Biblio

Modern Industrial Automation & Control Systems (IACS) are essential part of the critical infrastructures and services. They are used in health, power, water, and transportation systems, and the impact of cyberattacks on IACS could be severe, resulting, for example, in damage to the environment, public or employee safety or health. Thus, building IACS safe and secure against cyberattacks is extremely important. The attacker model is one of the key elements in risk assessment and other security related information system management tasks. The aim of the study is to specify the attacker's profile based on the analysis of network and system events. The paper presents an approach to the selection of attacker's profile attributes from raw network and system events of the Linux OS. To evaluate the approach the experiments were performed on data collected within the Global CPTC 2019 competition.

In software quality estimation research, software defect prediction is a key topic. A defect prediction model is generally constructed using a variety of software attributes and each attribute may have positive, negative or neutral effect on a specific model. Selection of an optimal set of attributes for model development remains a vital yet unexplored issue. In this paper, we have introduced a new feature space transformation process with a normalization technique to improve the defect prediction accuracy. We proposed a feature space transformation technique and classify the instances using Support Vector Machine (SVM) with its histogram intersection kernel. The proposed method is evaluated using the data sets from NASA metric data repository and its application demonstrates acceptable accuracy.

The Domain Name System (DNS) is a critically fundamental element in the internet technology as it translates domain names into corresponding IP addresses. The DNS queries and responses are UDP (User Datagram Protocol) based. DNS name servers are constantly facing threats of DNS amplification attacks. DNS amplification attack is one of the major Distributed Denial of Service (DDoS) attacks, in DNS. The DNS amplification attack victimized huge business and financial companies and organizations by giving disturbance to the customers. In this paper, a mechanism is proposed to detect such attacks coming from the compromised machines. We analysed DNS traffic packet comparatively based on the Machine Learning Classification algorithms such as Decision Tree (TREE), Multi Layer Perceptron (MLP), Naïve Bayes (NB) and Support Vector Machine (SVM) to classify the DNS traffics into normal and abnormal. In this approach attribute selection algorithms such as Information Gain, Gain Ratio and Chi Square are used to achieve optimal feature subset. In the experimental result it shows that the Decision Tree achieved 99.3% accuracy. This model gives highest accuracy and performance as compared to other Machine Learning algorithms.