PERANCANGAN DAN PEMBUATAN PROGRAM DETEKSI INTRUSI PADA JARINGAN KOMPUTER BERDASAR PACKET HEADER DENGAN ANALISIS OUTLIER

ABSTRACT Security is main priority in a network so need some tool software or hardware can recognize an attack in a network. In software scope today many of IDS (intrusion detection system), but majority is developed with signature method or use rule and some minority develop with anomaly. Anomaly is a method to find deviation in normal data. This final project purpose is make an IDS application based on anomaly, where is an analysis focused on IP packet header. Analysis method is use average and standard deviation from data passing through, this method have some benefit than clustering method, there is more speed in calculation. Aiming of this project is make an application that can detect old and new attack type and have wide range of recognition of intrusion data without update a new information. Keyword : Intrusion detection system , anomaly IDS , c++ anomaly,ID

Botnet Detection using Social Graph Analysis

Signature-based botnet detection methods identify botnets by recognizing Command and Control (C\&C) traffic and can be ineffective for botnets that use new and sophisticate mechanisms for such communications. To address these limitations, we propose a novel botnet detection method that analyzes the social relationships among nodes. The method consists of two stages: (i) anomaly detection in an "interaction" graph among nodes using large deviations results on the degree distribution, and (ii) community detection in a social "correlation" graph whose edges connect nodes with highly correlated communications. The latter stage uses a refined modularity measure and formulates the problem as a non-convex optimization problem for which appropriate relaxation strategies are developed. We apply our method to real-world botnet traffic and compare its performance with other community detection methods. The results show that our approach works effectively and the refined modularity measure improves the detection accuracy.Comment: 7 pages. Allerton Conferenc

Impact of IT Monoculture on Behavioral End Host Intrusion Detection

International audienceIn this paper, we study the impact of today's IT policies, defined based upon a monoculture approach, on the performance of endhost anomaly detectors. This approach leads to the uniform configuration of Host intrusion detection systems (HIDS) across all hosts in an enterprise networks. We assess the performance impact this policy has from the individual's point of view by analyzing network traces collected from 350 enterprise users. We uncover a great deal of diversity in the user population in terms of the “tail†behavior, i.e., the component which matters for anomaly detection systems. We demonstrate that the monoculture approach to HIDS configuration results in users that experience wildly different false positive and false negatives rates. We then introduce new policies, based upon leveraging this diversity and show that not only do they dramatically improve performance for the vast majority of users, but they also reduce the number of false positives arriving in centralized IT operation centers, and can reduce attack strength

Machine learning based botnet identification traffic

The continued growth of the Internet has resulted in the increasing sophistication of toolkit and methods to conduct computer attacks and intrusions that are easy to use and publicly available to download, such as Zeus botnet toolkit. Botnets are responsible for many cyber-attacks, such as spam, distributed denial-of-service (DDoS), identity theft, and phishing. Most of existence botnet toolkits release updates for new features, development and support. This presents challenges in the detection and prevention of bots. Current botnet detection approaches mostly ineffective as botnets change their Command and Control (C&C) server structures, centralized (e.g., IRC, HTTP), distributed (e.g., P2P), and encryption deterrent. In this paper, based on real world data sets we present our preliminary research on predicting the new bots before they launch their attack. We propose a rich set of features of network traffic using Classification of Network Information Flow Analysis (CONIFA) framework to capture regularities in C&C communication channels and malicious traffic. We present a case study of applying the approach to a popular botnet toolkit, Zeus. The experimental evaluation suggest that it is possible to detect effectively botnets during the botnet C&C communication generated from new updated Zeus botnet toolkit by building the classifier using machine learning from an earlier version and before they launch their attacks using traffic behaviors. Also, show that there is similarity in C&C structures various Botnet toolkit versions and that the network characteristics of botnet C&C traffic is different from legitimate network traffic. Such methods could reduce many different resources needed to identify C&C communication channels and malicious traffic

Machine learning based botnet identification traffic

The continued growth of the Internet has resulted in the increasing sophistication of toolkit and methods to conduct computer attacks and intrusions that are easy to use and publicly available to download, such as Zeus botnet toolkit. Botnets are responsible for many cyber-attacks, such as spam, distributed denial-of-service (DDoS), identity theft, and phishing. Most of existence botnet toolkits release updates for new features, development and support. This presents challenges in the detection and prevention of bots. Current botnet detection approaches mostly ineffective as botnets change their Command and Control (C&C) server structures, centralized (e.g., IRC, HTTP), distributed (e.g., P2P), and encryption deterrent. In this paper, based on real world data sets we present our preliminary research on predicting the new bots before they launch their attack. We propose a rich set of features of network traffic using Classification of Network Information Flow Analysis (CONIFA) framework to capture regularities in C&C communication channels and malicious traffic. We present a case study of applying the approach to a popular botnet toolkit, Zeus. The experimental evaluation suggest that it is possible to detect effectively botnets during the botnet C&C communication generated from new updated Zeus botnet toolkit by building the classifier using machine learning from an earlier version and before they launch their attacks using traffic behaviors. Also, show that there is similarity in C&C structures various Botnet toolkit versions and that the network characteristics of botnet C&C traffic is different from legitimate network traffic. Such methods could reduce many different resources needed to identify C&C communication channels and malicious traffic