Sharath Chandra Guntuku

Big Data Analytics framework for Peer-to-Peer Botnet detection using Random Forests

Abstract Network traffic monitoring and analysis-related research has struggled to scale for massive amounts of data in real time. Some of the vertical scaling solutions provide good implementation of signature based detection. Unfortunately these approaches treat network flows across different subnets and cannot apply anomaly-based classification if attacks originate from multiple machines at a lower speed, like the scenario of Peer-to-Peer Botnets. In this paper the authors build up on the progress of open source tools like Hadoop, Hive and Mahout to provide a scalable implementation of quasi-real-time intrusion detection system. The implementation is used to detect Peer-to-Peer Botnet attacks using machine learning approach. The contributions of this paper are as follows: (1) Building a distributed framework using Hive for sniffing and processing network traces enabling extraction of dynamic network features; (2) Using the parallel processing power of Mahout to build Random Forest based Decision Tree model which is applied to the problem of Peer-to-Peer Botnet detection in quasi-real-time. The implementation setup and performance metrics are presented as initial observations and future extensions are proposed.

Personality Modeling Based Image Recommendation

With the increasing proliferation of data production technologies (like cameras) and consumption avenues (like social media) multimedia has become an interaction channel among users today. Images and videos are being used by the users to convey innate preferences and tastes. This has led to the possibility of using multimedia as a source for user-modeling, thereby contributing to the field of personalization, recommender systems, content generation systems and so on. This work investigates approaches for modeling personality traits (based on the Five Factor Modeling approach) of users based on a collection of images they tag as ‘favorite’ on Flickr. It presents several insights for improving the personality estimation performance by proposing better features and modeling approaches. The efficacy of the improved personality modeling approach is demonstrated by its use in an image recommendation system with promising results.

Modelling the influence of personality and culture on affect and enjoyment in multimedia

Affect is evoked through an intricate relationship between the characteristics of stimuli, individuals, and systems of perception. While affect is widely researched, few studies consider the combination of multimedia system characteristics and human factors together. As such, this paper explores tpersonality (Five-Factor Model) and cultural traits (Hofstede Model) on the intensity of multimedia-evoked positive and negative affects (emotions). A set of 144 video sequences (from 12 short movie clips) were evaluated by 114 participants from a cross-cultural population, producing 1232 ratings. On this data, threehe influence of personality (Five-Factor Model) and cultural traits (Hofstede Model) on the intensity of multimedia-evoked positive and negative affects (emotions). A set of 144 video sequences (from 12 short movie clips) were evaluated by 114 participants from a cross-cultural population, producing 1232 ratings. On this data, three multilevel regression models are compared: a baseline model that only considers system factors; an extended model that includes personality and culture; and an optimistic model in which each participant is modelled. An analysis shows that personal and cultural traits represent 5.6% of the variance in positive affect and 13.6% of the variance in negative affect. In addition, the affect-enjoyment correlation varied across the clips. This suggests that personality and culture play a key role in predicting the intensity of negative affect and whether or not it is enjoyed, but a more sophisticated set of predictors is needed to model positive affect with the same efficacy.

The CP-QAE-I: A video dataset for exploring the effect of personality and culture on perceived quality and affect in multimedia

Perception of quality and affect are subjective, driven by a complex interplay between system and human factors. Is it, however, possible to model these factors to predict subjective perception? To pursue this question, broader collaboration is needed to sample all aspects of personality, culture, and other human factors. Thus, an appropriate dataset is needed to integrate such efforts. Here, the CP-QAE-I is proposed. This is a video dataset containing 144 video sequences based on 12 short movie clips. These vary by: frame rate; frame dimension; bit-rate; and affect. An evaluation by 76 participants drawn from the United Kingdom, Singapore, India, and China suggests adequate distinction between the video sequences in terms of perceived quality as well as positive and negative affect. Nationality also emerged as a significant predictor, supporting the rationale for further study. By sharing the dataset, this paper aims to promote work modeling human factors in multimedia perception.

Modelling Human Factors in Perceptual Multimedia Quality: On The Role of Personality and Culture

Perception of multimedia quality is shaped by a rich interplay between system, context and human factors. While system and context factors are widely researched, few studies consider human factors as sources of systematic variance. This paper presents an analysis on the influence of personality and cultural traits on the perception of multimedia quality. A set of 144 video sequences (from 12 short movie excerpts) were rated by 114 participants from a cross-cultural population, producing 1232 ratings. On this data, three models are compared: a baseline model that only considers system factors; an extended model that includes personality and culture as human factors; and an optimistic model in which each participant is modelled as a random effect. An analysis shows that personality and cultural traits represent 9.3\% of the variance attributable to human factors while human factors overall predict an equal or higher proportion of variance compared to system factors. In addition, the quality-enjoyment correlation varied across the excerpts. This suggests that human factors play an important role in perceptual multimedia quality, but further research to explore moderation effects and a broader range of human factors is warranted.

Latent Factor Representations for Cold-Start Video Recommendation

Recommending items that have rarely/never been viewed by users is a bottleneck for collaborative filtering (CF) based recommendation algorithms. To alleviate this problem, item content representation (mostly in textual form) has been used as auxiliary information for learning latent factor representations. In this work we present a novel method for learning latent factor representation for videos based on modelling the emotional connection between user and item. First of all we present a comparative analysis of state-of-the art emotion modelling approaches that brings out a surprising finding regarding the efficacy of latent factor representations in modelling emotion in video content. Based on this finding we present a method visual-CLiMF for learning latent factor representations for cold start videos based on implicit feedback. Visual-CLiMF is based on the popular collaborative less-is-more approach but demonstrates how emotional aspects of items could be used as auxiliary information to improve MRR performance. Experiments on a new data set and the Amazon products data set demonstrate the effectiveness of visual-CLiMF which outperforms existing CF methods with or without content information.

Studying Personality through the Content of Posted and Liked Images on Twitter

Interacting with images through social media has become widespread due to ubiquitous Internet access and multimedia enabled devices. Through images, users generally present their daily activities, preferences or interests. This study aims to identify the way and extent to which personality differences, measured using the Big Five model, are related to online image posting and liking. In two experiments, the larger consisting of ~1.5 million Twitter images both posted and liked by ~4,000 users, we extract interpretable semantic concepts using large-scale image content analysis and analyze differences specific of each personality trait. Predictive results show that image content can predict personality traits, and that there can be significant performance gain by fusing the signal from both posted and liked images.

Do Personality and Culture Influence Perceived Video Quality and Enjoyment

The interplay between system, context, and human factors is important in perception of multimedia quality. However, studies on human factors are very limited in comparison to those for system and context factors. This article presents an attempt to explore the influence of personality and cultural traits on perception of multimedia quality. As a first step, a database consisting of 144 video sequences from 12 short movie excerpts has been assembled and rated by 114 participants from a cross-cultural population, thereby providing a useful ground-truth for this (as well as future) study. As a second step, three statistical models are compared: (i) a baseline model to only consider system factors; (ii) an extended model to include personality and culture; and (iii) an optimistic model in which each participant is modeled. As a third step, predictive models based on content, affect, system, and human factors are trained to generalize the statistical findings. As shown by statistical analysis, personality and cultural traits represent 9.3% of the variance attributable to human factors, and human factors overall predict an equal or higher proportion of variance compared to system factors. Moreover, the quality-enjoyment correlation varies across the excerpts. Predictive models trained by including human factors demonstrate about 3% and 9% improvement over models trained solely based on system factors for predicting perceived quality and enjoyment. As evidenced by this, human factors indeed are important in perceptual multimedia quality, but the results suggest further investigation of moderation effects and a broader range of human factors is necessary.

Who likes What, and Why? Insights into Personality Modeling based on Image `Likes'

The increased proliferation of data production technologies (e.g., cameras) and consumption avenues (e.g., social media) has led to images and videos being utilized by users to convey innate preferences and tastes. This has opened up the possibility of using multimedia as a source for user-modeling. This work attempts to model personality traits (based on the Five Factor Theory) of users using a collection of images they tag as ‘favorite’ (or like) on Flickr. First, a set of semantic features are proposed to be used for representing different concepts in images which influence users to like them. The addition of the proposed features led to improvement over state-of-the-art by 12 percent. Second, a novel machine learning approach is developed to model users’ personality based on the image features (resulting in upto 15 percent improvement). Third, efficacy of the semantic features and the modeling approach is shown in recommending images based on personality modeling. Using the modeling approach, recommendations are made regarding the factors that might influence users with different personality traits to like an image.

Deep Representations to Model User ‘Likes’

Automatically understanding and modeling a user’s liking for an image is a challenging problem. This is because the relationship between the images features (even semantic ones extracted by existing tools, viz. faces, objects etc.) and users’ ‘likes’ is non-linear, influenced by several subtle factors. This work presents a deep bi-modal knowledge representation of images based on their visual content and associated tags (text). A mapping step between the different levels of visual and textual representations allows for the transfer of semantic knowledge between the two modalities. It also includes feature selection before learning deep representation to identify the important features for a user to like an image. Then the proposed representation is shown to be effective in learning a model of users image ‘likes’ based on a collection of images ‘liked’ by him. On a collection of images ‘liked’ by users (from Flickr) the proposed deep representation is shown to better state-of-art low-level features used for modeling user ‘likes’ by around 15–20 %.