Mohammad Zahidur Rahman

COMBINING NAIVE BAYES AND DECISION TREE FOR ADAPTIVE INTRUSION DETECTION

In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.

Anomaly Network Intrusion Detection Based on Improved Self Adaptive Bayesian Algorithm

Recently, research on intrusion detection in computer systems has received much attention to the computational intelligence society. Many intelligence learning algorithms applied to the huge volume of complex and dynamic dataset for the construction of efficient intrusion detection systems (IDSs). Despite of many advances that have been achieved in existing IDSs, there are still some difficulties, such as correct classification of large intrusion detection dataset, unbalanced detection accuracy in the high speed network traffic, and reduce false positives. This paper presents a new approach to the alert classification to reduce false positives in intrusion detection using improved self adaptive Bayesian algorithm (ISABA). The proposed approach applied to the security domain of anomaly based network intrusion detection, which correctly classifies different types of attacks of KDD99 benchmark dataset with high classification rates in short response time and reduce false positives using limited computational resources.

Adaptive Intrusion Detection based on Boosting and Naïve Bayesian Classifier

In this paper, we introduce a new learning algorithm for adaptive intrusion detection using boosting and naive Bayesian classifier, which considers a series of classifiers and combines the votes of each individual classifier for classifying an unknown or known example. The proposed algorithm generates the probability set for each round using naive Bayesian classifier and updates the weights of training examples based on the misclassification error rate that produced by the training examples in each round. This algorithm addresses the problem of classifying the large intrusion detection dataset, which improves the detection rates (DR) and reduces the false positives (FP) at acceptable level in intrusion detection. We tested the performance of the proposed algorithm with existing data mining algorithms by employing on the KDD99 benchmark intrusion detection dataset, and the experimental results proved that the proposed algorithm achieved high detection rates and significantly reduced the number of false positives for different types of network intrusions.

Learning intrusion detection based on adaptive bayesian algorithm

Recent intrusion detection have emerged an important technique for information security systems. Its very important that the security mechanisms for an information system should be designed to prevent unauthorized access of system resources and data. Last few years, many intelligent learning techniques of machine learning applied to the large volumes of complex and dynamic audit data for the construction of efficient intrusion detection systems (IDS). This paper presents, theoretical overview of intrusion detection and a new approach for intrusion detection based on adaptive Bayesian algorithm. This algorithm correctly classify different types of attack of KDD99 benchmark intrusion detection dataset with high detection accuracy in short response time. The experimental result also shows that, this algorithm maximize the detection rate (DR) and minimized the false positive rate (FPR) for intrusion detection.

Development of a Telemedicine model with low cost portable tool kit for remote diagnosis of rural people in Bangladesh

In this paper we have developed a Telemedicine model with portable tool kit for remote patients to collect vital signs of patients which are used for Telemedicine services. This developed system is low cost, portable, and easily maintainable and can be integrated with any complex health system. We have used the GNU health where local doctors can communicate with a low cost terminal. Expert doctors can also take part through this terminal and deliver treatment to the patients. The patients medical history is stored in GNU health database and accessed from the remote terminal. We have successfully designed the system and collected the patients data. Through our developed android apps, the data will be stored in the staging server. From the staging server, any health system can collect the data and give the services to the rural people. Finally we can conclude that, Telemedicine service can be given effectively by using our portable tool kit in a cost effective manner which improves the quality and accessibility especially in rural areas.

Mining Complex Network Data for Adaptive Intrusion Detection

Intrusion detection is the method of identifying intrusions or misuses in a computer network, which compromise the confidentiality and integrity of the network. Intrusion Detection System (IDS) is a security tool used to monitor network traffic and detect unauthorized activities in the network [23, 28, 30]. A security monitoring surveillance system, which is an intrusion detectionmodel based on detecting anomalies in user behaviors was first introduced by James P. Anderson in 1980 [1]. After that several intrusion detection models based on statistics, Markov chains, time-series, etc proposed by Dorothy Denning in 1986. At first host-based IDSwas implemented, which located in the servermachine to examine the internal interfaces [35], but with the evolution of computer networks day by day focus gradually shifted toward network-based IDS [20]. Network-based IDS monitors and analyzes network traffics for detecting intrusions from internal and external intruders [26, 27, 34]. A number of data mining algorithms have been widely used by the intelligent computational researchers in the large amount of network audit data for detecting known and unknown intrusions in the last decade [3, 9, 18, 32, 33]. Even for a small network the amount of network audit data is very large that an IDS needs to examine. Use of data mining for intrusion detection aim to solve the problem of analyzing the large volumes of audit data and realizing performance optimization of detection rules.

A realistic divisible transferable electronic cash for general use

Abstract An elegant and probably new divisible transferable anonymous electronic cash system with observer is proposed in this paper. The proposed divisible e-cash solves most of the crucial problem with existing paper cash and untraceable e-cash proposals. Electronic cash provides unconditional anonymity to the user. Researchers observed, however, that if anonymity in payment systems is unconditional, it might be exploited to facilitate crimes like money laundering. This observation spurred research into the idea of making anonymity in payment systems conditional, and, in particular, revocable by a third party or trustee or observer under bank’s order. An observer is a tamper resistant device that prevents doublespending physically. The user module is called a wallet since, it actually carries money. The idea of having an observer is that it can be incorporated in the wallet in such a way that no user module can do a transaction on its own. For any transaction protocol to be executed by the wallet, it needs help (a secret information) from the observer. The advantage of the proposed electronic cash system is that it is able to construct an observer capable of co-operating with divisible and transferable e-cash. A user who generates a divisible coin can transfer his any divisible amount of e-cash to another user and to a number of users subsequently without losing anonymity and without contacting the bank between the two transactions. Due to the presence of observer, the proposed e-cash has prior resistance of double spending. In each transfer of divisible e-cash, coin authentication and denomination revelation is checked to verify the validity of divisible e-cash. In any stage of coin transfer, the anonymity is guaranteed with protection of double spending.

STLF using Neural Networks and Fuzzy for anomalous load scenarios - A case study for Hajj

Load Forecasting has an important role in load generation, scheduling, planning etc. in power system. Different computational intelligent techniques are used in Short Term Load Forecasting (STLF) to make it more effective. Neural Networks (NN) is an effective mapping algorithm that can map complex input-output relationships, which is an important technique to do STLF having existing dataset. Usually a proper NN is sufficient to achieve accepted level of performance. But different load dataset may bear some irregular nature of load demand scenario due to having special events, where accuracy of NN suffers significantly. To enhance the performance for those situations, the authors propose a hybrid STLF approach-Neural Networks and Fuzzy (NNF) method. The authors first try to select the best possible trained NN and do STLF. Considering historical data trend and of existing errors of NN solution, for those special days, NNF determine the Load Change trend. Fuzzy Inference Rules (FIR) have been developed to further improvement by fuzzy method. In fuzzy part the NNF apply FIR on two inputs: STLF of NN and Load Change trend, to enhance the performance of STLF for special events. To evaluate the proposed method it is applied on the dataset of Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). Since the authors deal with the daily load dataset of Saudia Arabia of Hijri years (Arabian years), Hajj has been chosen as one of the anomalous load scenario. Empirical results show that for Hajj event of Hijri 1428 year, the accuracy of STLF by NN is approximately 6.37%, whereas proposed NNF can decline the error at only 1.92%.

Computational intelligence approach to load forecasting - a practical application for the desert of Saudi Arabia

This paper presents the development of an Artificial Neural Networks and Particle Swarm Optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land than other places. Thus this model is different from a typical forecasting model considering inputs and outputs. In this research paper two steps have been introduced, first load forecasting made by mapping mechanism and then optimization technique applied to improve its accuracy. This paper includes ANN and PSO models for 24-hours ahead load forecasting. ANN is an effective mathematical tool for mapping complex relationships. It is also successful for doing forecasting, categorization, classification, and so forth. On the other hand, PSO is the most promising optimization tool. It has better information sharing and conveying mechanism; it has better balance of local and global searching abilities; and can handle huge multi-dimensional optimization problems efficiently with hundreds of thousands of constraints. Thus PSO is chosen as the optimization model of the weight matrix of ANN. Results show that the proposed ANN-PSO performs much better than ANN for the load forecasting in a desert like Saudi Arabia.

Secure e-cash model using Java based smartcard

Association of a true observer guaranties electronic cash not to be double-spent by any means. Java card is a smartcard which represents one of the smallest computing platforms. A major challenge influencing the design and implementation of e-cash observer in Java card is the limited availability of computing resources in it. In this paper, we show a new methodology of blending and associating high-level CORBA based bank server, user wallets and resource-constrained Java based observer. We choose a realistic e-cash scheme and show its successful implementation. We also analyze performance of Java card with various lengths of secret keys used for generating electronic coins.