Joshua Ojo Nehinbe

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Complex and new cases of intrusions, new bugs, security issues and vulnerabilities are evolving everyday for a number of reasons. Consequently, researchers in the domains of Intrusion Detection Systems and Intrusion Prevention Systems constantly design new methods to lessen the aforementioned security issues. However, getting suitable datasets for(More)
Alerts swamping and intrusion redundancy are two critical problems of intrusion detection technology that often worsen the problems of classification, data reduction, false positives, intrusion correlation and reporting. Consequently, the validity and continuous usage of intrusion detectors are constantly threatened because the system administrators are(More)
False positives are critical problems of network intrusion detection systems that use pattern matching algorithm to detect network intrusions. The algorithm is unable to eliminate false packets with short lifespan. Secondly, the algorithm lacks the capability to manage the trade-offs between false and true positives. Consequently, system administrators are(More)
The scope of information security is becoming wider everyday due to new and emerging dimensions in the evolution of computer applications with the intention to lessen manual operations. These have necessitated the needs to manufacture portable and smart devices that can be interconnected to the Internet for home and industrial usages and for environmental(More)
Signature-based Intrusion Detection Systems have numerous redundant rules that do not match network attacks during intrusion detections. Instead, the toolkits have low efficacies in matching each packet with all the detection rules to avoid false positives. Unfortunately, there are no automatic functionalities to debug expert systems so that all noisy(More)
The concurrent reductions of true and false positives in Intrusion Detection Systems are exploitable avenues for attacks to succeed for a number of reasons. Firstly, intrusion detectors can concurrently generate numerous false positives with true positives. Secondly, intrusion aggregation models that are designed to reduce alerts workload reduce clusters of(More)
Network forensics are challenging because of numerous quantities of low level alerts that are generated by network intrusion detectors generate to achieve high detection rates. However, clustering analyses are insufficient to establish overall patterns, sequential dependencies and precise classifications of attacks embedded in of low level alerts. This is(More)