Using artificial neural networks to detect unknown computer worms

Detecting computer worms is a highly challenging task. We present a new approach that uses artificial neural networks (ANN) to detect the presence of computer worms based on measurements of computer behavior. We compare ANN to three other classification methods and show the advantages of ANN for detection of known worms. We then proceed to evaluate ANN’s ability to detect the presence of an unknown worm. As the measurement of a large number of system features may require significant computational resources, we evaluate three feature selection techniques. We show that, using only five features, one can detect an unknown worm with an average accuracy of 90%. We use a causal index analysis of our trained ANN to identify rules that explain the relationships between the selected features and the identity of each worm. Finally, we discuss the possible application of our approach to host-based intrusion detection systems.     

Regularized image recovery in scattering media

When imaging in scattering media, visibility degrades as objects become more distant. Visibility can be significantly restored by computer vision methods that account for physical processes occurring during image formation. Nevertheless, such recovery is prone to noise amplification in pixels corresponding to distant objects, where the medium transmittance is low. We present an adaptive filtering approach that counters the above problems: while significantly improving visibility relative to raw images, it inhibits noise amplification. Essentially, the recovery formulation is regularized, where the regularization adapts to the spatially varying medium transmittance. Thus, this regularization does not blur close objects. We demonstrate the approach in atmospheric and underwater experiments, based on an automatic method for determining the medium transmittance.     

On Locally Decodable Codes, Self-Correctable Codes, and t-Private PIR

A k-query locally decodable code (LDC) allows to probabilistically decode any bit of an encoded message by probing only k bits of its corrupted encoding. A stronger and desirable property is that of self-correction, allowing to efficiently recover not only bits of the message but also arbitrary bits of its encoding. In contrast to the initial constructions of LDCs, the recent and most efficient constructions are not known to be self-correctable. The existence of self-correctable codes of comparable efficiency remains open.     

The effect of cyclic loading on residual stresses and strains in a bonded joint

The mechanical performance of a structural bonded joint is mainly dependent on the interlaminar stresses and strains, the high concentration of which is close to the free edges of the adhesive. Under cyclic loading these stresses and strains are expected to be intensified and to accumulate. The present study deals with the distribution of the residual stresses and strains along the interlaminar adhesive layer under cyclic loading and how it is affected by geometrical edge conditions. A numerical non-linear finite element method was applied, the adhesive layer being regarded as an elasto-plastic bi-linear material with kinematic hardening (Bauschinger effect) which accounts for cyclic plasticity. Findings indicate that lateral normal residual strains in the edge of the adhesive layer are the major component which increases significantly with cycling and are probably responsible for failure initiation and propagation. It was also found that a significant reduction of stress concentration at the adhesive edge may be achieved by a modification of the free-edge geometry of both the adhesive and the adherend phases.     

Diurnal patterns of micropollutants concentrations in domestic greywater

In recent years, much interest has been given to presence of micropollutants in municipal wastewater, some of which are suspected to be endocrine disruptors, toxic or carcinogenic. Much less attention has been paid to their presence in greywater. The present research studies the diurnal patterns of micropollutants in greywater and computes their daily loads. Monitoring was carried-out using auto-controlled sampling system, designed to overcome the erratic greywater generation. Two main generation periods were identified: morning (5:00–11:00) and evening-night (18:00–2:00), contributing about 20% and >50% of daily greywater discharge, respectively. Average specific daily greywater discharge was 57 L p^?1d^?1, which matches reported value for greywater generated by showers and washbasins in Israel. The most frequently detected micropollutants in this study were methylparben (preservative), galaxolide (fragrance) and oxybenzone (UV-filter), which are common ingredients in many personal care-products. Their daily loads were 2, 840, 1, 887 and 728 µg p^?1d^?1, respectively.     

Measurement of Densification of Zinc Oxide Compacts Using an Optical, Noncontact, Noninvasive Extensometer

An optical noninvasive, noncontact extensometer was used to measure the shrinkage of zinc oxide powder compacts during sintering. Powder compacts were uniaxially and isostatically pressed from micrometer, submicrometer, and nano powders and sintered in a thermal oven at temperatures up to 1100°C. The nanometer-size sample started to densify at ∼400°C, about 200°C below the densification threshold of the micrometer-size sample. The results are in good agreement with those obtained using a contact dilatometer.