A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

ANOMALY DETECTION PADA INTRUSION DETECTION SYSTEM (IDS) MENGGUNAKAN METODE CLUSTERING ANOMALY DETECTION ON INTRUSION DETECTION SYSTEM (IDS) BY CLUSTERING METHOD

ABSTRAKSI: Intrusion Detection System (IDS) adalah sekumpulan teknik dan metode untuk mendeteksi aktivitas-aktivitas yang terjadi pada level network dan host. Pada sistem ini terdapat dua pendekatan yang dilakukan : signature-based intrusion detection systems dan anomaly detection system. Pendekatan yang pertama memiliki kelemahan yang cukup rentan, yaitu pendeteksian hanya akan dilakukan terhadap data yang sudah didefinisikan. Sementara untuk anomaly detection, selain menggunakan data yang sudah didefinisikan, dapat pula dilakukan dengan menganalisis pola-pola anomali dari paket network yang datang, namun jika salah mengambil parameter maka metode ini justru akan sering mengakibatkan false alarm.Untuk menganalisis anomaly detection pada paket yang datang dapat dilakukan dengan menggunakan outlier detection scheme. Dengan metode ini, paket-paket yang datang akan dianalisis dengan menggunakan beberapa algoritma, diantaranya adalah clustering. Algoritma clustering pada metode outlier detection scheme melakukan analisis dengan cara meng-cluster-kan data dan menandai cluster terkecil, kemudian cluster terkecil tersebut akan dianggap sebagai anomali.Dalam Tugas Akhir ini dibangun suatu implementasi pendeteksian intrusion (serangan) terhadap sistem atau jaringan komputer menggunakan metode anomaly detection dengan algoritma cluster-based outlier detection. Proses clustering itu sendiri dilakukan terhadap data koneksi jaringan. Adapun implementasi dilakukan dengan menggunakan bahasa pemrograman HTML, script PHP dan DBMS MySQL.Pengujian terhadap sistem anomaly detection ini menunjukkan hasil akhir bahwa hasil pendeteksian anomali sangat bergantung pada tiga hal hal, yaitu tergantung pada pemilihan data yang digunakan untuk dianalisis (dataset), jarak maksimal yang diijinkan dari titik pusat cluster atau center ke setiap data yang menjadi anggota dari cluster tersebut atau biasa disebut jari jari cluster, dan perbandingan jumlah data instrusion dengan data normal pada dataset.Kata Kunci : Intrusion Detection System(IDS), clustering, anomaly detection, outlier detection scheme.ABSTRACT: Intrusion Detection System (IDS) is a group of techniques and methods for detecting activities that hapenned in network and host level. IDS has two approaches : signature-based intrusion detection system and anomaly detection system. First approach has any weakness, the detection can only done if the intrusion had been definited. Therefore except using the data which had been definited, we can also analyze anomaly patterns from the packets , but if we take the wrong parameter this method could eventually be a false alarm.Analyze anomaly detection in network data packets can be handled by outlier detection scheme method. With this method we can build the analysis with some algorithms, one of the algorithms is clustering. Clustering algorithm clustered the data and mark the smallest cluster with assumption that smallest cluster as an anomaly.This final Project will build an implementation of intrusion detection system in computer or network system using anomaly detection method with cluster-based outlier detection algorithm. The process is to clustering data connection record. Implementation use HTML programming language, PHP script, and MySQL DBMS.Anomaly detection system evaluation shows that the results are depend on three things, data which have been analyzed or data set given and the maximum distance betwen center to each data point that included in that cluster, or cluster radius values and ratio between normal data and instrusion data.Keyword: Intrusion Detection System(IDS), clustering, anomaly detection, outlier detection scheme

A Trusted Environment for MPI Programs

Several algorithms have been proposed to implement intrusion detection systems (IDS) based on the idea that anomalies in the behavior of a system might be produced by a set of actions of an intruder or by a system fault. Almost no previous research has been conducted in the area of anomaly detection for high performance clusters. The research reported in this thesis demonstrates that the analysis of sequences of function calls issued by one or more processes can be used to verify the correct execution of parallel programs written in C/C++ with the Message Passing Interface (MPI) in a cluster of Linux workstations. The functions calls were collected via library interposition. Two anomaly detection algorithms previously reported to be effective methods for anomaly detection in sequences of system calls, Hidden Markov Model and sequence matching, were implemented and tested. In general, the simpler sequence matching algorithm out-performed the Hidden Markov Model

Anomaly Detection In Blockchain

Anomaly detection has been a well-studied area for a long time. Its applications in the financial sector have aided in identifying suspicious activities of hackers. However, with the advancements in the financial domain such as blockchain and artificial intelligence, it is more challenging to deceive financial systems. Despite these technological advancements many fraudulent cases have still emerged. Many artificial intelligence techniques have been proposed to deal with the anomaly detection problem; some results appear to be considerably assuring, but there is no explicit superior solution. This thesis leaps to bridge the gap between artificial intelligence and blockchain by pursuing various anomaly detection techniques on transactional network data of a public financial blockchain named 'Bitcoin'. This thesis also presents an overview of the blockchain technology and its application in the financial sector in light of anomaly detection. Furthermore, it extracts the transactional data of bitcoin blockchain and analyses for malicious transactions using unsupervised machine learning techniques. A range of algorithms such as isolation forest, histogram based outlier detection (HBOS), cluster based local outlier factor (CBLOF), principal component analysis (PCA), K-means, deep autoencoder networks and ensemble method are evaluated and compared

ANOMALY BASED DETECTION AND PREVENTION TO PROVIDE SECURE MANET USING DUAL HEAD CLUSTER IN HIERARCHICAL COOPERATIVE IDS

ABSTRACT A purely wireless network wherein each device itself acts as a node and also performs the task of router is called as Mobile Ad-hoc network. A MANET has become a need of today's fastest developing era. A measure issue in MANET is security as it is an autonomous system of nodes which has no fixed infrastructure and also, due to continuous movement of mobile nodes it has dynamic topology so it is difficult to maintain security. In our proposed system a cluster with dual head will be used in cooperative IDS for anomaly detection system .Two head nodes will be protecting each other from intrusion along with detecting intrusion for cluster member. This intrusion can be detected by signature analysis or anomaly based detection. Anomaly based detection will detect intrusion by monitoring the whole system activities. Our proposed system will also find attacks which are new and which were not possible to detect by using signature analysis. Proposed system will be able to detect the anomaly behaviour of the attacks like black hole, Dos and flood anomaly. As a result of our research work a stable, secure network will get formed