Sistem Pendeteksian Penyusupan Jaringan Komputer dengan Active Response Menggunakan Metode Hybrid Intrusion Detection, Signatures dan Anomaly Detection

The progress of internet technology increase the need of security data. The progress of tools which have intrusion ability, also influence these needed. The methods of Intrusion Detection System (IDS) implementation and methods of analyze intrusion have excess and lack, which mutually completes. There are a lot of IDS now, but just an IDS open source based is snort. Method of snort implementation is network based restricted. This Final Task\u27s system used Hybrid Intrusion Detection System, Signatures and Anomaly Detection Methods. The indicator which used to detect intrusion are IP Address and Port Number. This system use TCP, UDP and ICMP protocols. This system also, is completed by active response, like blocking access for intruder. This System Implementation with Java Programming Language for engine perform and Java Server Pages (JSP) to develop user interface, The database which used is MYSQL. There are two of development test; Link system test and intrusion test. The link system test show the connect each interface. Intrusion is executed by host detection which used DoS HTTP tools and network detection which used Ping of Death\u27s scripts. The intrusion testing conclusions are; can be detected, analyze and active response for intrusion

Model comparison using Akaike Information Criterion (AIC).

<p>Models compared are Poisson, Negative Binomial (NB), Zero-Inflated Poisson (ZIP), Zero-Inflated Negative Binomial (ZINB), Poisson Logit Hurdle (PLH) and Negative Binomial Logit Hurdle (NBLH).</p

Zero count capturing.

<p>Models compared are Poisson, Negative Binomial (NB), Zero-Inflated Poisson (ZIP), Zero-Inflated Negative Binomial (ZINB), Poisson Logit Hurdle (PLH) and Negative Binomial Logit Hurdle (NBLH).</p

Spherical variogram plot based on the deviance residuals.

<p> <b>The sill is at 0.3 at range 0f 10 kms.</b></p

The distribution of parasite counts of <i>S. haematobium</i>, measured as eggs/10 ml, in individual participants.

<p>The distribution of parasite counts of <i>S. haematobium</i>, measured as eggs/10 ml, in individual participants.</p

Fixed effects estimates for negative binomial logit hurdle model for <i>S. haematobium</i>.

<p>Fixed effects estimates for negative binomial logit hurdle model for <i>S. haematobium</i>.</p

Characteristics for individuals who had <i>S. haematobium</i>.

<p>Characteristics for individuals who had <i>S. haematobium</i>.</p

Flow diagram.

<p>Flow diagram.</p

Magnitude of selected NCDs and their risks factors in Malawi: July to September 2009.

<p>BP =  Blood Pressure, CI =  Confidence interval, FBS =  Fasting blood glucose, n =  number of participants in the group, *statistically significant, p<0.05; male vs female, urban vs rural.</p

Characteristics of participants enrolled in Malawi NCD STEPS survey compared to National Statistics Office (NSO) 2008 population figures.

<p>§Marital status NSO data includes those aged ≥65, *statistically significant, p<0.05, survey male participants vs NSO male population figures, #No comparable NSO data on education, n =  number in the group, CI =  confidence interval.</p