Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Desindustrialização e comércio exterior: evidências recentes para o Brasil

De-industrialization and foreign trade: recent evidences to Brazil. This paper analyses the behavior of the Brazilian manufacturing sector during the 2000's. We test the hypothesis of early de-industrialization induced by foreign trade. Our results show a mixed picture: although we showed signs of early de-industrialization induced by foreign trade, at the same time, we found evidence that Brazilian entrepreneurs have reduced manufacturing exports during internal market boom between 2004 and 2010. This fact determines the deterioration of trade balance of the manufacturing sector and justifies worries on long-term perspectives for Brazilian economy.

Value co-creation in bio-based chemicals value network

The purpose of this Master’s thesis is to study value co-creation in emerging value network. The main objective is to examine how value is co-created in bio-based chemicals value network. The study provides insights to different actors’ perceived value in the value network and enlightens their motivations to commit to the collaborative partnerships with other actors. Empirical study shows that value co-creation is creation of mutual value for both parties of the relationship by combining their non-competing resources to achieve a common goal. Value co-creation happens in interactions, and trust, commitment and information sharing are essential prerequisites for value co-creation. Value co-creation is not only common value creation, but it is also value that emerges for each actor because of the co-operation with the other actor. Even though the case companies define value mainly in economic terms, the other value elements like value of the partnership, knowledge transfer and innovation are more important for value co-creation.

Modelling the colour difference by artificial neural network

This thesis work studies the modelling of the colour difference using artificial neural network. Multilayer percepton (MLP) network is proposed to model CIEDE2000 colour difference formula. MLP is applied to classify colour points in CIE xy chromaticity diagram. In this context, the evaluation was performed using Munsell colour data and MacAdam colour discrimination ellipses. Moreover, in CIE xy chromaticity diagram just noticeable differences (JND) of MacAdam ellipses centres are computed by CIEDE2000, to compare JND of CIEDE2000 and MacAdam ellipses. CIEDE2000 changes the orientation of blue areas in CIE xy chromaticity diagram toward neutral areas, but on the whole it does not totally agree with the MacAdam ellipses. The proposed MLP for both modelling CIEDE2000 and classifying colour points showed good accuracy and achieved acceptable results.

Distribution of Smart Solutions for External Lighting in Switzerland - Case C2 Smartlight Ltd.

Establishing of export operations is the key to the competitiveness for all producing companies in high-tech industry. Distribution partnerships between exporting producer and local distributors of relevant foreign market are utilized by SMEs to gain cost-efficiency of operation. The purpose of this study was to investigate the Swiss market of outdoor lighting solutions and propose distribution channels for the case of company C2 SmartLight Ltd. The literature framework consists of three main parts: description of distribution channels for business products, the selection process of the distributor and management of the distributors. The empirical part of this study composed of the observation of Swiss lighting market, highlighting key customers, trends of energy efficiency and key industry players of the lighting market. The aim was to identify potential distribution channels, which reach the target customer groups and identify the market opportunity. Secondly, the data was collected through semi-structured phone interviews. The company, which operates in outdoor lighting business and has an established distributor in Switzerland, was interviewed and used as a benchmark. As a result of this research the market opportunity for distribution of C2 SmartLight products was identified based on potential customers and market need. C2 SmartLight Ltd. should establish a connection with wholesalers that distribute easy to handle and store electrical equipment. The results of this study can be used by other SME companies, operating in a similar field of economy, for selection of distributors.

Controlling full penetration in MAG welding by the application of infrared thermography and neural network

In this study, an infrared thermography based sensor was studied with regard to usability and the accuracy of sensor data as a weld penetration signal in gas metal arc welding. The object of the study was to evaluate a specific sensor type which measures thermography from solidified weld surface. The purpose of the study was to provide expert data for developing a sensor system in adaptive metal active gas (MAG) welding. Welding experiments with considered process variables and recorded thermal profiles were saved to a database for further analysis. To perform the analysis within a reasonable amount of experiments, the process parameter variables were gradually altered by at least 10 %. Later, the effects of process variables on weld penetration and thermography itself were considered. SFS-EN ISO 5817 standard (2014) was applied for classifying the quality of the experiments. As a final step, a neural network was taught based on the experiments. The experiments show that the studied thermography sensor and the neural network can be used for controlling full penetration though they have minor limitations, which are presented in results and discussion. The results are consistent with previous studies and experiments found in the literature.

Logística y distribución física internacional: clave en las operaciones de comercio exterior

Bogotá Emprende

Observatorio de las exportaciones: Comercio exterior de la región Bogotá- Cundinamarca con Estados Unidos 2000-2004. N. 8. Agosto de 2005

Gestión del conocimiento

Observatorio de las exportaciones: Comercio exterior de la región Bogotá- Cundinamarca con Estados Unidos 2000-2004. No.8 Agosto de 2005

Gestión del conocimiento