Computer Science And Technology - Research Publications

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    Combinational feature selection approach for network intrusion detection system
    (Institute of Electrical and Electronics Engineers Inc., 2015) Garg, T.; Kumar, Y.
    In the era of digital world, the computer networks are receiving multidimensional advancements. Due to these advancements more and more services are available for malicious exploitation. New vulnerabilities are found from common programs and even on vulnerability in a single computer might compromise the network of an entire company. There are two parallel ways to address this threat. The first way is to ensure that a computer doesn't have any known security vulnerabilities, before allowing it to the network it has access rights. The other way, is to use an Intrusion Detection System. IDSs concentrate on detecting malicious network traffic, such as packets that would exploit known security vulnerability. Generally the intrusions are detected by analyzing 41 attributes from the intrusion detection dataset. In this work we tried to reduce the number of attributes by using various ranking based feature selection techniques and evaluation has been done using ten classification algorithms that I have evaluated most important. So that the intrusions can be detected accurately in short period of time. Then the combinations of the six reduced feature sets have been made using Boolean AND operator. Then their performance has been analyzed using 10 classification algorithms. Finally the top ten combinations of feature selection have been evaluated among 1585 unique combinations. Combination of Symmetric and Gain Ratio while considering top 15 attributes has highest performance. ? 2014 IEEE.
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    Classification of defective modules using object-oriented metrics
    (Inderscience Enterprises Ltd., 2017) Singh, Satwinder; Singla, Rozy
    Software defect in today's era is crucial in the field of software engineering. Most of the organisations use various techniques to predict defects in their products before they are delivered. Defect prediction techniques help the organisations to use their resources effectively which results in lower cost and time requirements. There are various techniques that are used for predicting defects in software before it has to be delivered, e.g., clustering, neural networks, support vector machine (SVM). In this paper two defect prediction techniques: K-means clustering and multi-layer perceptron model (MLP) are compared. Both the techniques are implemented on different platforms. K-means clustering is implemented using WEKA tool and MLP is implemented using SPSS. The results are compared to find which algorithm produces better results. In this paper object-oriented metrics are used for predicting defects in the software. Copyright ? 2017 Inderscience Enterprises Ltd.
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    SHED: Spam Ham Email Dataset
    (International Journal on Recent and Innovation Trends in Computing and Communication (, 2017) Sharma, Upasana; Khurana, Surinder Singh
    Automatic filtering of spam emails becomes essential feature for a good email service provider. To gain direct or indirect benefits organizations/individuals are sending a lot of spam emails. Such kind emails activities are not only distracting the user but also consume lot of resources including processing power, memory and network bandwidth. The security issues are also associated with these unwanted emails as these emails may contain malicious content and/or links. Content based spam filtering is one of the effective approaches used for filtering. However, its efficiency depends upon the training set. The most of the existing datasets were collected and prepared a long back and the spammers have been changing the content to evade the filters trained based on these datasets. In this paper, we introduce Spam Ham email dataset (SHED): a dataset consisting spam and ham email. We evaluated the performance of filtering techniques trained by previous datasets and filtering techniques trained by SHED. It was observed that the filtering techniques trained by SHED outperformed the technique trained by other dataset. Furthermore, we also classified the spam email into various categories.
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    Comparison of classification techniques for intrusion detection dataset using WEKA
    (Institute of Electrical and Electronics Engineers Inc., 2014) Garg, T.; Khurana, S.S.
    As the network based applications are growing rapidly, the network security mechanisms require more attention to improve speed and precision. The ever evolving new intrusion types pose a serious threat to network security. Although numerous network security tools have been developed, yet the fast growth of intrusive activities is still a serious issue. Intrusion detection systems (IDSs) are used to detect intrusive activities on the network. Machine learning and classification algorithms help to design 'Intrusion Detection Models' which can classify the network traffic into intrusive or normal traffic. In this paper we present the comparative performance of NSL-KDD based data set compatible classification algorithms. These classifiers have been evaluated in WEKA (Waikato Environment for Knowledge Analysis) environment using 41 attributes. Around 94,000 instances from complete KDD dataset have been included in the training data set and over 48,000 instances have been included in the testing data set. Garrett's Ranking Technique has been applied to rank different classifiers according to their performance. Rotation Forest classification approach outperformed the rest. ? 2014 IEEE.