Feature selection using mutual information in network intrusion detection system

University of Technology Sydney. Faculty of Engineering and Information Technology.Network technologies have made significant progress in development, while the security issues alongside these technologies have not been well addressed. Current research on network security mainly focuses on developing preventative measures, such as security policies and secure communication protocols. Meanwhile, attempts have been made to protect computer systems and networks against malicious behaviours by deploying Intrusion Detection Systems (IDSs). The collaboration of IDSs and preventative measures can provide a safe and secure communication environment. Intrusion detection systems are now an essential complement to security project infrastructure of most organisations. However, current IDSs suffer from three significant issues that severely restrict their utility and performance. These issues are: a large number of false alarms, very high volume of network traffic and the classification problem when the class labels are not available. In this thesis, these three issues are addressed and efficient intrusion detection systems are developed which are effective in detecting a wide variety of attacks and result in very few false alarms and low computational cost. The principal contribution is the efficient and effective use of mutual information, which offers a solid theoretical framework for quantifying the amount of information that two random variables share with each other. The goal of this thesis is to develop an IDS that is accurate in detecting attacks and fast enough to make real-time decisions. First, a nonlinear correlation coefficient-based similarity measure to help extract both linear and nonlinear correlations between network traffic records is used. This measure is based on mutual information. The extracted information is used to develop an IDS to detect malicious network behaviours. However, the current network traffic data, which consist of a great number of traffic patterns, create a serious challenge to IDSs. Therefore, to address this issue, two feature selection methods are proposed; filter-based feature selection and hybrid feature selection algorithms, added to our current IDS for supervised classification. These methods are used to select a subset of features from the original feature set and use the selected subset to build our IDS and enhance the detection performance. The filter-based feature selection algorithm, named Flexible Mutual Information Feature Selection (FMIFS), uses the theoretical analyses of mutual information as evaluation criteria to measure the relevance between the input features and the output classes. To eliminate the redundancy among selected features, FMIFS introduces a new criterion to estimate the redundancy of the current selected features with respect to the previously selected subset of features. The hybrid feature selection algorithm is a combination of filter and wrapper algorithms. The filter method searches for the best subset of features using mutual information as a measure of relevance between the input features and the output class. The wrapper method is used to further refine the selected subset from the previous phase and select the optimal subset of features that can produce better accuracy. In addition to the supervised feature selection methods, the research is extended to unsupervised feature selection methods, and an Extended Laplacian score EL and a Modified Laplacian score ML methods are proposed which can select features in unsupervised scenarios. More specifically, each of EL and ML consists of two main phases. In the first phase, the Laplacian score algorithm is applied to rank the features by evaluating the power of locality preservation for each feature in the initial data. In the second phase, a new redundancy penalization technique uses mutual information to remove the redundancy among the selected features. The final output of these algorithms is then used to build the detection model. The proposed IDSs are then tested on three publicly available datasets, the KDD Cup 99, NSL-KDD and Kyoto dataset. Experimental results confirm the effectiveness and feasibility of these proposed solutions in terms of detection accuracy, false alarm rate, computational complexity and the capability of utilising unlabelled data. The unsupervised feature selection methods have been further tested on five more well-known datasets from the UCI Machine Learning Repository. These newly added datasets are frequently used in literature to evaluate the performance of feature selection methods. Furthermore, these datasets have different sample sizes and various numbers of features, so they are a lot more challenging for comprehensively testing feature selection algorithms. The experimental results show that ML performs better than EL and four other state-of-art methods (including the Variance score algorithm and the Laplacian score algorithm) in terms of the classification accuracy

Omani School Students’ Attitudes toward Agriculture: Investigating the Role of Gender and Geographical Regions

A 24-item questionnaire was designed to collect specific data in order to determine Omani students’ attitudes toward agriculture and, specifically, whether or not these attitudes differ according to gender and the geographical regions where students reside. A survey research method based on the use of a questionnaire was employed. The questionnaire items were divided into four domains: participants’ general knowledge about agriculture, their personal interest about agriculture, the role of government in supporting agriculture, and the role of agriculture in food security. The questionnaire was administered to 394 randomly selected Grade 10 students. Participants consisted of 189 male students and 205 female students in total. Questionnaires were distributed to 130 students from North Al Batinah Governorate, 142 from Al-Dakhiliyah Governorate, and 122 from Muscat Governorate. Questionnaire reliability was calculated using Cronbach’s alpha, an internal consistency method, which resulted in a value of 0.83 for the instrument. The study was conducted in the 2016/2017 academic year. The findings indicated that students’ attitudes toward agriculture, overall, were positive and both gender and geographical region had an effect upon their attitudes. The results of this study demonstrate a need to recommend improving students’ attitudes towards agriculture, especially for students who reside in the Muscat Governorate; for example, schools should be encouraged to include agricultural collaborative learning activities, both inside and outside the classroom. Overall, the study results suggest a benefit in conducting additional research in the area of agriculture education in Oman

Calogero model with Yukawa like interaction

We study an extension of one dimensional Calogero model involving strongly coupled and electrically charged particles. Besides Calogero term $\frac{g}{2x^{2}}$, there is an extra factor described by a Yukawa like coupling modeling short distance interactions. Mimicking Calogero analysis and using developments in formal series of the wave function $\Psi (x)$ factorised as $x^{\epsilon}\Phi (x)$ with $\epsilon (\epsilon -1) =g$, we develop a technique to approach the spectrum of the generalized system and show that information on full spectrum is captured by $\Phi (x)$ and $\Phi ^{\prime \prime}(x)$ at the singular point $x=0$ of the potential. Convergence of $\int dx| \Psi (x) | ^{2}$ requires $\epsilon >-{1/2}$ and is shown to be sensitive to the zero mode of $\Phi (x)$ at $x=0$. \textbf{Key words}: \textit{Hamitonian systems, quantum integrability, Calogero model, Yukawa like potential.}Comment: 12 pages, 1 figur

Implementation of a strategic group map and balanced scorecard in a university setting

A balanced scorecard reports those performance indicators that have been derived from the institution’s mission and stakeholder analysis and are aligned across all functions in the organisation. A comprehensive review of the literature reveals that most universities do not use a strategic map to show the causal linkages among the performance indicators. Drawing from the emerging performance excellence paradigm and research in strategic management, a strategic group map and implementation model are proposed from which a balanced scorecard is developed for higher education. Based on the application of these tools, the university achieved its first significant improvement in strategic indicators in over five years. © 2010 Inderscience Enterprises Ltd

MPPT control design for variable speed wind turbine

Variable speed wind turbine systems (VSWT’s) have been in receipt of extensive attention among the various renewable energy systems. The present paper focuses on fuzzy fractional order proportional-integral (FFOPI) control segment for variable speed wind turbine (VSWT) directly driving permanent magnet synchronous generator (PMSG). The main objective of this study is to reach maximum power point tracking (MPPT) through combination of advanced control based on FFOPI control applied to generator side converter (turbine and PMSG). The basic idea of the FFOPI controller is to implement a fuzzy logic controller (FLC) in cascade with Fractional Order Proportional Integral controller (FOPI). A comparative study with FOPI and classical PI control schemes is made. The traditional PI controller cannot deliver a sufficiently great performance for the VSWT. However, the results found that the proposed approach (FFOPI) is more effective and feasible for controlling the permanent magnet synchronous generator to mantain maximum power extraction. The validation of results has been performed through simulation using Matlab/Simulink®

A new approach in cryptographic systems using fractal image coding

Problem statement: With the rapid development in the communications and information transmissions there is a growing demand for new approaches that increase the security of cryptographic systems. Approach: Therefore some emerging theories, such as fractals, can be adopted to provide a contribution toward this goal. In this study we proposed a new cryptographic system utilizing fractal theories; this approach exploited the main feature of fractals generated by IFS techniques. Results: Double enciphering and double deciphering methods performed to enhance the security of the system. The encrypted date represented the attractor generated by the IFS transformation, collage theorem was used to find the IFSM for decrypting data. Conclusion/Recommendations: The proposed method gave the possibility to hide maximum amount of data in an image that represent the attractor of the IFS without degrading its quality and to make the hidden data robust enough to withstand known cryptographic attacks and image processing techniques which did not change the appearance of image

Biometric identification using local iterated function

Biometric identification protocol has been received an increasing interest recently. It is a process that determines person identity by making use of their biometric features. A new biometric identification method is presented in this paper based on partial self-similarity that used to identify features within fingerprint images. This approach is already used in Fractal Image Compression (FIC) due to their ability to represent the images by a limited number of affine transformations, and its variation of scale, translation or rotation. These features give the recognition process high impact and good performance. To process data in a fingerprint image, it first converted into digital format using Optical Fingerprint Reader (OFR). The verification process is done by comparing these data with the server data. The system analysis shows that the proposed method is efficient in terms of memory and time complexity

A new public key cryptosystem based on IFS

Most public key encryption methods suffers from the inability to prove the difficulty of the algorithms, which summarizes under the category of mathematical problems that have inverses which are believed (but not proven) to be hard. The length and strength of the Cryptography keys are considered an important mechanism. The keys used for encryption and decryption must be strong enough to produce strong encryption. Fractals and chaotic systems have properties which have been extensively studied over the years, and derive their inherent complexity from the extreme sensitivity of the system to the initial conditions. In this paper a new cryptographic system based on Iterated Function Systems ( IFS) have been proposed to reduce the computation cost and increase the security for the public-key cryptography protocols. In the proposed public-key encryption algorithm, generate iterated function systems as a global public element, then its Hutchinson operator is used as a public key. To encrypt the plaintext with the receiver's public key we use one of the key agreement protocols to generate a shared private key that used to find the attractor of the IFS. The chaotic nature of the fractal functions ensures the security of the proposed public-key cryptosystem scheme

Covid-19 and movement control order: stress and coping strategies of students observing self-quarantine

Coronavirus Disease 2019 (COVID-19) led students feel anxious with a constant internal dialogue of ‘Am I safe?’ that may take a serious toll on their psyche. The self-quarantine and physical distancing, economic hardship and fears of contracting the disease are likely sources of stress. Quite apart, students may also experience both physical sufferings and mental stress due to the news of increasing number of infected cases and reported deaths across the globe. A range of expert guidelines have been developed by governments and health authorities to curtail the spread of the virus. This study models a position paper which persuades the reader to realize that the opinions expressed are valid and could be defended. In gathering supporting evidence, an online qualitative survey was conducted to examine the stress of students observing selfquarantine and physical distancing in and around Desa Ilmu and Unigardern in Kota Saramarahan as well as in apartments at Jalan Kingfisher Sabah. These students were invited as respondents in this online investigation using interview protocol to take their responses. This study is among the first to examine the stress and coping strategies of students observing self-quarantine and physical distancing. The paper may provide useful information about how students cope in stressful situations and also identify effective ways to manage people during distress times in future

A new idea in zero knowledge protocols based on iterated function systems

A secure method of identification is crucial to avoid computer deception dynamics. This could be attained by using zero-knowledge protocols. Zero-knowledge protocols are cryptographic protocols that have been proven to provide secure entity authentication without revealing any knowledge to any entity or to any eavesdropper and used to build effective communication tools and ensure their privacy. Many schemes have been proposed since 1984. Among them are those that rely on factoring and discrete log which are practical schemes based on NP- hard problems. Our aim is to provide techniques and tools which may be useful towards developing those systems. Fractal code was proven as a NP-hard problem, which means it cannot be solved in a practical amount of time. In this paper a new zero-knowledge scheme is proposed based on iterated function systems and the fractal features are used to improve this system. The proposed scheme is a generalization of the Guillou-Quisquater identification scheme. The two schemes are implemented and compared to prove their efficiency and security. From the implementation results, we conclude that zero knowledge systems based on IFS transformation perform more efficiently than GQ system in terms of key size and key space