastructure to monitor wireless activity for signs of attacks. However, it is an indisputable fact that the art of detecting attacks remains in its infancy. WIDS generally collect the activities within the protected network and analyze them to detect intrusions and generates an intrusion alarm. Irrespective of the different types of Intrusion Detection Systems, the major problems arising with WIDS is its inability to handle large volumes of alarms and more prone to false alarm attacks. Reducing the false alarms can improve the overall efficiency of the WIDS. Many techniques have been proposed in the literature to reduce the false alarm rates. However, most of the existing techniques are failed to provide desirable result and the high complexity to achieve high detection rate with less false alarm rates. This is the right time to propose a new technique for providing high detection accuracy with less false alarm rate. This paper made an extensive survey about the role of machine learning techniques to reduce the false alarm rate in WLAN IEEE 802.11. This survey proved that the substantial improvement has been achieved by reducing false alarm rate through machine learning algorithms. In addition to that, advancements specific to machine learning approaches is studied meticulously and a filtration technique is proposed.

scholarly journals Doorways do not always cause forgetting: a multimodal investigation

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jessica McFadyen ◽  
Christopher Nolan ◽  
Ellen Pinocy ◽  
David Buteri ◽  
Oliver Baumann
Keyword(s):  
Working Memory ◽  
False Alarm ◽  
Memory Load ◽  
Real Life ◽  
Working Memory Load ◽  
Movement Data ◽  
Physical Environments ◽  
Series Of Experiments ◽  
False Alarm Rates ◽  
Location Updating

Abstract 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.

scholarly journals Security of Things Intrusion Detection System for Smart Healthcare

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1375
Author(s):  
Celestine Iwendi ◽  
Joseph Henry Anajemba ◽  
Cresantus Biamba ◽  
Desire Ngabo
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Web Security ◽  
Intrusion Detection Systems ◽  
High Detection Rate ◽  
Detection Systems ◽  
Smart Healthcare

Web security plays a very crucial role in the Security of Things (SoT) paradigm for smart healthcare and will continue to be impactful in medical infrastructures in the near future. This paper addressed a key component of security-intrusion detection systems due to the number of web security attacks, which have increased dramatically in recent years in healthcare, as well as the privacy issues. Various intrusion-detection systems have been proposed in different works to detect cyber threats in smart healthcare and to identify network-based attacks and privacy violations. This study was carried out as a result of the limitations of the intrusion detection systems in responding to attacks and challenges and in implementing privacy control and attacks in the smart healthcare industry. The research proposed a machine learning support system that combined a Random Forest (RF) and a genetic algorithm: a feature optimization method that built new intrusion detection systems with a high detection rate and a more accurate false alarm rate. To optimize the functionality of our approach, a weighted genetic algorithm and RF were combined to generate the best subset of functionality that achieved a high detection rate and a low false alarm rate. This study used the NSL-KDD dataset to simultaneously classify RF, Naive Bayes (NB) and logistic regression classifiers for machine learning. The results confirmed the importance of optimizing functionality, which gave better results in terms of the false alarm rate, precision, detection rate, recall and F1 metrics. The combination of our genetic algorithm and RF models achieved a detection rate of 98.81% and a false alarm rate of 0.8%. This research raised awareness of privacy and authentication in the smart healthcare domain, wireless communications and privacy control and developed the necessary intelligent and efficient web system. Furthermore, the proposed algorithm was applied to examine the F1-score and precisionperformance as compared to the NSL-KDD and CSE-CIC-IDS2018 datasets using different scaling factors. The results showed that the proposed GA was greatly optimized, for which the average precision was optimized by 5.65% and the average F1-score by 8.2%.

A comparison of data obtained with different false-alarm rates.

1964 ◽  
Vol 71 (3) ◽  
pp. 243-246 ◽  
Author(s):  
Donald A. Norman
Keyword(s):  
False Alarm ◽  
False Alarm Rates

scholarly journals The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2559 ◽  
Author(s):  
Celestine Iwendi ◽  
Suleman Khan ◽  
Joseph Henry Anajemba ◽  
Mohit Mittal ◽  
Mamdouh Alenezi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Detection System ◽  
Denial Of Service ◽  
Intrusion Detection Systems ◽  
Ensemble Models ◽  
Detection Systems

The pursuit to spot abnormal behaviors in and out of a network system is what led to a system known as intrusion detection systems for soft computing besides many researchers have applied machine learning around this area. Obviously, a single classifier alone in the classifications seems impossible to control network intruders. This limitation is what led us to perform dimensionality reduction by means of correlation-based feature selection approach (CFS approach) in addition to a refined ensemble model. The paper aims to improve the Intrusion Detection System (IDS) by proposing a CFS + Ensemble Classifiers (Bagging and Adaboost) which has high accuracy, high packet detection rate, and low false alarm rate. Machine Learning Ensemble Models with base classifiers (J48, Random Forest, and Reptree) were built. Binary classification, as well as Multiclass classification for KDD99 and NSLKDD datasets, was done while all the attacks were named as an anomaly and normal traffic. Class labels consisted of five major attacks, namely Denial of Service (DoS), Probe, User-to-Root (U2R), Root to Local attacks (R2L), and Normal class attacks. Results from the experiment showed that our proposed model produces 0 false alarm rate (FAR) and 99.90% detection rate (DR) for the KDD99 dataset, and 0.5% FAR and 98.60% DR for NSLKDD dataset when working with 6 and 13 selected features.

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