A survey of intrusion detection techniques in Cloud

Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks

Automation of Information Security Risk Assessment

An information security audit method (ISA) for a distributed computer network (DCN) of an informatization object (OBI) has been developed. Proposed method is based on the ISA procedures automation by using Bayesian networks (BN) and artificial neural networks (ANN) to assess the risks. It was shown that such a combination of BN and ANN makes it possible to quickly determine the actual risks for OBI information security (IS). At the same time, data from sensors of various hardware and software information security means (ISM) in the OBI DCS segments are used as the initial information. It was shown that the automation of ISA procedures based on the use of BN and ANN allows the DCN IS administrator to respond dynamically to threats in a real time manner, to promptly select effective countermeasures to protect the DC

Automation of Information Security Risk Assessment

An information security audit method (ISA) for a distributed computer network (DCN) of an informatization object (OBI) has been developed. Proposed method is based on the ISA procedures automation by using Bayesian networks (BN) and artificial neural networks (ANN) to assess the risks. It was shown that such a combination of BN and ANN makes it possible to quickly determine the actual risks for OBI information security (IS). At the same time, data from sensors of various hardware and software information security means (ISM) in the OBI DCS segments are used as the initial information. It was shown that the automation of ISA procedures based on the use of BN and ANN allows the DCN IS administrator to respond dynamically to threats in a real time manner, to promptly select effective countermeasures to protect the DC

ANOMALY NETWORK INTRUSION DETECTION SYSTEM BASED ON DISTRIBUTED TIME-DELAY NEURAL NETWORK (DTDNN)

In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type of attack (DoS, U2R, R2L and Probe attack) detected by dynamic neural network. The results indicate that dynamic neural nets (Distributed Time-Delay Artificial Neural Network) can achieve a high detection rate, where the overall accuracy classification rate average is equal to 97.24%

INFOSEC & COMPTECH

Збірник містить тези доповідей за матеріалами ІІ Міжнародної науково-практичної конференції “Інформаційна безпека та комп'ютерні технології”, що відбулась 20-22 квітня 2017 року на базі кафедри програмування та захисту інформації Центральноукраїнського національного технічного університету