Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, increase a huge amount of resource usage and time consumption for data analytics. The objective of this study is to analyze relevant and significant features of huge network traffic to be used to improve the accuracy of traffic anomaly detection and to decrease its execution time. Information Gain is the most feature selection technique used in Intrusion Detection System (IDS) research. This study uses Information Gain, ranking and grouping the features according to the minimum weight values to select relevant and significant features, and then implements Random Forest (RF), Bayes Net (BN), Random Tree (RT), Naive Bayes (NB) and J48 classifier algorithms in experiments on CICIDS-2017 dataset. The experiment results show that the number of relevant and significant features yielded by Information Gain affects significantly the improvement of detection accuracy and execution time. Specifically, the Random Forest algorithm has the highest accuracy of 99.86% using the relevant selected features of 22, whereas the J48 classifier algorithm provides an accuracy of 99.87% using 52 relevant selected features with longer execution time.

Internet of Things (IoT) devices may transfer data to the gateway/application server through File Transfer Protocol (FTP) transaction. Unfortunately, in terms of security, the FTP server at a gateway or data sink very often is improperly set up. At the same time, password matching/theft holding is among the popular attacks as the intruders attack the IoT network. Thus, this paper attempts to provide an insight of this type of attack with the main aim of coming up with attack patterns that may help the IoT system administrator to analyze any similar attacks. This paper investigates brute force attack (BFA) on the FTP server of the IoT network by using a time-sensitive statistical relationship approach and visualizing the attack patterns that identify its configurations. The investigation focuses on attacks launched from the internal network, due to the assumption that the IoT network has already installed a firewall. An insider/internal attack launched from an internal network endangers more the entire IoT security system. The experiments use the IoT network testbed that mimic the internal attack scenario with three major goals: (i) to provide a topological description on how an insider attack occurs; (ii) to achieve attack pattern extraction from raw sniffed data; and (iii) to establish attack pattern identification as a parameter to visualize real-time attacks. Experimental results validate the investigation.

Abstract-Hacking attempts or cyber-attacks to information systems have recently evolved to be sophisticated and deadly, resulting in such incidents as leakage of personal information and system destruction. While various security solutions to cope with these risks are being developed and deployed, it is still necessary to systematically consider the methods to enhance the existing security system and build more effective defense systems. Under this circumstance, it is necessary to identify the latest types of attacks attempted to the primary security system. This paper analyzes cyber attack techniques as well as the anatomy of penetration test in order to assist security officers to perform appropriate self security assesment on their network systems.

Agile software development is successful due to self-organizing teams, adaptive planning, a cooperative environment with respect to communication with clients and team members, small development cycles, continuous design improvements, continuous delivery and feedback of clients. Cloud computing helps to reduce cost, enables scalability and enhances communication through its services. A generic framework with the conjunction of Agile Development and Cloud Computing (ADCC) proposed in an earlier study is evaluated in this study. The Malaysia Research and education network (MyRen) cloud is utilized to implement the framework. A case study is conducted to evaluate the framework. Before conducting the case study, the participants are educated on the ADCC framework. The results of the case study show that the performance of agile methods is improved with the usage of the ADCC framework. The improvement is measured in terms of local and distributed agile development environments. INDEX TERMS Agile development, case study, cloud-based agile tools, cloud computing.

<p class="0abstract">Focus of this research is TCP FIN flood attack pattern recognition in Internet of Things (IoT) network using rule based signature analysis method. Dataset is taken based on three scenario normal, attack and normal-attack. The process of identification and recognition of TCP FIN flood attack pattern is done based on observation and analysis of packet attribute from raw data (pcap) using a feature extraction and feature selection method. Further testing was conducted using snort as an IDS. The results of the confusion matrix detection rate evaluation against the snort as IDS show the average percentage of the precision level.</p>