A cascaded classifier approach for improving detection rates on?rare attack categories in network intrusion detection

Network intrusion detection research work that employed KDDCup 99 dataset often encounter challenges in creating classifiers that could handle unequal distributed attack categories. The accuracy of a classification model could be jeopardized if the distribution of attack categories in a training dataset is heavily imbalanced where the rare categories are less than 2% of the total population. In such cases, the model could not efficiently learn the characteristics of rare categories and this will result in poor detection rates. In this research, we introduce an efficient and effective approach in dealing with the unequal distribution of attack categories. Our approach relies on the training of cascaded classifiers using a dichotomized training dataset in each cascading stage. The training dataset is dichotomized based on the rare and non-rare attack categories. The empirical findings support our arguments that training cascaded classifiers using the dichotomized dataset provides higher detection rates on the rare categories as well as comparably higher detection rates for the non-rare attack categories as compared to the findings reported in other research works. The higher detection rates are due to the mitigation of the influence from the dominant categories if the rare attack categories are separated from the dataset.     

Properties of Bayesian student model for INQPRO

Employing a probabilistic student model in a scientific inquiry learning environment often presents two challenges. First, what constitute the appropriate variables for modeling scientific inquiry skills in such a learning environment, considering the fact that it practices exploratory learning approach? Following exploratory learning approach, students are granted the freedom to navigate from one GUI to another. Second, do causal dependencies exist between the identified variables, and if they do, how should they be defined? To tackle the challenges, this research work attempted the Bayesian Networks framework. Leveraging on the framework, two student models were constructed to predict the acquisition of scientific inquiry skills for INQPRO, a scientific inquiry learning environment developed in this research work. The student models can be differentiated by the variables they modeled and the causal dependencies they encoded. An on-field evaluation involving 101 students was performed to assess the most appropriate structure of the INQPRO’s student model. To ensure fairness in model comparison, the same Dynamic Bayesian Network (DBN) construction approach was employed. Lastly, this paper highlights the properties of the student model that provide optimal results for modeling scientific inquiry skill acquisition in INQPRO.     

Electrical resistivity and corrosion potential of reinforced concrete: influencing factors and prediction models

Abstract

This paper presents two models: one for predicting the electrical resistivity of concrete and the other for the corrosion potential of reinforcing steel. The prediction models were developed based on experimental results, considering various influencing factors. The experiment approach included the concrete mix proportion, chloride content, concrete cover thickness and time of exposure as the parameters. The results revealed that fly ash concrete showed significantly high electrical resistivity even in the presence of chloride ions. The effects of fly ash became more significant when the water to binder ratio was lower. Chloride ions also decreased the corrosion potential of steel in both the OPC and fly ash concrete. The corrosion potential was found less negative for fly ash concrete due to higher electrical resistivity. The prediction results show good agreement with the experimental results of this study and some other researchers.     

Fabrication of transparent lead-free KNN glass ceramics by incorporation method

The incorporation method was employed to produce potassium sodium niobate [KNN] (K_0.5Na_0.5NbO₃) glass ceramics from the KNN-SiO₂ system. This incorporation method combines a simple mixed-oxide technique for producing KNN powder and a conventional melt-quenching technique to form the resulting glass. KNN was calcined at 800°C and subsequently mixed with SiO₂ in the KNN:SiO₂ ratio of 75:25 (mol%). The successfully produced optically transparent glass was then subjected to a heat treatment schedule at temperatures ranging from 525°C -575°C for crystallization. All glass ceramics of more than 40% transmittance crystallized into KNN nanocrystals that were rectangular in shape and dispersed well throughout the glass matrix. The crystal size and crystallinity were found to increase with increasing heat treatment temperature, which in turn plays an important role in controlling the properties of the glass ceramics, including physical, optical, and dielectric properties. The transparency of the glass samples decreased with increasing crystal size. The maximum room temperature dielectric constant (ε _r ) was as high as 474 at 10 kHz with an acceptable low loss (tanδ) around 0.02 at 10 kHz.     

Run-Time Checking of Dynamic Properties

We consider a first-order property specification language for run-time monitoring of dynamic systems. The language is based on a linear-time temporal logic and offers two kinds of quantifiers to bind free variables in a formula. One kind contains the usual first-order quantifiers that provide for replication of properties for dynamically created and destroyed objects in the system. The other kind, called attribute quantifiers, is used to check dynamically changing values within the same object. We show that expressions in this language can be efficiently checked over an execution trace of a system.     

Effect of A-site and B-site ion replaced with small ions on the intermediate phase in PbZrO3 ceramic

The solid solution of (1 − x)PbZrO₃–xNaNbO₃ ceramics, where x = 0.0–0.08, was synthesized by solid state reaction. The basic characterizations were performed using X-ray diffraction (XRD), dielectric spectroscopy, hysteresis measurement and differential scanning calorimetry (DSC) techniques. The results indicated that the crystal structure of the solid solution, (1 − x)PZ–xNN, where x = 0.00–0.08, is of orthorhombic symmetry. It was found that the effect of NN being replaced with small ions at the A-site and B-site can induce an AFE-like phase in PZ. The FE intermediate phase of PZ cannot be induced, although Zr⁴⁺ ions were substituted by small Nb⁵⁺ ions. This is due to the decreasing average rate of radii in the A-site (0.1 Å mol⁻¹) being higher than that in the B-site (0.08 Å mol⁻¹).     

Factors influencing the performance of Dynamic Decision Network for INQPRO

There has been an increasing interest in employing decision-theoretic framework for learner modeling and provision of pedagogical support in Intelligent Tutoring Systems (ITSs). Much of the existing learner modeling research work focuses on identifying appropriate learner properties. Little attention, however, has been given to leverage Dynamic Decision Network (DDN) as a dynamic learner model to reason and intervene across time. Employing a DDN-based learner model in a scientific inquiry learning environment, however, remains at infant stage because there are factors contributed to the performance the learner model. Three factors have been identified to influence the matching accuracy of INQPRO’s learner model. These factors are the structure of DDN model, the variable instantiation approach, and the weights assignment method for two consecutive Decision Networks (DNs). In this research work, a two-phase empirical study involving 107 learners and six domain experts was conducted to determine the optimal conditions for the INQPRO’s dynamic learner model. The empirical results suggested each time-slice of the INQPRO’s DDN should consist of a DN, and that DN should correspond to the Graphical User Interface (GUI) accessed. In light of evidence, observable variables should be instantiated to their observed states; leaving the remaining observable nodes uninstantiated. The empirical results also indicated that varying weights between two consecutive DNs could optimize the matching accuracy of INQPRO’s dynamic learner model.     

Monitoring, Checking, and Steering of Real-Time Systems

The MaC system has been developed to provide assurance that a target program is running correctly with respect to formal requirements specification. This is achieved by monitoring and checking the execution of the target program at run-time. MaC bridges the gap between formal verification, which ensures the correctness of a design rather than an implementation, and testing, which only partially validates an implementation. One weakness of the MaC system is that it can detect property violations but cannot provide any feedback to the running system. To remedy this weakness, the MaC system has been extended with a feedback capability. The resulting system is called MaCS (Monitoring and Checking with Steering). The feedback component uses the information collected during monitoring and checking to steer the application back to a safe state after an error occurs. We present a case study where MaCS is used in a control system that keeps an inverted pendulum upright. MaCS detects faults in controllers and performs dynamic reconfiguration of the control system using steering.     

Adhesion improvement in glass fiber reinforced polyethylene composite via admicellar polymerization

Admicellar polymerization (polymerization of monomer solubilized in adsorbed surfactant bilayers) has been used to form a thin film of polyethylene onto the surface of milled glass fibers using sodium dodecyl sulfate as the surfactant. The decrease in ethylene pressure was used to follow the solubilization and adsolubilization processes as well as the reaction processes. An increase in initiator (Na₂S₂O₈) to surfactant ratio gave thicker and more uniform coatings of polymer onto the glass fiber surface according to SEM micrographs. Although a substantial amount of ethylene polymerized in solution according to the pressure drop, the decrease in pressure attributed to admicelle polymerization corresponded to the amount of polymer formed on the glass fiber, indicating little, if any, solution polymer deposited on the fibers. The admicellar‐treated glass fiber was used to make composites with high‐density polyethylene. The composites showed an increase in tensile and flexural strength over composites made from as‐received glass fiber, indicating an improvement in the fiber‐matrix adhesion of the admicellar‐treated glass fiber.     

Prevalence of class 1 integrons in commensal Escherichia coli from pigs and pig farmers in Thailand

Escherichia coli isolates (n = 617) from fecal samples of healthy and diarrheal pigs, pig farmers, and nonfarmers were analyzed for class 1 integrons. Three hundred ninety-two isolates (63.5%) were positive for class 1 integrons, based on the presence of intI1, with seventy-one of those isolates (11.5%) harboring all three conserved genes (intI1, qacEdelta1, and sul1) known to be associated with class 1 integrons. The presence of integrons was associated with isolate origin. Integrons were more prevalent in isolates from most pig groups than in isolates from pig farmers and nonfarmers. Selected integron-positive and integron-negative isolates were tested for resistance to 16 antimicrobials. All integron-positive swine isolates were multidrug resistant to at least three antimicrobial agents, demonstrating resistance to 14 different antibiotics that included sulphamethoxazole (100%), tetracycline (97.1%), ampicillin (92.8%), streptomycin (89.9%), trimethoprim-sulphamethoxazole (88.1%), nalidixic acid (60.9%), chloramphenicol (58.0%), kanamycin (55.1%), cephalothin (44.9%), gentamicin (39.1%), ciprofloxacin (33.3%), cefoxitin (8.7%), amoxicillin-clavulanic acid (5.8%), and amikacin (2.9%). All isolates were susceptible to ceftiofur and ceftriaxone. Forty-seven resistance patterns were observed among 69 integron-positive swine and swine farmer isolates. The most frequent pattern was tetracycline-ciprofloxacin-gentamicin-nalidixic acid-sulphamethoxazole-trimethoprim-sulphamethoxazole-kanamycin-ampicillin-streptomycin (10.1%), which was found in diarrheal and healthy pigs. This study shows that integrons and multidrug-resistant commensal bacteria are common and appear to be a significant aspect of microbial communities associated with pigs and humans in southern Thailand.