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.

Hot topic detection in news blogs from the perspective of W2T

News blog hot topics are important for the information recommendation service and marketing. However, information overload and personalized management make the information arrangement more difficult. Moreover, what influences the formation and development of blog hot topics is seldom paid attention to. In order to correctly detect news blog hot topics, the paper first analyzes the development of topics in a new perspective based on W2T (Wisdom Web of Things) methodology. Namely, the characteristics of blog users, context of topic propagation and information granularity are unified to analyze the related problems. Some factors such as the user behavior pattern, network opinion and opinion leader are subsequently identified to be important for the development of topics. Then the topic model based on the view of event reports is constructed. At last, hot topics are identified by the duration, topic novelty, degree of topic growth and degree of user attention. The experimental results show that the proposed method is feasible and effective.

Elemental characterisation and detection of heavy metals in house dusts collected from sites in South-East Queensland, Australia

Knowledge of the elements present in house dusts is important in understanding potential health effects on humans. In this study, dust samples collected from 10 houses in south-east Queensland have been analysed by scanning electron microscopy and X-ray microanalysis to measure the inorganic element compositions and to investigate the form of heavy metals in the dusts. The overall analytical results were then used to discriminate between different localities using chemometric techniques. The relative amounts of elements, particularly of Si, Ca, and Fe, varied between size fractions and between different locations for the same size fraction. By analysing individual small particles, many other constituents were identified including Ti, Cr, Mn, Ni, Cu, Zn, Ba, Ag, W, Au, Hg, Pb, Bi, La and Ce. The heavy metals were mostly concentrated in small particles in the smaller size fractions, which allowed detection by particle analysis, though their average concentrations were very low.

Static smartphone malware detection

Static anaylsis represents an approach of checking source code or compiled code of applications before it gets executed. Chess and McGraw state that static anaylsis promises to identify common coding problems automatically. While manual code checking is also a form of static analysis, software tools are used in most cases in order to perform the checks. Chess and McGraw additionaly claim that good static checkers can help to spot and eradicate common security bugs.

Teams rather than individuals : collaborative intrusion detection

We propose CIMD (Collaborative Intrusion and Malware Detection), a scheme for the realization of collaborative intrusion detection approaches. We argue that teams, respectively detection groups with a common purpose for intrusion detection and response, improve the measures against malware. CIMD provides a collaboration model, a decentralized group formation and an anonymous communication scheme. Participating agents can convey intrusion detection related objectives and associated interests for collaboration partners. These interests are based on intrusion objectives and associated interests for collaboration partners. These interests are based on intrusion detection related ontology, incorporating network and hardware configurations and detection capabilities. Anonymous Communication provided by CIMD allows communication beyond suspicion, i.e. the adversary can not perform better than guessing an IDS to be the source of a message at random. The evaluation takes place with the help of NeSSi² (www.nessi2.de), the Network Security Simulator, a dedicated environment for analysis of attacks and countermeasures in mid-scale and large-scale networks. A CIMD prototype is being built based on the JIAC agent framework(www.jiac.de).

A formal method for attack modelling and detection

This paper presents a formal methodology for attack modeling and detection for networks. Our approach has three phases. First, we extend the basic attack tree approach 1 to capture (i) the temporal dependencies between components, and (ii) the expiration of an attack. Second, using the enhanced attack trees (EAT) we build a tree automaton that accepts a sequence of actions from input stream if there is a traverse of an attack tree from leaves to the root node. Finally, we show how to construct an enhanced parallel automaton (EPA) that has each tree automaton as a subroutine and can process the input stream by considering multiple trees simultaneously. As a case study, we show how to represent the attacks in IEEE 802.11 and construct an EPA for it.

Monitoring android for collaborative anomaly detection : a first architectural draft

Our daily lives become more and more dependent upon smartphones due to their increased capabilities. Smartphones are used in various ways, e.g. for payment systems or assisting the lives of elderly or disabled people. Security threats for these devices become more and more dangerous since there is still a lack of proper security tools for protection. Android emerges as an open smartphone platform which allows modification even on operating system level and where third-party developers first time have the opportunity to develop kernel-based low-level security tools. Android quickly gained its popularity among smartphone developers and even beyond since it bases on Java on top of "open" Linux in comparison to former proprietary platforms which have very restrictive SDKs and corresponding APIs. Symbian OS, holding the greatest market share among all smartphone OSs, was even closing critical APIs to common developers and introduced application certification. This was done since this OS was the main target for smartphone malwares in the past. In fact, more than 290 malwares designed for Symbian OS appeared from July 2004 to July 2008. Android, in turn, promises to be completely open source. Together with the Linux-based smartphone OS OpenMoko, open smartphone platforms may attract malware writers for creating malicious applications endangering the critical smartphone applications and owners privacy. Since signature-based approaches mainly detect known malwares, anomaly-based approaches can be a valuable addition to these systems. They base on mathematical algorithms processing data that describe the state of a certain device. For gaining this data, a monitoring client is needed that has to extract usable information (features) from the monitored system. Our approach follows a dual system for analyzing these features. On the one hand, functionality for on-device light-weight detection is provided. But since most algorithms are resource exhaustive, remote feature analysis is provided on the other hand. Having this dual system enables event-based detection that can react to the current detection need. In our ongoing research we aim to investigates the feasibility of light-weight on-device detection for certain occasions. On other occasions, whenever significant changes are detected on the device, the system can trigger remote detection with heavy-weight algorithms for better detection results. In the absence of the server respectively as a supplementary approach, we also consider a collaborative scenario. Here, mobile devices sharing a common objective are enabled by a collaboration module to share information, such as intrusion detection data and results. This is based on an ad-hoc network mode that can be provided by a WiFi or Bluetooth adapter nearly every smartphone possesses.

Solving inherent problems of anomaly detection by cooperation

Anomaly detection compensates shortcomings of signature-based detection such as protecting against Zero-Day exploits. However, Anomaly Detection can be resource-intensive and is plagued by a high false-positive rate. In this work, we address these problems by presenting a Cooperative Intrusion Detection approach for the AIS, the Artificial Immune System, as an example for an anomaly detection approach. In particular we show, how the cooperative approach reduces the false-positive rate of the detection and how the overall detection process can be organized to account for the resource constraints of the participating devices. Evaluations are carried out with the novel network simulation environment NeSSi as well as formally with an extension to the epidemic spread model SIR

Power of QTL detection using association tests with family controls

The power of testing for a population-wide association between a biallelic quantitative trait locus and a linked biallelic marker locus is predicted both empirically and deterministically for several tests. The tests were based on the analysis of variance (ANOVA) and on a number of transmission disequilibrium tests (TDT). Deterministic power predictions made use of family information, and were functions of population parameters including linkage disequilibrium, allele frequencies, and recombination rate. Deterministic power predictions were very close to the empirical power from simulations in all scenarios considered in this study. The different TDTs had very similar power, intermediate between one-way and nested ANOVAs. One-way ANOVA was the only test that was not robust against spurious disequilibrium. Our general framework for predicting power deterministically can be used to predict power in other association tests. Deterministic power calculations are a powerful tool for researchers to plan and evaluate experiments and obviate the need for elaborate simulation studies.

Evaluation of strain energy based damage indices for flexural crack detection of RC beams

Vibration Based Damage Identification Techniques which use modal data or their functions, have received significant research interest in recent years due to their ability to detect damage in structures and hence contribute towards the safety of the structures. In this context, Strain Energy Based Damage Indices (SEDIs), based on modal strain energy, have been successful in localising damage in structuers made of homogeneous materials such as steel. However, their application to reinforced concrete (RC) structures needs further investigation due to the significant difference in the prominent damage type, the flexural crack. The work reported in this paper is an integral part of a comprehensive research program to develop and apply effective strain energy based damage indices to assess damage in reinforced concrete flexural members. This research program established (i) a suitable flexural crack simulation technique, (ii) four improved SEDI's and (iii) programmable sequentional steps to minimise effects of noise. This paper evaluates and ranks the four newly developed SEDIs and existing seven SEDIs for their ability to detect and localise flexural cracks in RC beams. Based on the results of the evaluations, it recommends the SEDIs for use with single and multiple vibration modes.

Genetic variability and antimicrobial resistance of Ureaplasma parvum in response to maternal erythromycin treatment : a study in pregnant sheep

Background: Ureaplasmas are the most frequently isolated microorganisms from the amniotic fluid (AF) of pregnant women and can cause chronic infections that are difficult to eradicate with standard macrolide treatment. We tested the effects of erythromycin treatment on phenotypic and genotypic markers of ureaplasmal antimicrobial resistance in sheep. Method: At 50 days of gestation (d, term=145d) 12 pregnant ewes received intra-amniotic injections of U. parvum serovar 3 (erythromycin-sensitive, 2x104 colony-forming-units). At 100d ewes received: erythromycin treatment (500 mg, q3h for 4 days, IM, n=6) or no treatment (n=6). Fetuses were delivered surgically (125d) and AF and chorioamnion were collected for: culture, minimum inhibitory concentration (MIC) and minimum biofilm inhibitory concentration (MBIC) testing; 23S rRNA sequencing; and detection of macrolide-lincosamide-streptogramin resistance (MLSr) genes. Results: MICs of erythromycin, azithromycin and roxithromycin against AF isolates were low (range = 0.06 mg/L to 1.0 mg/L); however, chorioamnion isolates demonstrated increased resistance to roxithromycin (0.13 – 5.33 mg/L). 62.5% of chorioamnion ureaplasmas formed biofilms in vitro and mutations (125 nucleotides, 29.6%) were found in the 23S rRNA gene (domain V) of chorioamnion (but not AF) ureaplasmas. MLSr genes (ermB, msrC and msrD) were detected in 100% of chorioamnion isolates and only msrD was detected in AF isolates (40%). Conclusions: 23S rRNA mutations and MLSr genes occurred independently of erythromycin treatment, suggesting that the anatomical site of infection and microenvironment may exert selective pressures on ureaplasmas that cause genetic changes and alter antimicrobial sensitivity profiles. These results have serious implications for treatment of in utero infections.

Breast cancer detection from thermal images using bispectral invariant features

Highly sensitive infrared (IR) cameras provide high-resolution diagnostic images of the temperature and vascular changes of breasts. These images can be processed to emphasize hot spots that exhibit early and subtle changes owing to pathology. The resulting images show clusters that appear random in shape and spatial distribution but carry class dependent information in shape and texture. Automated pattern recognition techniques are challenged because of changes in location, size and orientation of these clusters. Higher order spectral invariant features provide robustness to such transformations and are suited for texture and shape dependent information extraction from noisy images. In this work, the effectiveness of bispectral invariant features in diagnostic classification of breast thermal images into malignant, benign and normal classes is evaluated and a phase-only variant of these features is proposed. High resolution IR images of breasts, captured with measuring accuracy of ±0.4% (full scale) and temperature resolution of 0.1 °C black body, depicting malignant, benign and normal pathologies are used in this study. Breast images are registered using their lower boundaries, automatically extracted using landmark points whose locations are learned during training. Boundaries are extracted using Canny edge detection and elimination of inner edges. Breast images are then segmented using fuzzy c-means clustering and the hottest regions are selected for feature extraction. Bispectral invariant features are extracted from Radon projections of these images. An Adaboost classifier is used to select and fuse the best features during training and then classify unseen test images into malignant, benign and normal classes. A data set comprising 9 malignant, 12 benign and 11 normal cases is used for evaluation of performance. Malignant cases are detected with 95% accuracy. A variant of the features using the normalized bispectrum, which discards all magnitude information, is shown to perform better for classification between benign and normal cases, with 83% accuracy compared to 66% for the original.

Enhanced hydrogen separation by vertically-aligned carbon nanotube membranes with zeolite imidazolate frameworks as a selective layer

Vertically-aligned carbon nanotube (VACNT) membranes show very high permeation fluxes due to the inherent smooth and frictionless nature of the interior of the nanotubes. However, the hydrogen selectivities are all in the Knudsen range and are quite low. In this study we grew molecular sieve zeolite imidazolate frameworks (ZIFs) via secondary seeded growth on the VACNT membranes as a gas selective layer. The ZIF layer has a thickness of 5–6 μm and shows good contact with the VACNT membrane surface. The VACNT supported ZIF membrane shows much higher H2 selectivity than Ar (7.0); O2 (13.6); N2 (15.1) and CH4 (9.8). We conclude that tailoring metal–organic frameworks on the membrane surface can be an effective route to improve the gas separation performance of the VACNT membrane.

Generator coherency and area detection in large power systems

This paper provides a new general approach for defining coherent generators in power systems based on the coherency in low frequency inter-area modes. The disturbance is considered to be distributed in the network by applying random load changes which is the random walk representation of real loads instead of a single fault and coherent generators are obtained by spectrum analysis of the generators velocity variations. In order to find the coherent areas and their borders in the inter-connected networks, non-generating buses are assigned to each group of coherent generator using similar coherency detection techniques. The method is evaluated on two test systems and coherent generators and areas are obtained for different operating points to provide a more accurate grouping approach which is valid across a range of realistic operating points of the system.