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Abstract-Internet network attacks are complicated and worth studying. The attacks include Denial of Service (DoS). DoS attacks that exploit vulnerabilities found in operating systems, network services and applications. Indicators of DoS attacks, is when legitimate users cannot access the system. This paper proposes a framework for Internet based forensic logs that aims to assist in the investigation process to reveal DoS attacks. The framework in this study consists of several steps, among others : logging into the text file and database as well as identifying an attack based on the packet header length. After the identification process, logs are grouped using k-means clustering algorithm into three levels of attack (dangerous, rather dangerous and not dangerous) based on port numbers and tcpflags of the package. Based on the test results the proposed framework can be grouped into three level attacks and found the attacker with a success rate of 89,02%, so, it can be concluded that the proposed framework can meet the goals set in this research.

an electronic nose (e-nose) based on a gas sensor array equipped with a stable temperature controller has been successfully developed by applying proportional-integral-derivative (PID) controller. This study was motivated due to the dependence of sensor response on the temperature of a sample. Here, the temperature influences the volatile organic compounds (VOC) of the sample. The performance of the e-nose was then evaluated to classify the quality levels of black tea (Q1, Q2, and Q3). The black tea samples were purchased from Tambi Tea Industry in Central Java, Indonesia. Here, the quality is based on the information from the factory. For each quality level, the measurement was repeated 31 times. Therefore, the total of measurements is 93, and only six sensors, i.e. MQ-7, TGS 813, TGS 2602, TGS 826, TGS 2620 and TGS 825 have the high influence on the patterns differentiation. It is found that the e-nose is not able to distinguish the three quality levels of black tea correctly when applying without heating treatment. In this condition, only 88.9% of cross-validated grouped are correctly classified. On the other hand, for the black tea sample with unstable heating, the performance of the e-nose has been improved. In this case, 92.5% of crossvalidated grouped are correctly classified. Moreover, for the black tea sample with stable heating treatment, all samples are separated where 97.8% of cross-validated grouped are correctly classified. The results indicated that the e-nose with the highly stable heater was capable of detecting the quality of black tea.

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