Gap analysis of intrusion detection in smart grids

Given the recent emergence of the smart grid and smart grid related technologies, their security is a prime concern. Intrusion detection provides a second line of defense. However, conventional intrusion detection systems (IDSs) are unable to adequately address the unique requirements of the smart grid. This paper presents a gap analysis of contemporary IDSs from a smart grid perspective. This paper highlights the lack of adequate intrusion detection within the smart grid and discusses the limitations of current IDSs approaches. The gap analysis identifies current IDSs as being unsuited to smart grid application without significant changes to address smart grid specific requirements.

Data preprocessing for anomaly based network intrusion detection : a review

Data preprocessing is widely recognized as an important stage in anomaly detection. This paper reviews the data preprocessing techniques used by anomaly-based network intrusion detection systems (NIDS), concentrating on which aspects of the network traffic are analyzed, and what feature construction and selection methods have been used. Motivation for the paper comes from the large impact data preprocessing has on the accuracy and capability of anomaly-based NIDS. The review finds that many NIDS limit their view of network traffic to the TCP/IP packet headers. Time-based statistics can be derived from these headers to detect network scans, network worm behavior, and denial of service attacks. A number of other NIDS perform deeper inspection of request packets to detect attacks against network services and network applications. More recent approaches analyze full service responses to detect attacks targeting clients. The review covers a wide range of NIDS, highlighting which classes of attack are detectable by each of these approaches. Data preprocessing is found to predominantly rely on expert domain knowledge for identifying the most relevant parts of network traffic and for constructing the initial candidate set of traffic features. On the other hand, automated methods have been widely used for feature extraction to reduce data dimensionality, and feature selection to find the most relevant subset of features from this candidate set. The review shows a trend toward deeper packet inspection to construct more relevant features through targeted content parsing. These context sensitive features are required to detect current attacks.

Reactive image-based collision avoidance system for unmanned aircraft systems

Approximately 20 years have passed now since the NTSB issued its original recommendation to expedite development, certification and production of low-cost proximity warning and conflict detection systems for general aviation [1]. While some systems are in place (TCAS [2]), ¡¨see-and-avoid¡¨ remains the primary means of separation between light aircrafts sharing the national airspace. The requirement for a collision avoidance or sense-and-avoid capability onboard unmanned aircraft has been identified by leading government, industry and regulatory bodies as one of the most significant challenges facing the routine operation of unmanned aerial systems (UAS) in the national airspace system (NAS) [3, 4]. In this thesis, we propose and develop a novel image-based collision avoidance system to detect and avoid an upcoming conflict scenario (with an intruder) without first estimating or filtering range. The proposed collision avoidance system (CAS) uses relative bearing ƒÛ and angular-area subtended ƒê , estimated from an image, to form a test statistic AS C . This test statistic is used in a thresholding technique to decide if a conflict scenario is imminent. If deemed necessary, the system will command the aircraft to perform a manoeuvre based on ƒÛ and constrained by the CAS sensor field-of-view. Through the use of a simulation environment where the UAS is mathematically modelled and a flight controller developed, we show that using Monte Carlo simulations a probability of a Mid Air Collision (MAC) MAC RR or a Near Mid Air Collision (NMAC) RiskRatio can be estimated. We also show the performance gain this system has over a simplified version (bearings-only ƒÛ ). This performance gain is demonstrated in the form of a standard operating characteristic curve. Finally, it is shown that the proposed CAS performs at a level comparable to current manned aviations equivalent level of safety (ELOS) expectations for Class E airspace. In some cases, the CAS may be oversensitive in manoeuvring the owncraft when not necessary, but this constitutes a more conservative and therefore safer, flying procedures in most instances.

Characterization of sky-region morphological-temporal airborne collision detection

Automated airborne collision-detection systems are a key enabling technology for facilitat- ing the integration of unmanned aerial vehicles (UAVs) into the national airspace. These safety-critical systems must be sensitive enough to provide timely warnings of genuine air- borne collision threats, but not so sensitive as to cause excessive false-alarms. Hence, an accurate characterisation of detection and false alarm sensitivity is essential for understand- ing performance trade-offs, and system designers can exploit this characterisation to help achieve a desired balance in system performance. In this paper we experimentally evaluate a sky-region, image based, aircraft collision detection system that is based on morphologi- cal and temporal processing techniques. (Note that the examined detection approaches are not suitable for the detection of potential collision threats against a ground clutter back- ground). A novel collection methodology for collecting realistic airborne collision-course target footage in both head-on and tail-chase engagement geometries is described. Under (hazy) blue sky conditions, our proposed system achieved detection ranges greater than 1540m in 3 flight test cases with no false alarm events in 14.14 hours of non-target data (under cloudy conditions, the system achieved detection ranges greater than 1170m in 4 flight test cases with no false alarm events in 6.63 hours of non-target data). Importantly, this paper is the first documented presentation of detection range versus false alarm curves generated from airborne target and non-target image data.

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.

Obstacle detection for a mining vehicle using a 2D laser

This paper discusses a number of key issues for the development of robust obstacle detection systems for autonomous mining vehicles. Strategies for obstacle detection are described and an overview of the state-of-the-art in obstacle detection for outdoor autonomous vehicles using lasers is presented, with their applicability to the mining environment noted. The development of an obstacle detection system for a mining vehicle is then detailed. This system uses a 2D laser scanner as the prime sensor and combines dead-reckoning data with laser data to create local terrain maps. The slope of the terrain maps is then used to detect potential obstacles.

Issues in obstacle detection for autonomous mining and construction vehicles

This paper discusses a Dumber of key issues for the development of robust, obstacle detection systems for autonomous mining and construction vehicles. A taxonomy of obstacle detection systems is described; An overview of the state-of- the-art in obstacle detection for outdoor autonomous vehicles is presented with their applicability to the mining and construction environments noted. The issue of so-called fail-safe obstacle detection is then discussed. Finally, we describe the development of an obstacle detection system for a mining vehicle.

Heated Nebulizer Microchips for Mass Spectrometry

Miniaturized analytical devices, such as heated nebulizer (HN) microchips studied in this work, are of increasing interest owing to benefits like faster operation, better performance, and lower cost relative to conventional systems. HN microchips are microfabricated devices that vaporize liquid and mix it with gas. They are used with low liquid flow rates, typically a few µL/min, and have previously been utilized as ion sources for mass spectrometry (MS). Conventional ion sources are seldom feasible at such low flow rates. In this work HN chips were developed further and new applications were introduced. First, a new method for thermal and fluidic characterization of the HN microchips was developed and used to study the chips. Thermal behavior of the chips was also studied by temperature measurements and infrared imaging. An HN chip was applied to the analysis of crude oil – an extremely complex sample – by microchip atmospheric pressure photoionization (APPI) high resolution mass spectrometry. With the chip, the sample flow rate could be reduced significantly without loss of performance and with greatly reduced contamination of the MS instrument. Thanks to its suitability to high temperature, microchip APPI provided efficient vaporization of nonvolatile compounds in crude oil. The first microchip version of sonic spray ionization (SSI) was presented. Ionization was achieved by applying only high (sonic) speed nebulizer gas to an HN microchip. SSI significantly broadens the range of analytes ionizable with the HN chips, from small stable molecules to labile biomolecules. The analytical performance of the microchip SSI source was confirmed to be acceptable. The HN microchips were also used to connect gas chromatography (GC) and capillary liquid chromatography (LC) to MS, using APPI for ionization. Microchip APPI allows efficient ionization of both polar and nonpolar compounds whereas with the most popular electrospray ionization (ESI) only polar and ionic molecules are ionized efficiently. The combination of GC with MS showed that, with HN microchips, GCs can easily be used with MS instruments designed for LC-MS. The presented analytical methods showed good performance. The first integrated LC–HN microchip was developed and presented. In a single microdevice, there were structures for a packed LC column and a heated nebulizer. Nonpolar and polar analytes were efficiently ionized by APPI. Ionization of nonpolar and polar analytes is not possible with previously presented chips for LC–MS since they rely on ESI. Preliminary quantitative performance of the new chip was evaluated and the chip was also demonstrated with optical detection. A new ambient ionization technique for mass spectrometry, desorption atmospheric pressure photoionization (DAPPI), was presented. The DAPPI technique is based on an HN microchip providing desorption of analytes from a surface. Photons from a photoionization lamp ionize the analytes via gas-phase chemical reactions, and the ions are directed into an MS. Rapid analysis of pharmaceuticals from tablets was successfully demonstrated as an application of DAPPI.

MOE and MOR assessment technologies for improving graded recovery of exotic pines in Australia.

This project was designed to provide the structural softwood processing industry with the basis for improved green and dry grading to allow maximise MGP grade yields, consistent product performance and reduced processing costs. To achieve this, advanced statistical techniques were used in conjunction with state-of-the-art property measurement systems. Specifically, the project aimed to make two significant steps forward for the Australian structural softwood industry: • assessment of technologies, both existing and novel, that may lead to selection of a consistent, reliable and accurate device for the log yard and green mill. The purpose is to more accurately identify and reject material that will not make a minimum grade of MGP10 downstream; • improved correlation of grading MOE and MOR parameters in the dry mill using new analytical methods and a combination of devices. The three populations tested were stiffness-limited radiata pine, strength-limited radiata pine and Caribbean pine. Resonance tests were conducted on logs prior to sawmilling, and on boards. Raw data from existing in-line systems were captured for the green and dry boards. The dataset was analysed using classical and advanced statistical tools to provide correlations between data sets and to develop efficient strength and stiffness prediction equations. Stiffness and strength prediction algorithms were developed from raw and combined parameters. Parameters were analysed for comparison of prediction capabilities using in-line parameters, off-line parameters and a combination of in-line and off-line parameters. The results show that acoustic resonance techniques have potential for log assessment, to sort for low stiffness and/or low strength, depending on the resource. From the log measurements, a strong correlation was found between the average static MOE of the dried boards within a log and the predicted value. These results have application in segregating logs into structural and non-structural uses. Some commercial technologies are already available for this application such as Hitman LG640. For green boards it was found that in-line and laboratory acoustic devices can provide a good prediction of dry static MOE and moderate prediction for MOR.There is high potential for segregating boards at this stage of processing. Grading after the log breakdown can improve significantly the effectiveness of the mill. Subsequently, reductions in non-structural volumes can be achieved. Depending on the resource it can be expected that a 5 to 8 % reduction in non structural boards won’t be dried with an associated saving of $70 to 85/m3. For dry boards, vibration and a standard Metriguard CLT/HCLT provided a similar level of prediction on stiffness limited resource. However, Metriguard provides a better strength prediction in strength limited resources (due to this equipment’s ability to measure local characteristics). The combination of grading equipment specifically for stiffness related predictors (Metriguard or vibration) with defect detection systems (optical or X-ray scanner) provides a higher level of prediction, especially for MOR. Several commercial technologies are already available for acoustic grading on board such those from Microtec, Luxscan, Falcon engineering or Dynalyse AB for example. Differing combinations of equipment, and their strategic location within the processing chain, can dramatically improve the efficiency of the mill, the level of which will vary depending of the resource. For example, an initial acoustic sorting on green boards combined with an optical scanner associated with an acoustic system for grading dry board can result in a large reduction of the proportion of low value low non-structural produced. The application of classical MLR on several predictors proved to be effective, in particular for MOR predictions. However, the usage of a modern statistics approach(chemometrics tools) such as PLS proved to be more efficient for improving the level of prediction. Compared to existing technologies, the results of the project indicate a good improvement potential for grading in the green mill, ahead of kiln drying and subsequent cost-adding processes. The next stage is the development and refinement of systems for this purpose.

Improvement in Intrusion Detection With Advances in Sensor Fusion

Various intrusion detection systems (IDSs) reported in the literature have shown distinct preferences for detecting a certain class of attack with improved accuracy, while performing moderately on the other classes. In view of the enormous computing power available in the present-day processors, deploying multiple IDSs in the same network to obtain best-of-breed solutions has been attempted earlier. The paper presented here addresses the problem of optimizing the performance of IDSs using sensor fusion with multiple sensors. The trade-off between the detection rate and false alarms with multiple sensors is highlighted. It is illustrated that the performance of the detector is better when the fusion threshold is determined according to the Chebyshev inequality. In the proposed data-dependent decision ( DD) fusion method, the performance optimization of ndividual IDSs is first addressed. A neural network supervised learner has been designed to determine the weights of individual IDSs depending on their reliability in detecting a certain attack. The final stage of this DD fusion architecture is a sensor fusion unit which does the weighted aggregation in order to make an appropriate decision. This paper theoretically models the fusion of IDSs for the purpose of demonstrating the improvement in performance, supplemented with the empirical evaluation.

Demonstration of a microfluidic polarimeter

Traditional methods of detecting chiral molecules, such as optical rotation are not suitable for miniaturization, since, the magnitude of the rotation of polarization scales down linearly with the optical path length of the device. Since the origin of optical activity is due to difference of refractive indices between the two circularly polarized states of light, it is possible to detect chiral media by measuring the dependence of the angles of refraction on the polarization state of the incident light. This however is a weak effect and hence requires sensitive optical detection schemes, based on novel polarization modulation techniques. The device can be scaled down for applications involving small sample volumes. Fabrication details of a prototype microfluidic device are described.

Cultural methods of detection for microorganisms: recent advances and successes

Most microbiological methods require culture to allow organisms to recover or to selectively increase, and target organisms are identified by growth on specific agar media. Many cultural methods take several days to complete and even then the results require confirmation. Alternative techniques include the use of chromogenic and fluorogenic substances to identify bacteria as they are growing, selective capture using antibodies after short periods of growth, molecular techniques, and direct staining with or without flow cytometry for enumeration and identification. Future microbiologists may not use culture but depend on the use of specific probes and sophisticated detection systems.

Plasmonic nanofocusing of light in an integrated silicon photonics platform.

The capability to focus electromagnetic energy at the nanoscale plays an important role in nanoscinece and nanotechnology. It allows enhancing light matter interactions at the nanoscale with applications related to nonlinear optics, light emission and light detection. It may also be used for enhancing resolution in microscopy, lithography and optical storage systems. Hereby we propose and experimentally demonstrate the nanoscale focusing of surface plasmons by constructing an integrated plasmonic/photonic on chip nanofocusing device in silicon platform. The device was tested directly by measuring the optical intensity along it using a near-field microscope. We found an order of magnitude enhancement of the intensity at the tip's apex. The spot size is estimated to be 50 nm. The demonstrated device may be used as a building block for "lab on a chip" systems and for enhancing light matter interactions at the apex of the tip.

G-quadruplex-based DNAzyme for sensitive mercury detection with the naked eye

Hg2+ is able to inhibit the peroxidase-like DNAzyme function of a T-containing G-quadruplex DNA via Hg2+-mediated T-T base pairs, which enables the visual detection of Hg2+ in the TMB-H2O2 reaction system with high selectivity and sensitivity.

Label-Free Colorimetric Detection of Aqueous Mercury Ion (Hg2+) Using Hg2+-Modulated G-Quadruplex-Based DNAzymes

Mercury ion (Hg2+) is able to specifically bind to the thymine-thymine (T-T) base pair in a DNA duplex, thus providing a rationale for DNA-based selective detection of Hg2+ with various means. In this work, we for the first time utilize the Hg2+-mediated T-T base pair to modulate the proper folding of G-quadruplex DNAs and inhibit the DNAzyme activity, thereby pioneering a facile approach to sense Hg2+ with colorimetry. Two bimolecular DNA G-quadruplexes containing many T residues are adopted here, which function well in low- and high-salt conditions, respectively. These G-quadruplex DNAs are able to bind hemin to form the peroxidase-like DNAzymes in the folded state. Upon addition of Hg2+, the proper folding of G-quadruplex DNAs is inhibited due to the formation of T-Hg2+-T complex. Ibis is reflected by the notable change of the Soret band of hemin when investigated by using UV-vis absorption spectroscopy. As a result of Hg2+ inhibition, a sharp decrease in the catalytic activity toward the H2O2-mediated oxidation of 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)diammonium salt (ABTS) is observed, accompanied by a change in solution color. Through this approach, aqueous Hg2+ can be detected at 50 nM (10 ppb) with colorimetry in a facile way, with high selectivity against other metal ions.