Sanjay K. Sahay

Mohit Sewak, Sanjay K. Sahay, Hemant Rathore

The cybersecurity threat landscape has lately become overly complex. Threat actors leverage weaknesses in the network and endpoint security in a very coordinated manner to perpetuate sophisticated attacks that could bring down the entire network and many critical hosts in the network. Increasingly advanced deep and machine learning-based solutions have been used in threat detection and protection. The application of these techniques has been reviewed well in the scientific literature. Deep Reinforcement Learning has shown great promise in developing AI-based solutions for areas that had earlier required advanced human cognizance. Different techniques and algorithms under deep reinforcement learning have shown great promise in applications ranging from games to industrial processes, where it is claimed to augment systems with general AI capabilities. These algorithms have recently also been used in cybersecurity, especially in threat detection and endpoint protection, where these are showing state-of-the-art results. Unlike supervised machines and deep learning, deep reinforcement learning is used in more diverse ways and is empowering many innovative applications in the threat defense landscape. However, there does not exist any comprehensive review of these unique applications and accomplishments. Therefore, in this paper, we intend to fill this gap and provide a comprehensive review of the different applications of deep reinforcement learning in cybersecurity threat detection and protection.

Trupil Limbasiya, Sanjay K. Sahay, Bharath Sridharan

The usage of different technologies and smart devices helps people to get medical services remotely for multiple benefits. Thus, critical and sensitive data is exchanged between a user and a doctor. When health data is transmitted over a common channel, it becomes essential to preserve various privacy and security properties in the system. Further, the number of users for remote services is increasing day-by-day exponentially, and thus, it is not adequate to deal with all users using the one server due to the verification overhead, server failure, and scalability issues. Thus, researchers proposed various authentication protocols for multi-server architecture, but most of them are vulnerable to different security attacks and require high computational resources during the implementation. To Tackle privacy and security issues using less computational resources, we propose a privacy-preserving mutual authentication and key agreement protocol for a multi-server healthcare system. We discuss the proposed scheme's security analysis and performance results to understand its security strengths and the computational resource requirement, respectively. Further, we do the comparison of security and performance results with recent relevant authentication protocols.

Mohit Sewak, Sanjay K. Sahay, Hemant Rathore

Supervised Deep Learning requires plenty of labeled data to converge, and hence perform optimally for task-specific learning. Therefore, we propose a novel mechanism named DRo (for Deep Routing) for data-scarce domains like security. The DRo approach builds upon some of the recent developments in Deep-Clustering. In particular, it exploits the self-augmented training mechanism using synthetically generated local perturbations. DRo not only allays the challenges with sparse-labeled data but also offers many unique advantages. We also developed a system named DRoID that uses the DRo mechanism for enhancing the performance of an existing Malware Detection System that uses (low information features like the) Android implicit Intent(s) as the only features. We conduct experiments on DRoID using a popular and standardized Android malware dataset and found that the DRo mechanism could successfully reduce the false-alarms generated by the downstream classifier by 67.9%, and also simultaneously boosts its accuracy by 11.3%. This is significant not only because the gains achieved are unparalleled but also because the features used were never considered rich enough to train a classifier on; and hence no decent performance could ever be reported by any malware classification system till-date using these features in isolation. Owing to the results achieved, the DRo mechanism claims a dominant position amongst all known systems that aims to enhance the classification performance of deep learning models with sparse-labeled data.

Hemant Rathore, Sanjay K. Sahay, Ritvik Rajvanshi et al.

Since Google unveiled Android OS for smartphones, malware are thriving with 3Vs, i.e. volume, velocity, and variety. A recent report indicates that one out of every five business/industry mobile application leaks sensitive personal data. Traditional signature/heuristic-based malware detection systems are unable to cope up with current malware challenges and thus threaten the Android ecosystem. Therefore recently researchers have started exploring machine learning and deep learning based malware detection systems. In this paper, we performed a comprehensive feature analysis to identify the significant Android permissions and propose an efficient Android malware detection system using machine learning and deep neural network. We constructed a set of $16$ permissions ($8\%$ of the total set) derived from variance threshold, auto-encoders, and principal component analysis to build a malware detection engine that consumes less train and test time without significant compromise on the model accuracy. Our experimental results show that the Android malware detection model based on the random forest classifier is most balanced and achieves the highest area under curve score of $97.7\%$, which is better than the current state-of-art systems. We also observed that deep neural networks attain comparable accuracy to the baseline results but with a massive computational penalty.

Hemant Rathore, Sanjay K. Sahay, Shivin Thukral et al.

Today anti-malware community is facing challenges due to the ever-increasing sophistication and volume of malware attacks developed by adversaries. Traditional malware detection mechanisms are not able to cope-up with next-generation malware attacks. Therefore in this paper, we propose effective and efficient Android malware detection models based on machine learning and deep learning integrated with clustering. We performed a comprehensive study of different feature reduction, classification and clustering algorithms over various performance metrics to construct the Android malware detection models. Our experimental results show that malware detection models developed using Random Forest eclipsed deep neural network and other classifiers on the majority of performance metrics. The baseline Random Forest model without any feature reduction achieved the highest AUC of 99.4%. Also, the segregating of vector space using clustering integrated with Random Forest further boosted the AUC to 99.6% in one cluster and direct detection of Android malware in another cluster, thus reducing the curse of dimensionality. Additionally, we found that feature reduction in detection models does improve the model efficiency (training and testing time) many folds without much penalty on the effectiveness of the detection model.

Mohit Sewak, Sanjay K. Sahay, Hemant Rathore

In this paper, we propose a novel mechanism to normalize metamorphic and obfuscated malware down at the opcode level and hence create an advanced metamorphic malware de-obfuscation and defense system. We name this system DRLDO, for Deep Reinforcement Learning based De-Obfuscator. With the inclusion of the DRLDO as a sub-component, an existing Intrusion Detection System could be augmented with defensive capabilities against 'zero-day' attacks from obfuscated and metamorphic variants of existing malware. This gains importance, not only because there exists no system to date that uses advanced DRL to intelligently and automatically normalize obfuscation down even to the opcode level, but also because the DRLDO system does not mandate any changes to the existing IDS. The DRLDO system does not even mandate the IDS' classifier to be retrained with any new dataset containing obfuscated samples. Hence DRLDO could be easily retrofitted into any existing IDS deployment. We designed, developed, and conducted experiments on the system to evaluate the same against multiple-simultaneous attacks from obfuscations generated from malware samples from a standardized dataset that contains multiple generations of malware. Experimental results prove that DRLDO was able to successfully make the otherwise un-detectable obfuscated variants of the malware detectable by an existing pre-trained malware classifier. The detection probability was raised well above the cut-off mark to 0.6 for the classifier to detect the obfuscated malware unambiguously. Further, the de-obfuscated variants generated by DRLDO achieved a very high correlation (of 0.99) with the base malware. This observation validates that the DRLDO system is actually learning to de-obfuscate and not exploiting a trivial trick.

Hemant Rathore, Sanjay K. Sahay, Piyush Nikam et al.

The current state-of-the-art Android malware detection systems are based on machine learning and deep learning models. Despite having superior performance, these models are susceptible to adversarial attacks. Therefore in this paper, we developed eight Android malware detection models based on machine learning and deep neural network and investigated their robustness against adversarial attacks. For this purpose, we created new variants of malware using Reinforcement Learning, which will be misclassified as benign by the existing Android malware detection models. We propose two novel attack strategies, namely single policy attack and multiple policy attack using reinforcement learning for white-box and grey-box scenario respectively. Putting ourselves in the adversary's shoes, we designed adversarial attacks on the detection models with the goal of maximizing fooling rate, while making minimum modifications to the Android application and ensuring that the app's functionality and behavior do not change. We achieved an average fooling rate of 44.21% and 53.20% across all the eight detection models with a maximum of five modifications using a single policy attack and multiple policy attack, respectively. The highest fooling rate of 86.09% with five changes was attained against the decision tree-based model using the multiple policy approach. Finally, we propose an adversarial defense strategy that reduces the average fooling rate by threefold to 15.22% against a single policy attack, thereby increasing the robustness of the detection models i.e. the proposed model can effectively detect variants (metamorphic) of malware. The experimental analysis shows that our proposed Android malware detection system using reinforcement learning is more robust against adversarial attacks.

Mohit Sewak, Sanjay K. Sahay, Hemant Rathore

Recurrent deep learning language models like the LSTM are often used to provide advanced cyber-defense for high-value assets. The underlying assumption for using LSTM networks for malware-detection is that the op-code sequence of malware could be treated as a (spoken) language representation. There are differences between any spoken-language (sequence of words/sentences) and the machine-language (sequence of op-codes). In this paper, we demonstrate that due to these inherent differences, an LSTM model with its default configuration as tuned for a spoken-language, may not work well to detect malware (using its op-code sequence) unless the network's essential hyper-parameters are tuned appropriately. In the process, we also determine the relative importance of all the different hyper-parameters of an LSTM network as applied to malware detection using their op-code sequence representations. We experimented with different configurations of LSTM networks, and altered hyper-parameters like the embedding-size, number of hidden layers, number of LSTM-units in a hidden layer, pruning/padding-length of the input-vector, activation-function, and batch-size. We discovered that owing to the enhanced complexity of the malware/machine-language, the performance of an LSTM network configured for an Intrusion Detection System, is very sensitive towards the number-of-hidden-layers, input sequence-length, and the choice of the activation-function. Also, for (spoken) language-modeling, the recurrent architectures by-far outperform their non-recurrent counterparts. Therefore, we also assess how sequential DL architectures like the LSTM compare against their non-sequential counterparts like the MLP-DNN for the purpose of malware-detection.

Mohit Sewak, Sanjay K. Sahay, Hemant Rathore

The Intent in Android plays an important role in inter-process and intra-process communications. The implicit Intent that an application could accept are declared in its manifest and are amongst the easiest feature to extract from an apk. Implicit Intents could even be extracted online and in real-time. So far neither the feasibility of developing an Intrusion Detection System solely on implicit Intent has been explored, nor are any benchmarks available of a malware classifier that is based on implicit Intent alone. We demonstrate that despite Intent is implicit and well declared, it can provide very intuitive insights to distinguish malicious from non-malicious applications. We conducted exhaustive experiments with over 40 different end-to-end Deep Learning configurations of Auto-Encoders and Multi-Layer-Perceptron to create a benchmark for a malware classifier that works exclusively on implicit Intent. Using the results from the experiments we create an intrusion detection system using only the implicit Intents and end-to-end Deep Learning architecture. We obtained an area-under-curve statistic of 0.81, and accuracy of 77.2% along with false-positive-rate of 0.11 on Drebin dataset.

Shriya TP Gupta, Sanjay K Sahay

Hyper-spectral satellite imagery is now widely being used for accurate disaster prediction and terrain feature classification. However, in such classification tasks, most of the present approaches use only the spectral information contained in the images. Therefore, in this paper, we present a novel framework that takes into account both the spectral and spatial information contained in the data for land cover classification. For this purpose, we use the Gaussian Maximum Likelihood (GML) and Convolutional Neural Network methods for the pixel-wise spectral classification and then, using segmentation maps generated by the Watershed algorithm, we incorporate the spatial contextual information into our model with a modified majority vote technique. The experimental analyses on two benchmark datasets demonstrate that our proposed methodology performs better than the earlier approaches by achieving an accuracy of 99.52% and 98.31% on the Pavia University and the Indian Pines datasets respectively. Additionally, our GML based approach, a non-deep learning algorithm, shows comparable performance to the state-of-the-art deep learning techniques, which indicates the importance of the proposed approach for performing a computationally efficient classification of hyper-spectral imagery.

Trupil Limbasiya, Sanjay K. Sahay

Authentication schemes are practised globally to verify the legitimacy of users and servers for the exchange of data in different facilities. Generally, the server verifies a user to provide resources for different purposes. But due to the large network system, the authentication process has become complex and therefore, time-to-time different authentication protocols have been proposed for the multi-server architecture. However, most of the protocols are vulnerable to various security attacks and their performance is not efficient. In this paper, we propose a secure and energy-efficient remote user authentication protocol for multi-server systems. The results show that the proposed protocol is comparatively ~44% more efficient and needs ~38% less communication cost. We also demonstrate that with only two-factor authentication, the proposed protocol is more secure from the earlier related authentication schemes.

Trupil Limbasiya, Debasis Das, Sanjay K. Sahay

The pioneering concept of connected vehicles has transformed the way of thinking for researchers and entrepreneurs by collecting relevant data from nearby objects. However, this data is useful for a specific vehicle only. Moreover, vehicles get a high amount of data (e.g., traffic, safety, and multimedia infotainment) on the road. Thus, vehicles expect adequate storage devices for this data, but it is infeasible to have a large memory in each vehicle. Hence, the vehicular cloud computing (VCC) framework came into the picture to provide a storage facility by connecting a road-side-unit (RSU) with the vehicular cloud (VC). In this, data should be saved in an encrypted form to preserve security, but there is a challenge to search for information over encrypted data. Next, we understand that many of vehicular communication schemes are inefficient for data transmissions due to its poor performance results and vulnerable to different fundamental security attacks. Accordingly, on-device performance is critical, but data damages and secure on-time connectivity are also significant challenges in a public environment. Therefore, we propose reliable data transmission protocols for cutting-edge architecture to search data from the storage, to resist against various security attacks, and provide better performance results. Thus, the proposed data transmission protocol is useful in diverse smart city applications (business, safety, and entertainment) for the benefits of society.

Sanjay K. Sahay, Mayank Chaudhari

Due to continuous increase in the number of malware (according to AV-Test institute total ~8 x 10^8 malware are already known, and every day they register ~2.5 x 10^4 malware) and files in the computational devices, it is very important to design a system which not only effectively but can also efficiently detect the new or previously unseen malware to prevent/minimize the damages. Therefore, this paper presents a novel group-wise approach for the efficient detection of malware by parallelizing the classification using the power of GPGPU and shown that by using the Naive Bayes classifier the detection speed-up can be boosted up to 200x. The investigation also shows that the classification time increases significantly with the number of features.

Sanjay K. Sahay, Ashu Sharma

Android-based smart devices are exponentially growing, and due to the ubiquity of the Internet, these devices are globally connected to the different devices/networks. Its popularity, attractive features, and mobility make malware creator to put a number of malicious apps in the market to disrupt and annoy the victims. Although to identify the malicious apps, time-to-time various techniques are proposed. However, it appears that malware developers are always ahead of the anti-malware group, and the proposed techniques by the anti-malware groups are not sufficient to counter the advanced malicious apps. Therefore, to understand the various techniques proposed/used for the identification of Android malicious apps, in this paper, we present a survey conducted by us on the work done by the researchers in this field.

Hemant Rathore, Sanjay K. Sahay, Palash Chaturvedi et al.

Android malware have been growing at an exponential pace and becomes a serious threat to mobile users. It appears that most of the anti-malware still relies on the signature-based detection system which is generally slow and often not able to detect advanced obfuscated malware. Hence time-to-time various authors have proposed different machine learning solutions to identify sophisticated malware. However, it appears that detection accuracy can be improved by using the clustering method. Therefore in this paper, we propose a novel scalable and effective clustering method to improve the detection accuracy of the malicious android application and obtained a better overall accuracy (98.34%) by random forest classifier compared to regular method, i.e., taking the data altogether to detect the malware. However, as far as true positive and true negative are concerned, by clustering method, true positive is best obtained by decision tree (97.59%) and true negative by support vector machine (99.96%) which is the almost same result obtained by the random forest true positive (97.30%) and true negative (99.38%) respectively. The reason that overall accuracy of random forest is high because the true positive of support vector machine and true negative of the decision tree is significantly less than the random forest.

Ashu Sharma, Sanjay K. Sahay

In the fast-growing smart devices, Android is the most popular OS, and due to its attractive features, mobility, ease of use, these devices hold sensitive information such as personal data, browsing history, shopping history, financial details, etc. Therefore, any security gap in these devices means that the information stored or accessing the smart devices are at high risk of being breached by the malware. These malware are continuously growing and are also used for military espionage, disrupting the industry, power grids, etc. To detect these malware, traditional signature matching techniques are widely used. However, such strategies are not capable to detect the advanced Android malicious apps because malware developer uses several obfuscation techniques. Hence, researchers are continuously addressing the security issues in the Android based smart devices. Therefore, in this paper using Drebin benchmark malware dataset we experimentally demonstrate how to improve the detection accuracy by analyzing the apps after grouping the collected data based on the permissions and achieved 97.15% overall average accuracy. Our results outperform the accuracy obtained without grouping data (79.27%, 2017), Arp, et al. (94%, 2014), Annamalai et al. (84.29%, 2016), Bahman Rashidi et al. (82%, 2017)) and Ali Feizollah, et al. (95.5%, 2017). The analysis also shows that among the groups, Microphone group detection accuracy is least while Calendar group apps are detected with the highest accuracy, and with the highest accuracy, and for the best performance, one shall take 80-100 features.

Sanjay Sharma, C. Rama Krishna, Sanjay K. Sahay

In today's digital world most of the anti-malware tools are signature based which is ineffective to detect advanced unknown malware viz. metamorphic malware. In this paper, we study the frequency of opcode occurrence to detect unknown malware by using machine learning technique. For the purpose, we have used kaggle Microsoft malware classification challenge dataset. The top 20 features obtained from fisher score, information gain, gain ratio, chi-square and symmetric uncertainty feature selection methods are compared. We also studied multiple classifier available in WEKA GUI based machine learning tool and found that five of them (Random Forest, LMT, NBT, J48 Graft and REPTree) detect malware with almost 100% accuracy.

Mohit Sewak, Sanjay K. Sahay, Hemant Rathore

Recently, Deep Learning has been showing promising results in various Artificial Intelligence applications like image recognition, natural language processing, language modeling, neural machine translation, etc. Although, in general, it is computationally more expensive as compared to classical machine learning techniques, their results are found to be more effective in some cases. Therefore, in this paper, we investigated and compared one of the Deep Learning Architecture called Deep Neural Network (DNN) with the classical Random Forest (RF) machine learning algorithm for the malware classification. We studied the performance of the classical RF and DNN with 2, 4 & 7 layers architectures with the four different feature sets, and found that irrespective of the features inputs, the classical RF accuracy outperforms the DNN.

Mohit Sewak, Sanjay K. Sahay, Hemant Rathore

We investigate a Deep Learning based system for malware detection. In the investigation, we experiment with different combination of Deep Learning architectures including Auto-Encoders, and Deep Neural Networks with varying layers over Malicia malware dataset on which earlier studies have obtained an accuracy of (98%) with an acceptable False Positive Rates (1.07%). But these results were done using extensive man-made custom domain features and investing corresponding feature engineering and design efforts. In our proposed approach, besides improving the previous best results (99.21% accuracy and a False Positive Rate of 0.19%) indicates that Deep Learning based systems could deliver an effective defense against malware. Since it is good in automatically extracting higher conceptual features from the data, Deep Learning based systems could provide an effective, general and scalable mechanism for detection of existing and unknown malware.

Ashu Sharma, Sanjay K. Sahay

Android devices are growing exponentially and are connected through the internet accessing billion of online websites. The popularity of these devices encourages malware developer to penetrate the market with malicious apps to annoy and disrupt the victim. Although, for the detection of malicious apps different approaches are discussed. However, proposed approaches are not suffice to detect the advanced malware to limit/prevent the damages. In this, very few approaches are based on opcode occurrence to classify the malicious apps. Therefore, this paper investigates the five classifiers using opcodes occurrence as the prominent features for the detection of malicious apps. For the analysis, we use WEKA tool and found that FT detection accuracy (79.27%) is best among the investigated classifiers. However, true positives rate i.e. malware detection rate is highest (99.91%) by RF and fluctuate least with the different number of prominent features compared to other studied classifiers. The analysis shows that overall accuracy is majorly affected by the false positives of the classifier.

Ashu Sharma, Sanjay K. Sahay, Abhishek Kumar

Detection of unknown malware with high accuracy is always a challenging task. Therefore, in this paper, we study the classification of unknown malware by two methods. In the first/regular method, similar to other authors [17][16][20] approaches we select the features by taking all dataset in one group and in the second method, we select the features by partitioning the dataset in the range of file 5 KB size. We find that the second method to detect the malware with ~8.7% more accurate than the first/regular method.

Sanjay K. Sahay, Ashu Sharma

The metamorphic malware variants with the same malicious behavior (family), can obfuscate themselves to look different from each other. This variation in structure leads to a huge signature database for traditional signature matching techniques to detect them. In order to effective and efficient detection of malware in large amounts of executables, we need to partition these files into groups which can identify their respective families. In addition, the grouping criteria should be chosen such a way that, it can also be applied to unknown files encounter on computers for classification. This paper discusses the study of malware and benign executables in groups to detect unknown malware with high accuracy. We studied sizes of malware generated by three popular second generation malware (metamorphic malware) creator kits viz. G2, PS-MPC and NGVCK, and observed that the size variation in any two generated malware from same kit is not much. Hence, we grouped the executables on the basis of malware sizes by using Optimal k-Means Clustering algorithm and used these obtained groups to select promising features for training (Random forest, J48, LMT, FT and NBT) classifiers to detect variants of malware or unknown malware. We find that detection of malware on the basis of their respected file sizes gives accuracy up to 99.11% from the classifiers.

Ashu Sharma, Sanjay K. Sahay

Combating malware is very important for software/systems security, but to prevent the software/systems from the advanced malware, viz. metamorphic malware is a challenging task, as it changes the structure/code after each infection. Therefore in this paper, we present a novel approach to detect the advanced malware with high accuracy by analyzing the occurrence of opcodes (features) by grouping the executables. These groups are made on the basis of our earlier studies [1] that the difference between the sizes of any two malware generated by popular advanced malware kits viz. PS-MPC, G2 and NGVCK are within 5 KB. On the basis of obtained promising features, we studied the performance of thirteen classifiers using N-fold cross-validation available in machine learning tool WEKA. Among these thirteen classifiers we studied in-depth top five classifiers (Random forest, LMT, NBT, J48 and FT) and obtain more than 96.28% accuracy for the detection of unknown malware, which is better than the maximum detection accuracy (95.9%) reported by Santos et al (2013). In these top five classifiers, our approach obtained a detection accuracy of 97.95% by the Random forest.

Aruna Govada, Bhavul Gauri, S. K. Sahay

Support Vector Machines (SVMs) were primarily designed for 2-class classification. But they have been extended for N-class classification also based on the requirement of multiclasses in the practical applications. Although N-class classification using SVM has considerable research attention, getting minimum number of classifiers at the time of training and testing is still a continuing research. We propose a new algorithm CBTS-SVM (Centroid based Binary Tree Structured SVM) which addresses this issue. In this we build a binary tree of SVM models based on the similarity of the class labels by finding their distance from the corresponding centroids at the root level. The experimental results demonstrates the comparable accuracy for CBTS with OVO with reasonable gamma and cost values. On the other hand when CBTS is compared with OVA, it gives the better accuracy with reduced training time and testing time. Furthermore CBTS is also scalable as it is able to handle the large data sets.

Rajendra Kumar Roul, Saransh Varshneya, Ashu Kalra et al.

The traditional apriori algorithm can be used for clustering the web documents based on the association technique of data mining. But this algorithm has several limitations due to repeated database scans and its weak association rule analysis. In modern world of large databases, efficiency of traditional apriori algorithm would reduce manifolds. In this paper, we proposed a new modified apriori approach by cutting down the repeated database scans and improving association analysis of traditional apriori algorithm to cluster the web documents. Further we improve those clusters by applying Fuzzy C-Means (FCM), K-Means and Vector Space Model (VSM) techniques separately. For experimental purpose, we use Classic3 and Classic4 datasets of Cornell University having more than 10,000 documents and run both traditional apriori and our modified apriori approach on it. Experimental results show that our approach outperforms the traditional apriori algorithm in terms of database scan and improvement on association of analysis. We found out that FCM is better than K-Means and VSM in terms of F-measure of clusters of different sizes.

R. K. Roul, O. R. Devanand, S. K. Sahay

The dynamic web has increased exponentially over the past few years with more than thousands of documents related to a subject available to the user now. Most of the web documents are unstructured and not in an organized manner and hence user facing more difficult to find relevant documents. A more useful and efficient mechanism is combining clustering with ranking, where clustering can group the similar documents in one place and ranking can be applied to each cluster for viewing the top documents at the beginning.. Besides the particular clustering algorithm, the different term weighting functions applied to the selected features to represent web document is a main aspect in clustering task. Keeping this approach in mind, here we proposed a new mechanism called Tf-Idf based Apriori for clustering the web documents. We then rank the documents in each cluster using Tf-Idf and similarity factor of documents based on the user query. This approach will helps the user to get all his relevant documents in one place and can restrict his search to some top documents of his choice. For experimental purpose, we have taken the Classic3 and Classic4 datasets of Cornell University having more than 10,000 documents and use gensim toolkit to carry out our work. We have compared our approach with traditional apriori algorithm and found that our approach is giving better results for higher minimum support. Our ranking mechanism is also giving a good F-measure of 78%.

R. K. Roul, S. K. Sahay

Exponential growth of the web increased the importance of web document classification and data mining. To get the exact information, in the form of knowing what classes a web document belongs to, is expensive. Automatic classification of web document is of great use to search engines which provides this information at a low cost. In this paper, we propose an approach for classifying the web document using the frequent item word sets generated by the Frequent Pattern (FP) Growth which is an association analysis technique of data mining. These set of associated words act as feature set. The final classification obtained after Naïve Bayes classifier used on the feature set. For the experimental work, we use Gensim package, as it is simple and robust. Results show that our approach can be effectively classifying the web document.

R. K. Roul, S. K. Sahay

Search engine returns thousands of web pages for a single user query, in which most of them are not relevant. In this context, effective information retrieval from the expanding web is a challenging task, in particular, if the query is ambiguous. The major question arises here is that how to get the relevant pages for an ambiguous query. We propose an approach for the effective result of an ambiguous query by forming community vector based on association concept of data minning using vector space model and the freedictionary. We develop clusters by computing the similarity between community vectors and document vectors formed from the extracted web pages by the search engine. We use Gensim package to implement the algorithm because of its simplicity and robust nature. Analysis shows that our approach is an effective way to form clusters for an ambiguous query.