PFU: Profiling Forum users in online social networks, a knowledge driven data mining approach

Online Social Networks (OSNs) provide platform to raise opinions on various issues, create and spread news rapidly in Online Social Network Forums (OSNFs). This work proposes a novel method for Profiling Forum Users (PFU) by exploring their behavioral characteristics based on their involvement in various topics of discussion and number of posts in respective topics posted by them in OSNFs dynamically. Modeling the proposed method mathematically, the PFU algorithm is illustrated for its adequacy and accuracy

CPMTS: Catching packet modifiers with trust support in wireless sensor networks

Security in wireless sensor networks is critical due to its way of open communication. Packet modification is a common attack in wireless sensor networks. In literature, many schemes have been proposed to mitigate such an attack but very few detect the malicious nodes effectively. In the proposed approach, each node chooses the parent node for forwarding the packet towards sink. Each node adds its identity and trust on parent as a routing path marker and encrypts only the bytes added by node in packet before forwarding to parent. Sink can determine the modifiers based on trust value and node identities marked in packet. Child node observes the parent and decides the trust on parent based on successful and unsuccessful transactions. Data transmission is divided into multiple rounds of equal time duration. Each node chooses the parent node at the beginning of a round based on its own observation on parent. Simulated the algorithm in NS-3 and performance analysis is discussed. With the combination of trust factor and fixed path routing to detect malicious activity, analytical results show that proposed method detect modifiers efficiently and early, and also with low percentage of false detection

CMNTS: Catching malicious nodes with trust support in wireless sensor networks

Security in wireless sensor networks is critical due to its way of open communication. In this paper we have considered suite of attacks - packet modification, packet dropping, sybil attack, packet misrouting, and bad mouthing attack, and provided a solution to detect attacks. In literature, many schemes have been proposed to mitigate such attacks but very few detect the malicious nodes effectively and also no single solution detects all attacks. In the proposed approach, each node chooses the parent node for forwarding the packet towards sink. Each node adds its identity and trust on parent as a routing path marker and encrypts only the bytes added by node in packet before forwarding to parent. Sink can identify the malicious node based on trust value and node identities marked in packet. Child node observes the parent and decides the trust on parent based on successful and unsuccessful transactions. Data transmission is divided into multiple rounds of equal time duration. Each node chooses the parent node at the beginning of a round based on its own observation on parent. Simulated the algorithm in NS-3 and performance analysis is discussed by comparing the results with other two recently proposed approaches. With the combination of trust factor and fixed path routing to detect malicious activity, simulation results show that proposed method detect malicious nodes efficiently and early, and also with low percentage of false detection

Security in Data Mining- A Comprehensive Survey

Data mining techniques, while allowing the individuals to extract hidden knowledge on one hand, introduce a number of privacy threats on the other hand. In this paper, we study some of these issues along with a detailed discussion on the applications of various data mining techniques for providing security. An efficient classification technique when used properly, would allow an user to differentiate between a phishing website and a normal website, to classify the users as normal users and criminals based on their activities on Social networks (Crime Profiling) and to prevent users from executing malicious codes by labelling them as malicious. The most important applications of Data mining is the detection of intrusions, where different Data mining techniques can be applied to effectively detect an intrusion and report in real time so that necessary actions are taken to thwart the attempts of the intruder. Privacy Preservation, Outlier Detection, Anomaly Detection and PhishingWebsite Classification are discussed in this paper

HSRA: Hindi stopword removal algorithm

In the last few years, electronic documents have been the main source of data in many research areas like Web Mining, Information Retrieval, Artificial Intelligence, Natural Language Processing etc. Text Processing plays a vital role for processing structured or unstructured data from the web. Preprocessing is the main step in any text processing systems. One significant preprocessing technique is the elimination of functional words, also known as stopwords, which affects the performance of text processing tasks. An efficient stopword removal technique is required in all text processing tasks. In this paper, we are proposing a stopword removal algorithm for Hindi Language which is using the concept of a Deterministic Finite Automata (DFA). A large number of available works on stopword removal techniques are based on dictionary containing stopword lists. Then pattern matching technique is applied and the matched patterns, which is a stopword, is removed from the document. It is a time consuming task as searching process takes a long time. This makes the method inefficient and very expensive. In comparison of that, our algorithm has been tested on 200 documents and achieved 99% accuracy and also time efficient

HSAS: Hindi Subjectivity Analysis System

With the development of Web 2.0, we are abundant with the documents expressing user's opinions, attitudes and sentiments in the textual form. This user generated textual content is an important source of information to make sound decisions by the organizations and the government. The textual information can be categorized into two types: facts and opinions. Subjectivity analysis is the automatic extraction of subjective information from the opinions posted by users and divides the content into subjective and objective sentences. Most of the works in subjectivity analysis exists for English language data but with the introduction of unicode standards UTF-8, Hindi language content on the web is growing very rapidly. In this paper, Hindi Subjectivity Analysis System (HSAS) is proposed. It explores two different methods of generating subjectivity lexicon using the available resources in English language and their comparative evaluation in performing the task of subjectivity analysis at the sentence level. The first method uses English language OpinionFinder subjectivity lexicon. The second method uses a small seed word list of Hindi language and expands it to generate subjectivity lexicon. Different evaluation strategies are used to validate the lexicon. We achieved 71.4% agreement with human annotators and ~80% accuracy in classification on a parallel data set in English and Hindi. Extensive simulations conducted on the test dataset confirm the validity of the suggested method

SDLM: Source detection based local monitoring in wireless sensor networks

Security in wireless sensor networks is critical due to its way of open communication. Local monitoring is one of the powerful technique to secure the data and detect various malicious activities. In local monitoring, neighbour nodes observe the communication between current sender, current receiver and next hop receiver to detect the malicious activity. To make sensors power efficient, sleep-wake scheduling algorithms along with local monitoring are suggested in literature. Solutions in the literature do not address the problem if source node is malicious and do not consider unnecessary wake up of the nodes as malicious activity. This paper tries to achieve without assuming source node as honest and considers unnecessary wake up of the node as a malicious activity. Simulated the algorithm in NS-2 and performance analysis is discussed. Even with additional checks applied to detect malicious activities, analytical results show no degradation in the performance

Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier

With the rapid development of the World Wide Web, electronic word-of-mouth interaction has made consumers active participants. Nowadays, a large number of reviews posted by the consumers on the Web provide valuable information to other consumers. Such information is highly essential for decision making and hence popular among the internet users. This information is very valuable not only for prospective consumers to make decisions but also for businesses in predicting the success and sustainability. In this paper, a Gini Index based feature selection method with Support Vector Machine (SVM) classifier is proposed for sentiment classification for large movie review data set. The results show that our Gini Index method has better classification performance in terms of reduced error rate and accuracy

Fault tolerant BeeHive routing in mobile ad-hoc multi-radio network

In this paper, fault tolerance in a multi-radio network is discussed. Fault tolerance is achieved using the BeeHive routing algorithm. The paper discusses faults added to the system as random fluctuations in hardware radio operation. The multi-radio nodes are designed using WiMAX and WiFi Radios that work in conjunction using traffic splitting to transfer data across a multi-hop network. During the operation of this network random faults are introduced by turning off certain radios in nodes. The paper discusses fault tolerance as applied to multi radio nodes that use traffic splitting in the transmission of data. We also propose a method to handle random faults in hardware radios by using traffic splitting and combining it with the BeeHive routing algorithm

HUBFIRE - A multi-class SVM based JPEG steganalysis using HBCL statistics and FR Index

Blind Steganalysis attempts to detect steganographic data without prior knowledge of either the embedding algorithm or the 'cover' image. This paper proposes new features for JPEG blind steganalysis using a combination of Huffman Bit Code Length (HBCL) Statistics and File size to Resolution ratio (FR Index); the Huffman Bit File Index Resolution (HUBFIRE) algorithm proposed uses these functionals to build the classifier using a multi-class Support Vector Machine (SVM). JPEG images spanning a wide range of resolutions are used to create a 'stego-image' database employing three embedding schemes - the advanced Least Significant Bit encoding technique, that embeds in the spatial domain, a transform-domain embedding scheme: JPEG Hide-and-Seek and Model Based Steganography which employs an adaptive embedding technique. This work employs a multi-class SVM over the proposed 'HUBFIRE' algorithm for statistical steganalysis, which is not yet explored by steganalysts. Experiments conducted prove the model's accuracy over a wide range of payloads and embedding schemes