Machine learning algorithm for advanced persistent threat detection

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dc.contributor.authorOmollo, Vincent
dc.contributor.authorAbeka, Silvance
dc.date.accessioned2021-05-12T11:29:03Z
dc.date.available2021-05-12T11:29:03Z
dc.date.issued2019-08
dc.descriptionPaper presented at the 5th Strathmore International Mathematics Conference (SIMC 2019), 12 - 16 August 2019, Strathmore University, Nairobi, Kenyaen_US
dc.description.abstractNetworked computer syster:ns are increasingly being employed to run critical infrastructural activities by both private companies and governments. Advanced persistent threats have emerged as serious security threats to these networks due to their level of sophistication and multiple attack vectors. Conventional countermeasures against these network threats have been antivirus, antimalware, firewalls, intrusion detection systems, intrusion prevention systems and sandboxing. However, these techniques are ineffective against advanced persistent threats since the attackers employ a number of evasion techniques such as code obfuscation and encryption. In addition, these technologies are rarely monitored or updated, hence lulling end-user enterprises into a false sense of security. The signature based scanning utilized in some of these technologies is unable of detecting new and sophisticated malware. Sandboxes on their part, a number of malware deploy sandbox detection techniques that help them detect when they are being analyzed and evade the sandbox by hiding their malicious behavior. Due to these shortfalls, researchers have proposed machine learning, deep neural networks, and data mining using misuse detection and anomaly detection as possible threat detection strategies. Unfortunately, machine learning and deep neural networks are susceptible to evasion attacks using adversarial examples that involve small changes to the input data that cause misclassification at test time. Misuse detection is unable to discover attacks whose instances have not yet been observed while anomaly detection can generate false positives due to previously unseen and yet legitimate system behaviors being recognized as anomalies, and hence flagged as potential intrusions. The aim of this paper will be therefore to implement an enhanced algorithm for intrusion detection using machine learning to curb the rising number of advanced persistent threats.en_US
dc.description.sponsorshipKisii University, Kenya. Jaramogi Oginga Odinga University of Science and Technology, Kenya.en_US
dc.identifier.urihttp://hdl.handle.net/11071/11861
dc.language.isoen_USen_US
dc.publisherStrathmore Universityen_US
dc.subjectIntrusion detectionen_US
dc.subjectMachine learningen_US
dc.subjectMalwareen_US
dc.titleMachine learning algorithm for advanced persistent threat detectionen_US
dc.typeArticleen_US
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