Flow-Based Rules Generation for Intrusion Detection System using Machine Learning Approach

Rapid increase in internet users also brought new ways of privacy and security exploitation. Intrusion is one of such attacks in which an authorized user can access system resources and is major concern for cyber security community. Although AV and firewall companies work hard to cope with this kind of attacks and generate signatures for such exploits but still, they are lagging behind badly in this race. This research proposes an approach to ease the task of rules generationby making use of machine learning for this purpose. We used 17 network features to train a random forest classifier and this trained classifier is then translated into rules which can easily be integrated with most commonly used firewalls like snort and suricata etc. This work targets five kind of attacks: brute force, denial of service, HTTP DoS, infiltrate from inside and SSH brute force. Separate rules are generated for each kind of attack. As not every generated rule contributes toward detection that's why an evaluation mechanism is also used which selects the best rule on the basis of precision and f-measure values. Generated rules for some attacks have 100% precision with detection rate of more than 99% which represents effectiveness of this approach on traditional firewalls. As our proposed system translates trained classifier model into set of rules for firewalls so it is not only effective for rules generation but also give machine learning characteristics to traditional firewall to some extent.&nbsp

Object Oriented Model for Evaluation of On-Chip Networks

Abstract: The Network on Chip (NoC) paradigm is rapidly replacing bus based System on Chip (SoC) designs due to their inherent disadvantages such as non-scalability, saturation and congestion. Currently very few tools are available for the simulation and evaluation of on-chip architectures. This study proposes a generic object oriented model for performance evaluation of on-chip interconnect architectures and algorithms. The generic nature of the proposed model can help the researchers in evaluation of any kind of on-chip switching networks. The model was applied on 2D-Mesh and 2D-Diagonal-Mesh on-chip switching networks for verification and selection of best out of both the analyzed architectures. The results show the superiority of 2D-Diagonal-Mesh over 2D-Mesh in terms of average packet delay

Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals

Riverside landslides present a significant geohazard globally, posing threats to infrastructure and human lives. In line with the United Nations’ Sustainable Development Goals (SDGs), which aim to address global challenges, professionals in the field have developed diverse methodologies to analyze, assess, and predict the occurrence of landslides, including quantitative, qualitative, and semi-quantitative approaches. With the advent of computer programs, quantitative techniques have gained prominence, with computational intelligence and knowledge-based methods like artificial neural networks (ANNs) achieving remarkable success in landslide susceptibility assessments. This article offers a comprehensive review of the literature concerning the utilization of ANNs for landslide susceptibility assessment, focusing specifically on riverside areas, in alignment with the SDGs. Through a systematic search and analysis of various references, it has become evident that ANNs have emerged as the preferred method for these assessments, surpassing traditional approaches. The application of ANNs aligns with the SDGs, particularly Goal 11: Sustainable Cities and Communities, which emphasizes the importance of inclusive, safe, resilient, and sustainable urban environments. By effectively assessing riverside landslide susceptibility using ANNs, communities can better manage risks and enhance the resilience of cities and communities to geohazards. While the number of ANN-based studies in landslide susceptibility modeling has grown in recent years, the overarching objective remains consistent: researchers strive to develop more accurate and detailed procedures. By leveraging the power of ANNs and incorporating relevant SDGs, this survey focuses on the most commonly employed neural network methods for riverside landslide susceptibility mapping, contributing to the overall SDG agenda of promoting sustainable development, resilience, and disaster risk reduction. Through the integration of ANNs in riverside landslide susceptibility assessments, in line with the SDGs, this review aims to advance our knowledge and understanding of this field. By providing insights into the effectiveness of ANNs and their alignment with the SDGs, this research contributes to the development of improved risk management strategies, sustainable urban planning, and resilient communities in the face of riverside landslides

Effect of nicotinamide on the photolysis of riboflavin in aqueous solution

The photolysis of riboflavin (RF) in aqueous solution in the presence of nicotinamide (NA) by visible light has been studied in the pH range 1.0–12.0 and the various photoproducts have been identified as known compounds. RF has been determined in degraded solutions by a specific multicomponent spectrometric method in the presence of its photoproducts and N

Comparative Analysis for Slope Stability by Using Machine Learning Methods

Featured Application: The presented paper conducted a comparative analysis based on well-known MLP, SVM, DT, and RF learning methods to assess/predict the safety factor (F.S) of earthslopes. Earth slopes’ stability analysis is a key task in geotechnical engineering that provides a detailed view of the slope conditions used to implement appropriate stabilizations. In the stability analysis process, calculating the safety factor (F.S) plays an essential part in the stability assessment, which guarantees operations’ success. Providing accurate and reliable F.S can be used to improve the stability analysis procedure as well as stabilizations. In this regard, researchers used computational intelligent methodologies to reach highly accurate F.S calculations. The presented study focused on the F.S estimation process and attempted to provide a comparative analysis based on computational intelligence and machine learning methods. In this regard, the well-known multilayer perceptron (MLP), decision tree (DT), support vector machines (SVM), and random forest (RF) learning algorithms were used to predict/calculate F.S for the earth slopes. These machine learning classifiers have a strong capability predict the F.S under certain conditions for slope failures and uncertainties. These models were implemented on a dataset containing 100 earth slopes’ stabilities, recorded based on F.S from various locations in the provinces of Fars, Isfahan, and Tehran in Iran, which were randomly divided into the training and testing datasets. These predictive models were validated by Janbu’s limit equilibrium analysis method (LEM) and GeoStudio commercial software. Regarding the study’s results, MLP (accuracy = 0.901/precision = 0.90) provides more accurate results to predict the F.S than other classifiers, with good agreement with LEM results. The SVM algorithm follows MLP (accuracy = 0.873/precision = 0.85). Regarding the estimated loss function, MLP obtained a 0.29 average loss in the F.S prediction process, which is the lowest rate. The SVM, DT, and RF obtained 0.41, 0.62, and 0.45 losses, respectively. This article tried to fill the gap in traditional analysis procedures based on advanced procedures in slope stability assessments

Validation of a stability-indicating spectrometric method for the determination of sulfacetamide sodium in pure form and ophthalmic preparations

Introduction: Sulfacetamide sodium is a widely used sulfonamide for ophthalmic infections. Objective: A number of analytical methods have been reported for the analysis of sulfacetamide but they lack the ability to determine both the active drug and its major degradation product, sulfanilamide, simultaneously in a sample. Materials and Methods: In the present study a simple, rapid and economical stability-indicating UV spectrometric method has been validated for the simultaneous assay of sulfacetamide sodium and sulfanilamide in pure form and in ophthalmic preparations. Results: The method has been found to be accurate (recovery 100.03 ±0.589%) and precise (RSD 0.587%) with detectable and quantifiable limits of 1.67×10–6 M (0.04 mg%) and 5.07×10–6 M (0.13 mg%), respectively for the assay of pure sulfacetamide sodium. The method is also found to be accurate and precise to small changes in wavelength, pH and buffer concentration as well as to forced degradation. The study further includes the validation of the method for the assay of pure sulfanilamide in solution, which has been found to be accurate, precise and robust. Conclusion: The results indicate that the proposed two-component spectrometric method is stability-indicating and can be used for the simultaneous assay of both sulfacetamide sodium and sulfanilamide in synthetic mixtures and degraded solutions

A novel receiver design of nonorthogonal FDM systems in underwater acoustics communication

While orthogonal frequency-division multiplexing (OFDM) has been studied for a decade, the study of nonorthogonal frequency-division multiplexing (NOFDM) in the underwater acoustics (UWA) communication has never been reported, to the best of our knowledge. Therefore, we investigate the NOFDM technique for the UWA communication considering the doubly dispersive channel. The main feature of the NOFDM is that it involves more closely packed subcarriers compared to the OFDM, which results in higher spectral efficiency. However, this transmission suffers from severe intercarrier interference. Since, we are considering the doubly dispersive channel, intersymbol interference is also encountered due to the multipath propagation. Therefore, the traditional receiver of the NOFDM system in the UWA channel tends to have high computational complexity. Considering this problem, we design a receiver for the NOFDM system, where the basis expansion model is used along with the compressed sensing channel estimation technique, i.e., orthogonal matching pursuit (OMP), which can effectively reduce the computational complexity. However, while implementing this technique in the real sea environment, the problem of long delay/Doppler spread is encountered due to guard intervals. Therefore, we propose the use of a time-domain equalizer to mitigate the effect of long delay/Doppler spread. Simulation and experimental results based on the bit error rate demonstrate the performance degradation due to severe interference in our proposed receiver. On the contrary, the mean-square-error performance shows that our proposed receiver with the OMP channel estimation outperforms the OFDM receiver. Similarly, higher spectral efficiency gain is attained due to the closely packed subcarriers.Accepted versionThe work of S. Anwar and H. Sun was supported in part by the National Natural Science Foundation of China under Grant 61671394, in part by the Science and Technology Program of Shenzhen, China, under Grant JSGG20170414090428464, in part by Fundamental Research Funds for the Central Universities under Grant 20720170044, and in part by the National Key R&D program of China under Grant 2018YFC0809200

Photostability and Photostabilization of Drugs and Drug Products

Photostability studies of drugs and drug products are an integral part of the product development process in the pharmaceutical industry. These studies are carried out to ensure quality, efficacy, and safety of the formulated products during manufacture, storage, and use. This review deals with the concept of photostability and related aspects and the literature available in the field. It highlights the role of the photochemistry in the photostability studies, describes the functional groups important for the photoreactivity of drugs, explains photophysical processes, and deals with the kinetics of photochemical reactions. The various modes of photodegradation of drugs with examples of selected compounds are presented. The biological consequences of the effect of light on the drug degradation are described. The photostability testing of drugs and drug products and the requirements under ICH guideline are discussed. Some information on the packaging requirements for the formulated products is provided. The various methods used for the photostabilization of solid and liquid dosage forms are also discussed