Anomalous event detection using a semi-two dimensional hidden Markov model

The rapid increase in the deployment of CCTV systems has led to a greater demand for algorithms that are able to process incoming video feeds. These algorithms are designed to extract information of interest for human operators. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned `normal' model. Many researchers have tried various sets of features to train different learning models to detect abnormal behaviour in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM) to model the normal activities of people. The outliers of the model with insufficient likelihood are identified as abnormal activities. Our Semi-2D HMM is designed to model both the temporal and spatial causalities of the crowd behaviour by assuming the current state of the Hidden Markov Model depends not only on the previous state in the temporal direction, but also on the previous states of the adjacent spatial locations. Two different HMMs are trained to model both the vertical and horizontal spatial causal information. Location features, flow features and optical flow textures are used as the features for the model. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Comparison of antigen detection and quantitative PCR in the detection of chlamydial infection in koalas (Phascolarctos cinereus)

The gold standard method for detecting chlamydial infection in domestic and wild animals is PCR, but the technique is not suited to testing animals in the field when a rapid diagnosis is frequently required. The objective of this study was to compare the results of a commercially available enzyme immunoassay test for Chlamydia against a quantitative Chlamydia pecorum-specific PCR performed on swabs collected from the conjunctival sac, nasal cavity and urogenital sinuses of naturally infected koalas (Phascolarctos cinereus). The level of agreement for positive results between the two assays was low (43.2%). The immunoassay detection cut-off was determined as approximately 400 C. pecorum copies, indicating that the test was sufficiently sensitive to be used for the rapid diagnosis of active chlamydial infections.

Efficient real-time face detection for high resolution surveillance applications

This paper presents an efficient face detection method suitable for real-time surveillance applications. Improved efficiency is achieved by constraining the search window of an AdaBoost face detector to pre-selected regions. Firstly, the proposed method takes a sparse grid of sample pixels from the image to reduce whole image scan time. A fusion of foreground segmentation and skin colour segmentation is then used to select candidate face regions. Finally, a classifier-based face detector is applied only to selected regions to verify the presence of a face (the Viola-Jones detector is used in this paper). The proposed system is evaluated using 640 x 480 pixels test images and compared with other relevant methods. Experimental results show that the proposed method reduces the detection time to 42 ms, where the Viola-Jones detector alone requires 565 ms (on a desktop processor). This improvement makes the face detector suitable for real-time applications. Furthermore, the proposed method requires 50% of the computation time of the best competing method, while reducing the false positive rate by 3.2% and maintaining the same hit rate.

Monitoring smartphones for anomaly detection

In this paper we demonstrate how to monitor a smartphone running Symbian operating system and Windows Mobile in order to extract features for anomaly detection. These features are sent to a remote server because running a complex intrusion detection system on this kind of mobile device still is not feasible due to capability and hardware limitations. We give examples on how to compute relevant features and introduce the top ten applications used by mobile phone users based on a study in 2005. The usage of these applications is recorded by a monitoring client and visualized. Additionally, monitoring results of public and self-written malwares are shown. For improving monitoring client performance, Principal Component Analysis was applied which lead to a decrease of about 80 of the amount of monitored features.

Improved foreground detection via block-based classifier cascade with probabilistic decision integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.

Modeling and detection of complex attacks

A complex attack is a sequence of temporally and spatially separated legal and illegal actions each of which can be detected by various IDS but as a whole they constitute a powerful attack. IDS fall short of detecting and modeling complex attacks therefore new methods are required. This paper presents a formal methodology for modeling and detection of complex attacks in three phases: (1) we extend basic attack tree (AT) approach to capture temporal dependencies between components and expiration of an attack, (2) using enhanced AT we build a tree automaton which accepts a sequence of actions from input message streams from various sources if there is a traversal of an AT from leaves to root, and (3) we show how to construct an enhanced parallel automaton that has each tree automaton as a subroutine. We use simulation to test our methods, and provide a case study of representing attacks in WLANs.

SERS substrate for detection of explosives

A novel gold coated femtosecond laser nanostructured sapphire surface – an “optical nose” - based on surface-enhanced Raman spectroscopy (SERS) for detecting vapours of explosive substances was investigated. Four different nitroaromatic vapours at room temperature were tested. Sensor responses were unambiguous and showed response in the range of 0.05 – 15 uM at 25 °C. The laser fabricated substrate nanostructures produced up to an eight-fold increase in Raman signal over that observed on the unstructured portions of the substrate. This work demonstrates a simple sensing system that is compatible with commercial manufacturing practices to detect taggants in explosives which can undertake as part of an integrated security or investigative mission.

SAIVT-QUT@TRECVid 2012 : interactive surveillance event detection

In this paper, we propose an approach which attempts to solve the problem of surveillance event detection, assuming that we know the definition of the events. To facilitate the discussion, we first define two concepts. The event of interest refers to the event that the user requests the system to detect; and the background activities are any other events in the video corpus. This is an unsolved problem due to many factors as listed below: 1) Occlusions and clustering: The surveillance scenes which are of significant interest at locations such as airports, railway stations, shopping centers are often crowded, where occlusions and clustering of people are frequently encountered. This significantly affects the feature extraction step, and for instance, trajectories generated by object tracking algorithms are usually not robust under such a situation. 2) The requirement for real time detection: The system should process the video fast enough in both of the feature extraction and the detection step to facilitate real time operation. 3) Massive size of the training data set: Suppose there is an event that lasts for 1 minute in a video with a frame rate of 25fps, the number of frames for this events is 60X25 = 1500. If we want to have a training data set with many positive instances of the event, the video is likely to be very large in size (i.e. hundreds of thousands of frames or more). How to handle such a large data set is a problem frequently encountered in this application. 4) Difficulty in separating the event of interest from background activities: The events of interest often co-exist with a set of background activities. Temporal groundtruth typically very ambiguous, as it does not distinguish the event of interest from a wide range of co-existing background activities. However, it is not practical to annotate the locations of the events in large amounts of video data. This problem becomes more serious in the detection of multi-agent interactions, since the location of these events can often not be constrained to within a bounding box. 5) Challenges in determining the temporal boundaries of the events: An event can occur at any arbitrary time with an arbitrary duration. The temporal segmentation of events is difficult and ambiguous, and also affected by other factors such as occlusions.

Analyzing the effectiveness of graph metrics for anomaly detection in online social networks

Online social networks can be modelled as graphs; in this paper, we analyze the use of graph metrics for identifying users with anomalous relationships to other users. A framework is proposed for analyzing the effectiveness of various graph theoretic properties such as the number of neighbouring nodes and edges, betweenness centrality, and community cohesiveness in detecting anomalous users. Experimental results on real-world data collected from online social networks show that the majority of users typically have friends who are friends themselves, whereas anomalous users’ graphs typically do not follow this common rule. Empirical analysis also shows that the relationship between average betweenness centrality and edges identifies anomalies more accurately than other approaches.

Static analysis of executables for collaborative malware detection on Android

Smartphones are getting increasingly popular and several malwares appeared targeting these devices. General countermeasures to smartphone malwares are currently limited to signature-based antivirus scanners which efficiently detect known malwares, but they have serious shortcomings with new and unknown malwares creating a window of opportunity for attackers. As smartphones become host for sensitive data and applications, extended malware detection mechanisms are necessary complying with the corresponding resource constraints. The contribution of this paper is twofold. First, we perform static analysis on the executables to extract their function calls in Android environment using the command readelf. Function call lists are compared with malware executables for classifying them with PART, Prism and Nearest Neighbor Algorithms. Second, we present a collaborative malware detection approach to extend these results. Corresponding simulation results are presented.

An Android application sandbox system for suspicious software detection

Smartphones are steadily gaining popularity, creating new application areas as their capabilities increase in terms of computational power, sensors and communication. Emerging new features of mobile devices give opportunity to new threats. Android is one of the newer operating systems targeting smartphones. While being based on a Linux kernel, Android has unique properties and specific limitations due to its mobile nature. This makes it harder to detect and react upon malware attacks if using conventional techniques. In this paper, we propose an Android Application Sandbox (AASandbox) which is able to perform both static and dynamic analysis on Android programs to automatically detect suspicious applications. Static analysis scans the software for malicious patterns without installing it. Dynamic analysis executes the application in a fully isolated environment, i.e. sandbox, which intervenes and logs low-level interactions with the system for further analysis. Both the sandbox and the detection algorithms can be deployed in the cloud, providing a fast and distributed detection of suspicious software in a mobile software store akin to Google's Android Market. Additionally, AASandbox might be used to improve the efficiency of classical anti-virus applications available for the Android operating system.

Collaborative intrusion detection framework : characteristics, adversarial opportunities and countermeasures

Complex Internet attacks may come from multiple sources, and target multiple networks and technologies. Nevertheless, Collaborative Intrusion Detection Systems (CIDS) emerges as a promising solution by using information from multiple sources to gain a better understanding of objective and impact of complex Internet attacks. CIDS also help to cope with classical problems of Intrusion Detection Systems (IDS) such as zero-day attacks, high false alarm rates and architectural challenges, e. g., centralized designs exposing the Single-Point-of-Failure. Improved complexity on the other hand gives raise to new exploitation opportunities for adversaries. The contribution of this paper is twofold. We first investigate related research on CIDS to identify the common building blocks and to understand vulnerabilities of the Collaborative Intrusion Detection Framework (CIDF). Second, we focus on the problem of anonymity preservation in a decentralized intrusion detection related message exchange scheme. We use techniques from design theory to provide multi-path peer-to-peer communication scheme where the adversary can not perform better than guessing randomly the originator of an alert message.

Framework for evaluating collaborative intrusion detection systems

Securing IT infrastructures of our modern lives is a challenging task because of their increasing complexity, scale and agile nature. Monolithic approaches such as using stand-alone firewalls and IDS devices for protecting the perimeter cannot cope with complex malwares and multistep attacks. Collaborative security emerges as a promising approach. But, research results in collaborative security are not mature, yet, and they require continuous evaluation and testing. In this work, we present CIDE, a Collaborative Intrusion Detection Extension for the network security simulation platform ( NeSSi 2 ). Built-in functionalities include dynamic group formation based on node preferences, group-internal communication, group management and an approach for handling the infection process for malware-based attacks. The CIDE simulation environment provides functionalities for easy implementation of collaborating nodes in large-scale setups. We evaluate the group communication mechanism on the one hand and provide a case study and evaluate our collaborative security evaluation platform in a signature exchange scenario on the other.

Detection of banana bunchy top virus in virus-infected plants using polymerase chain reaction

Polymerase chain reaction (PCR) was developed for the detection of Banana bunchy top virus (BBTV) at maximum after 210 min and at minimum after 90 min using Pc-1 and Pc-2, respectively. PCR detection of BBTV in crude sap indicated that the freezing of banana tissue in liquid nitrogen (LN2) before extraction was more effective than using sand as the extraction technique. BBTV was also detected using PCR assay in 69 healthy and diseased plants using Na-PO4 buffer containing 1 % SDS. PCR detection of BBTV in nucleic acid extracts using seven different extraction buffers to adapt the use of PCR in routine detection in the field was studied. Results proved that BBTV was detected with high sensitivity in nucleic acid extracts more than in infectious sap. The results also suggested the common aetiology for the BBTV by the PCR reactions of BBTV in nucleic acid extracts from Australia, Burundi, Egypt, France, Gabon, Philippines and Taiwan. Results also proved a positive relation between the Egyptian-BBTV isolate and abaca bunchy top isolate from the Philippines, but there no relation was found with the Cucumber mosaic cucumovirus (CMV) isolates from Egypt and Philippines and Banana bract mosaic virus (BBMV) were found.

Decentralized detector generation in cooperative intrusion detection system

We consider Cooperative Intrusion Detection System (CIDS) which is a distributed AIS-based (Artificial Immune System) IDS where nodes collaborate over a peer-to-peer overlay network. The AIS uses the negative selection algorithm for the selection of detectors (e.g., vectors of features such as CPU utilization, memory usage and network activity). For better detection performance, selection of all possible detectors for a node is desirable but it may not be feasible due to storage and computational overheads. Limiting the number of detectors on the other hand comes with the danger of missing attacks. We present a scheme for the controlled and decentralized division of detector sets where each IDS is assigned to a region of the feature space. We investigate the trade-off between scalability and robustness of detector sets. We address the problem of self-organization in CIDS so that each node generates a distinct set of the detectors to maximize the coverage of the feature space while pairs of nodes exchange their detector sets to provide a controlled level of redundancy. Our contribution is twofold. First, we use Symmetric Balanced Incomplete Block Design, Generalized Quadrangles and Ramanujan Expander Graph based deterministic techniques from combinatorial design theory and graph theory to decide how many and which detectors are exchanged between which pair of IDS nodes. Second, we use a classical epidemic model (SIR model) to show how properties from deterministic techniques can help us to reduce the attack spread rate.