Computer Science
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Item A FRAMEWORK FOR DEPLOYMENT OF MOBILE AGENTS AS WINDOWS OPERATING SYSTEM SERVICE FOR INFORMATION RETRIEVAL IN DISTRIBUTED ENVIRONMENTS(2013-12) OYATOKUN, BOSEDE OYENIKEMobile Agent Technology (MAT), remote method invocation and remote procedure calling are the three most widely used techniques for information storage and retrieval in network environments. Previous studies have shown that MAT provides a more efficient and dynamic approach to information storage and retrieval than others. However, for mobile agents to effectively perform their various tasks, a static agent platform must be installed on the computers. These platforms consume more memory, increase access time and prevent other tasks from running on the computer. Therefore, an alternative framework that will eliminate the problems associated with agent platform is imperative. Consequently, this work was aimed at developing a more efficient framework for mobile agent system deployment as an operating system service. Two classes of existing information retrieval agents were adapted to develop Embedded Mobile Agent (EMA) system. The EMA was embedded into the Windows Operating System (OS) kernel, so that it could run as a service for information retrieval. This was done to eliminate the overheads associated with the middleware provided by agent platforms. The targeted OS were Windows XP, Windows Vista and Windows7. Mathematical models were simulated to assess the performance of EMA by measuring service delay, memory utilisation, fault tolerance, turn around time at fixed bandwidth with varying number of network nodes, and percentage denial of service. Denied services were generated by a random number generator modelled after the Bernoulli Random Variable with 0.1 probability of failure. The model‟s performance was then compared with Java Agent DEvelopment framework (JADE), a widely used open-source existing mobile agent system running on platforms. The implementation was done using four computer systems running the targeted Windows on an existing local area network. Analysis of data was done using descriptive statistics and independent t-test at p = 0.01. The EMA model effectively retrieved information from the network without the agent platform, thereby reducing access times and saving memory, regardless of the version of the Windows OS. The mean service delay for EMA (15067.5 ± 8489.6 ms) was lower than that of JADE (15697.0 ± 8844.5 ms). The embedded agent requires 3 KB of UNIVERSITY OF IBADAN LIBRARY xv memory to run compared to JADE platform requiring 2.83 103 KB. The mean fault tolerance in terms of fault recovery time for EMA was approximately 50% that of JADE (327.8 ± 193.1 ms). The mean turn around time for EMA was 499.7 ± 173.0 ms and JADE was 843.3 ± 321.6 ms consequential to the time JADE spent activating platforms. The mean percentage denial of service for EMA was 14.3 ± 9.8 while JADE was 24.7 ± 18.5. Memory requirements and service delay increased with increasing number of nodes while others show no systematic change. For all the parameters tested, there were significant differences between the two schemes. The embedded mobile agent provided more efficient, dynamic and flexible solution compared to Java Agent DEvelopment framework for distributed information retrieval applications. It could be incorporated into new versions of operating systems as operating system service for universal distributed information retrieval. Keywords: Mobile agent technology, Embedded mobile agent, Operating system service, Java agent development framework. Word count: 497Item MODELLING AND MITIGATING MINOR-THREATS IN NETWORK THREAT MANAGEMENT(2015-07) ORIOLA, OLUWAFEMINetwork Threat Management (NTM) is used to model and mitigate network threats classified as major-threats and minor-threats without exceeding Cost of Detection (CD), Time of Detection (TD) and False Positive Rate (FPR) limits. Existing network threat modelling and mitigation frameworks focused on major-threats because until recently, only major-threats are usually harmful, while minor-threats were perceived non-harmful. Recent studies however have shown that some minor-threats are harmful. This study was designed to model and mitigate minor-threats in NTM. The Threat Prediction Model (TPDM) and Threat Prioritisation Model (TPRM) were used for modelling while Threat Mitigation Model (TMTM) was used for mitigation. The TPDM was modified to identify minor-threats by incorporating actionable attributes. The modified TPDM accuracy was compared with TPDM based on confidence, with 1.0 benchmark. The TPRM was modified to rate minor-threats using Dempster-Shafer Method and compared with snort-classifier and Common Vulnerability Scoring System (CVSS) as standards. The rating range between 0 and 5 was ‗less harmful‘ while rating above 5 was ‗moderately harmful‘. The modified TPDM and TPRM were implemented using java. The TMTM was modified using Hillson‘s risk mitigation model. The CD based on number of rules, TD and FPR were used to compare modified TMTM and TMTM for snort and suricata implementations. Real life minor-threats known as Plymouth University Advanced Persistent Threats (PUAPT) were developed using metasploit for analysis. Existing Lincoln Lab Denial of Service (LLDOS) minor-threats were also analysed for standardisation. The CD, TD and FPR limits for PUAPT analysis were set at 5_rules, 60_seconds and 25% respectively while LLDOS were 5_rules, 90_seconds and 25%. Data were analysed using descriptive statistics. In PUAPT analysis, modified TPDM was accurate with confidence of 1.0 compared to 0.0 of existing TPDM. The modified TPRM rated harmful minor-threats as moderately harmful while non-harmful as less harmful. The snort-classifier rated both harmful and non-harmful minor-threats as less harmful while CVSS rated none of the minor-threats. With modified TMTM for snort implementation, CD, TD and FPR of UNIVERSITY OF IBADAN LIBRARY xvii 5_rules, 1_second and 2.7% respectively were incurred compared to 19082_rules, 240_seconds and 99.1% of existing TMTM. With modified TMTM for suricata implementation, CD, TD and FPR of 5_rules, 1_second and 1.2% respectively were incurred compared to 18701_rules, 240_seconds and 99.8% of existing TMTM. The modified TPDM for LLDOS was accurate with confidence of 1.0 compared to 0.1 of existing TPDM. The modified TPRM rated harmful minor-threats as moderately harmful while non-harmful as less harmful, snort-classifier rated both harmful and non-harmful minor-threats as less harmful and CVSS rated only minor-threats with vulnerabilities. With modified TMTM for snort implementation, CD, TD and FPR of 5_rules, 3_seconds and 21.1% respectively were incurred compared to 19082_rules, 480_seconds and 99.9% of existing TMTM. With modified TMTM for suricata implementation, CD, TD and FPR of 5_rules, 75_seconds and 1.3% respectively were incurred compared to 18701_rules, 480_seconds and 99.0% of existing TMTM. The modified models accurately modelled and mitigated minor-threats without exceeding cost of detection, time of detection and false positive rate limits. The modified models are recommended for modelling and mitigating minor-threats in network threat management. Keywords: Network threat management, Minor-threat, Threat modelling, Threat mitigation. Word count: 500Item DEVELOPMENT OF ADVANCED DATA SAMPLING SCHEMES TO ALLEVIATE CLASS IMBALANCE PROBLEM IN DATA MINING CLASSIFICATION ALGORITHMS(2015-09) FOLORUNSO, SAKINAT OLUWABUKONLAClassification is the process of finding a set of models that distinguish data classes to predict unknown class label in data mining. The class imbalance problem occurs when standard classifiers are majority-biased while the minority class is ignored. Existing classifiers tend to maximise overall prediction accuracy and minimise error at the expense of the minority class. However, research had shown that misclassification cost of the minority class is higher and should not be ignored since it is the class of interest. This work was therefore designed to develop advanced data sampling schemes that improve the classification performance of imbalance datasets with the view of increasing the recall of the minority class. Synthetic Minority Oversampling Technique (SMOTE) was extended to SMOTE+300% and combined with existing under-sampling schemes: Random Under-Sampling (RUS), Neighbourhood Cleaning Rule (NCL), Wilson’s Edited Nearest Neighbour (ENN) and Condense Nearest Neighbour (CNN). Five advanced data sampling scheme algorithms: SMOTE300ENN, SMOTE300RUS, SMOTE300NCL, SMOTENCL and SMOTERUS were coded using JAVA and implemented in WEKA, a data mining tool as an Application Programming Interface. The existing and developed schemes were applied to 886 Diabetes Mellitus (DM), 1,163 Senior Secondary School Certificate Result (SSSCR) and 786 Contraceptive Methods (CM) datasets. The datasets were collected in Ilesha and Ibadan, Nigeria. Their performances were determined with different classification algorithms using Receiver Operating Characteristics (ROC), recall of the minority class and performance gain metrics. Friedman’s Test at p = 0.05 was used to analyse these schemes against the classification algorithms. The ROC metric revealed that the mean rank values for DM, SSSCR and CM datasets treated with the advanced schemes ranged from 6.9-13.8, 3.8-12.8 and 6.6-13.5, respectively when compared with the existing schemes which ranged from 3.4-7.8, 2.6-12.6 and 2.8-7.9, respectively. These results signifies improved classification performance. The Recall metric analysis for the DM, SSSCR and CM datasets in the advanced schemes ranged from 9.4-13.0, 6.3-14.0 and 7.3-13.6, respectively when compared with the existing schemes 2.0-7.5, 2.5-8.9 and 2.1-7.4, respectively. These results show increased detection of the minority class. Performance gains by the advanced UNIVERSITY OF IBADAN LIBRARY vii schemes over the original dataset (DM, SSCE and CM) were: SMOTE300ENN (27.1%), SMOTE300RUS (11.6%), SMOTE300NCL (15.5%), SMOTENCL (8.3%) and SMOTERUS (7.3%). Significant difference was observed amongst all the schemes. The higher the mean rank value and performance gain, the better the scheme. The SMOTE300ENN scheme gave the highest ROC and recall values in the three datasets which were 13.8, 12.8, 12.3 and 13.0, 14.0, 13.6, respectively. The developed Synthetic Minority Oversampling Technique 300 Wilson’s Edited Nearest Neighbour scheme significantly improved classification performance and increased the recall of the minority class over the existing schemes using the same dataset. It is therefore recommended for classification of imbalanced datasets. Keywords: Imbalanced dataset, Receiver operating characteristics, Data reduction techniques, Data reduction techniques Word count: 445Item FORMALISING THE LOGIC OF SPATIAL QUALIFICATION USING A QUALITATIVE REASONING APPROACH(2014-04) BASSEY, PATIENCE CHARLESSpatial qualification problem, an aspect of spatial reasoning, is concerned with the impossibility of knowing an agent‟s presence at a specific location and time. An agent‟s location determines its ability to carry out an action given its known spatial antecedents. There are sparse works on the formalisation of this problem. Qualitative reasoning approach is the most widely used approach for spatial reasoning due to its ability to reason with incomplete knowledge or reduced data set. This approach has been applied to spatial concepts, such as, shapes, sizes, distance and orientation but not spatial qualification. Therefore, this work was aimed at formalising a logical theory for reasoning about the spatial qualification of an agent to carry out an action based on prior knowledge using qualitative reasoning approach. The notions of persistence, discretisation and commutative distance coverage were used as parameters in formalising the concept of spatial qualification. The axioms and derivation rules for the theory were formally represented using quantified modal logic. The formalised theory was compared with standardised systems of axioms: S4 (containing Kripke‟s minimal system K, axioms T and 4) and S5 (containing K,T,4 and axiom B). The characteristics of the domain of the formalised theory were compared with Barcan‟s axioms, and its semantics were described using Kripke‟s Possible World Semantics (PWS) with constant domain across worlds. A proof system for reasoning with the formalised theory was developed using analytical tableau method. The theory was applied to an agent‟s local distribution planning task with set deadline. Cases with known departure time and routes were considered to determine the possibility of an agent‟s presence at a location. From the formalisation, a body of axioms named Spatial Qualification Model (SQM) was obtained. The axioms showed the presence log and reachability of locations as determinants for agent‟s spatial presence. The properties exhibited by the formalised UNIVERSITY OF IBADAN LIBRARY xvii model when examined in light of S4 and S5 systems of axioms were KP1, KP2 (equivalent to axiom K), TP and 4P (equivalent to axioms T and 4 respectively) in an S4 system. The SQM therefore demonstrated the characteristics of an S4 system of axioms but fell short of being an S5 system. Barcan‟s axiom held, confirming constant domain across possible worlds in the formalised model. Explicating the axioms in the SQM using PWS enabled the understanding of tableau proof rules. Through closed tableaux, the SQM was demonstrably semi-decidable in the sense that the possibility of an agent‟s presence at a certain location and time was only provable in the affirmative, while its negation was not. Depending on the route, the application of SQM to the product distribution planning domain resulted in agent‟s feasible availability times, within or outside the set deadline to assess the agent‟s spatial qualification in agreement with possible cases in the planning task. The spatial qualification model specified the spatial presence log and reachability axioms required for reasoning about an agent‟s spatial presence. The model successfully assessed plans of product distribution task from one location to the other for vans‟ availability. Keywords: Spatial qualification model, Quantified modal logic, Tableau proof, Possible world semantics. Word count: 497Item IMPROVING VISION IMPAIRED USERS ACCESS TO ELECTRONIC RESOURCES IN E-LEARNING ENVIRONMENT WITH MODIFIED ARTIFICIAL NEURAL NETWORK(2017-05) FASOLA, O. O.Assistive Technology (ATs) provide means through which persons with visual impairment are empowered with adaptive devices and methods for accessing multimedia information. However, the degree of sensitivity and specificity values for access to electronic resources by visual impaired persons varies. Existing ATs were designed as “one model fits all” (static calibration requirements), thereby limiting the usability by vision impaired users in an e-learning environment. The study presents a Dynamic Thresholding Model (DTM) that adaptively adjusts the vision parameters to meet the calibration requirements of vision impaired users. Data from International Statistical Classification of Diseases and Related Health Problems of World Health Organisation (WHO) containing 1001 instances of visual impairment measures were obtained from 2008 to 2013. The users’ vision parameters of WHO for Visual Acuity Range (VAR) were adopted. These were: VAR ≥ 0.3(299); 0.1 < VAR < 0.3(182); 0.07 ≤ VAR < 0.1(364); 0.05 ≤ VAR < 0.07(120); 0.02 ≤ VAR < 0.05(24); and VAR < 0.02(12). Data for six VAR groups were partitioned into 70% (700) and 30% (301) for training and testing, respectively. Data for the six groups were transformed into 3-bits encoding to facilitate model derivation. The DTM was developed with calibrator parameters (Visual Acuity (Va), Print Size (Ps) and Reading Rate (Rr)) for low acuity, adaptive vision calibrator and dynamic thresholding. The VAR from the developed DTM was used to predict the optimal operating range and accuracy value on observed WHO dataset irrespective of the grouping. Six-epochs were conducted for each thresholding value to determine the sensitivity and specificity values relative to the False Negative Rate (FNR) and False Positive Rate (FPR), respectively, which are evidences of misclassification. The 3-bit encoding coupled with the DTM yielded optimised equations of the form: .1718.172436.14985.834508.07474.19383.128042.5730703.5976073.4631RrPsVaOPRrVaPsOPRrVaPsOP Where OP1, OP2 and OP3 represent the first, second and third bit, respectively. Five local maxima accuracy and one global maximum threshold values were obtained from the DTM. Local maxima threshold values were 0.455, 0.470, 0.515, 0.530, and 0.580, with corresponding percentage accuracy of 99.257, 99.343, 99.171, 99.229, and 99.429. Global maximum accuracy was 99.6 at threshold value of 0.5. The Va, Ps, and Rr produced equal numbers of observations (301) agreeing with the result in WHO report. Correctly classified user impairment was 99.89%, with error rate of 0.11%. The model predicted sensitivity value of 99.79% (0.21 FNR), and specificity value of 99.52% (0.48 FPR). The developed dynamic thresholding model adaptively classified various degrees of visual impairment for vision impaired users.Item CONCEPTUAL KNOWLEDGE MODEL FOR IMPROVING TERM SIMILARITY IN RETRIEVAL OF WEB DOCUMENTS(2016-05) ABDULLAH, K. A.Terms Similarity (TS) in retrieval systems are based on lexical matching, which determines if query terms are useful and reflect the users’ information need in related domains. Existing works on TS use Term Frequency-Inverse Document Frequency (TF-IDF) to determine the occurrence of terms in web documents (snippets) is incapable of capturing the problem of semantic language mismatch. This study was designed to develop a conceptual knowledge model to solve the problem of TS in web documents retrieval by amplifying structured semantic network in Multiple Document Sources (MDSs) to reduce mismatch in retrieval results. Four hundred and forty-two IS-A hierarchy concepts were extracted from Internet using a web ontology language. These hierarchies were structured in MDSs to determine similarities. The concepts were used to formulate queries with the addition of terms from knowledge domain. Suffix Tree Clustering (STC) was adapted to cluster, structure the web and reduce dimensionality of features. The IS-A hierarchy concept on parent and child relationship was incorporated into the STC to select the best cluster, consisting of 100 snippets, four web page counts and WordNet as MDSs. Similarity was estimated on Cosine, Euclidean and Radial Basis Function (RBF) on the TF-IDF. Based on STC, TF-IDF was modified to develop Concept Weighting (CW) estimation on snippets and web page count. Similarity was estimated between TF-IDF and developed Concept Weighting; Cosine and CW-Cosine, Euclidean and CW-Euclidean and RBF and CW-RBF. Semantic network (WordNetSimilarity) LIn’ measure was extended with PAth length of the taxonomy concept to develop LIPA. The LIPA was compared with other WordNetSimilarity distance measures: Jiang and Conrath (JCN) and Wu and Palmer (WUP) as well as LIn and PAth length separately. Concept Weighting and WordNetSimilarity scores were combined using machine learning techniques to leverage a robust semantic similarity score and accuracy measure using Mean Absolute Error (MAE). The RBF and CW-RBF generated inconsistent values (0.9 for null and zero snippets. Similarity estimation obtained on Cosine, Euclidean, CW-Cosine and CW-Euclidean were 0.881, 0.446, 0.950 and 0.964, respectively. The retrieved snippets removed irrelevant features and enhanced precisions. WordNetSimilarity JCN, WUP, LIn, PAth, and LIPA values were 0.868, 0.953, 0.995, 0.955 and 0.998, respectively. The WordNetSimilarity improved the semantic similarity of concepts. The Concept Weighting and WordNetSimilarity; CW-Cosine, CW-Euclidean, JCN, WUP, LIn, PAth, and LIPA were combined to generate similarity coefficient scores 0.941, 0.944, 0.661, 0.928, 0.996, 0.924 and 0.998, respectively. The MAE on Cosine, Euclidean, CW-Cosine and CW Euclidean were 0.058, 0.011, 0.014 and 0.009, respectively while for JCN, WUP, LIn, PAth, and LIPA were 0.022, 0.004, 0.022, 0.019 and 0.020, respectively. The accuracy of the combined similarity for JCN, WUP, LIn, PAth, CW-Cosine, CW-Euclidean and LIPA were 0.023, 0.050, 0.008, 0.011, 0.024, 0.015 and 0.009, respectively. The developed conceptual knowledge model improved retrieval of web documents with structured multiple document sources. This improved precision of information retrieval system and solved the problem of semantic language mismatch with robust similarity between the terms.