A comparison of the classification of disparate malware collected in different time periods

It has been argued that an anti-virus strategy based on malware collected at a certain date, will not work at a later date because malware evolves rapidly and an anti-virus engine is then faced with a completely new type of executable not as amenable to detection as the first was.

In this paper, we test this idea by collecting two sets of malware, the first from 2002 to 2007, the second from 2009 to 2010 to determine how well the anti-virus strategy we developed based on the earlier set [18] will do on the later set. This anti-virus strategy integrates dynamic and static features extracted from the executables to classify malware by distinguishing between families. We also perform another test, to investigate the same idea whereby we accumulate all the malware executables in the old and new dataset, separately, and apply a malware versus cleanware classification.

The resulting classification accuracies are very close for both datasets, with a difference of approximately 5.4% for both experiments, the older malware being more accurately classified than the newer malware. This leads us to conjecture that current anti-virus strategies can indeed be modified to deal effectively with new malware.

Identifying microphone from noisy recordings by using representative instance one class-classification approach

Rapid growth of technical developments has created huge challenges for microphone forensics - a sub-category of audio forensic science, because of the availability of numerous digital recording devices and massive amount of recording data. Demand for fast and efficient methods to assure integrity and authenticity of information is becoming more and more important in criminal investigation nowadays. Machine learning has emerged as an important technique to support audio analysis processes of microphone forensic practitioners. However, its application to real life situations using supervised learning is still facing great challenges due to expensiveness in collecting data and updating system. In this paper, we introduce a new machine learning approach which is called One-class Classification (OCC) to be applied to microphone forensics; we demonstrate its capability on a corpus of audio samples collected from several microphones. In addition, we propose a representative instance classification framework (RICF) that can effectively improve performance of OCC algorithms for recording signal with noise. Experiment results and analysis indicate that OCC has the potential to benefit microphone forensic practitioners in developing new tools and techniques for effective and efficient analysis.

Impaction bone grafting of the glenoid in revision shoulder arthroplasty : classification, technical description and early results

Background Glenoid component fixation remains an issue in the long-term survival of total shoulder arthroplasty. As a consequence revision of the glenoid component is becoming increasingly more common and reconstructive techniques to preserve and restore bone stock are becoming more important.

Methods In this article we describe the combined technique of impaction grafting and glenoid component exchange together with a classification of the glenoid defect with a report on four sequential cases in patients with rheumatoid arthritis with an average age of 56 years. The minimum follow-up was 34 months (range 34 months to 62 months).

Results Patients reported excellent pain relief and some improvement in motion and function. The complication rate remains low. Radiological assessment using tomograms showed good incorporation of the bone graft and minimal signs of glenoid loosening.

Conclusion The results of this study confirm that at least in the short term impaction grafting techniques used to reconstitute the glenoid in revision surgery can be successful.

Interobserver reliability of radial head fracture classification : two-dimensional compared with three-dimensional CT

Background: The Broberg and Morrey modification of the Mason classification of radial head fractures has substantial interobserver variation. This study used a large web-based collaborative of experienced orthopaedic surgeons to test the hypothesis that three-dimensional reconstructions of computed tomography (CT) scans improve the interobserver reliability of the classification of radial head fractures according to the Broberg and Morrey modification of the Mason classification.

Methods: Eighty-five orthopaedic surgeons evaluated twelve radial head fractures. They were randomly assigned to review either radiographs and two-dimensional CT scans or radiographs and three-dimensional CT images to determine the fracture classification, fracture characteristics, and treatment recommendations. The kappa multirater measure (κ) was calculated to estimate agreement between observers.

Results: Three-dimensional CT had moderate agreement and two-dimensional CT had fair agreement among observers for the Broberg and Morrey modification of the Mason classification, a difference that was significant. Observers assessed seven fracture characteristics, including fracture line, comminution, articular surface involvement, articular step or gap of ≥2 mm, central impaction, recognition of more than three fracture fragments, and fracture fragments too small to repair. There was a significant difference in kappa values between three-dimensional CT and two-dimensional CT for fracture fragments too small to repair, recognition of three fracture fragments, and central impaction. The difference between the other four fracture characteristics was not significant. Among treatment recommendations, there was fair agreement for both three-dimensional CT and two-dimensional CT.

Conclusions: Although three-dimensional CT led to some small but significant decreases in interobserver variation, there is still considerable disagreement regarding classification and characterization of radial head fractures. Three-dimensional CT may be insufficient to optimize interobserver agreement.

Interobserver reliability of coronoid fracture classification : two-dimensional versus three-dimensional computed tomography

Purpose : This study tests the hypothesis that 3-dimensional computed tomography (CT) reconstructions improve interobserver agreement on classification and treatment of coronoid fractures compared with 2-dimensional CT.

Methods : A total of 29 orthopedic surgeons evaluated 10 coronoid fractures on 2 occasions (first with radiographs and 2-dimensional CT and then with radiographs and 3-dimensional CT), separated by a minimum of 2 weeks. Surgeons classified fractures according to the classifications of Regan and Morrey and of O'Driscoll et al., identified specific characteristics, recommended the most appropriate treatment approach, and made treatment recommendations. The kappa multirater measure (κ) was calculated to estimate agreement between observers.

Results : Regardless of the imaging modality used, there was fair to moderate agreement for most of the observations. Three-dimensional CT improved interobserver agreement in Regan and Morrey's classsication (κ3-dimensional = 0.51 vs κ2-dimensional = 0.40; p < .001) and O'Driscoll et al.'s classifications (κ3-dimensional = 0.48 vs κ2-dimensional = 0.42; p = .009). There were trends toward better reliability for 3-dimensional reconstruction in recognition of coronoid tip fractures (κ3-dimensional = 0.19, κ2-dimensional = 0.03; p = .268), comminution (κ3-dimensional = 0.41 vs κ2-dimensional = 0.29; p = .133), and impacted fragments (κ3-dimensional = 0.39 vs κ2-dimensional = 0.27; p = .094), and in surgeons' opinions on the need for something other than screws or plate for surgical fixation (κ3-dimensional = 0.31 vs κ2-dimensional = 0.15; p = .138). Interobserver agreement on treatment approach was better with 2-dimensional CT (κ3-dimensional = 0.27, κ2-dimensional = 0.32; p = .015).

Conclusions : Three-dimensional CT reconstructions improve interobserver agreement with respect to fracture classification compared with 2-dimensional CT.

A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification

In this paper, a two-stage pattern classification and rule extraction system is proposed. The first stage consists of a modified fuzzy min-max (FMM) neural-network-based pattern classifier, while the second stage consists of a genetic-algorithm (GA)-based rule extractor. Fuzzy if-then rules are extracted from the modified FMM classifier, and a ??don't care?? approach is adopted by the GA rule extractor to minimize the number of features in the extracted rules. Five benchmark problems and a real medical diagnosis task are used to empirically evaluate the effectiveness of the proposed FMM-GA system. The results are analyzed and compared with other published results. In addition, the bootstrap hypothesis analysis is conducted to quantify the results of the medical diagnosis task statistically. The outcomes reveal the efficacy of FMM-GA in extracting a set of compact and yet easily comprehensible rules while maintaining a high classification performance for tackling pattern classification tasks.

A hybrid neural classifier for dimensionality reduction and data visualization and its application to fault detection and classification of induction motors

In this paper, a hybrid neural classifier combining the auto-encoder neural network and the Lattice Vector Quantization (LVQ) model is described. The auto-encoder network is used for dimensionality reduction by projecting high dimensional data into the 2D space. The LVQ model is used for data visualization by forming and adapting the granularity of a data map. The mapped data are employed to predict the target classes of new data samples. To improve classification accuracy, a majority voting scheme is adopted by the hybrid classifier. To demonstrate the applicability of the hybrid classifier, a series of experiments using simulated and real fault data from induction motors is conducted. The results show that the hybrid classifier is able to outperform the Multi-Layer Perceptron neural network, and to produce very good classification accuracy rates for various fault conditions of induction motors.