Journal of Intelligent & Fuzzy Systems - Volume 32, issue 4

Authors: Mishra, Kapil | Saharan, Ravi | Rathor, Bharti

Article Type: Research Article

Abstract: Exponentially increasing multimedia data over the internet raises concerns over its security. In the current work, we present a new technique for encrypting digital images based upon the pixel shuffling combined with changing pixel values using 128-bit secret key using henon chaotic map. Chaotic maps are characterized by their high sensitivity towards the initial parameters, which makes it a natural choice for developing a dynamic permutation matrix or as it is mostly called, permutation map. So we used chaotic Henon map in order to dynamically generate the permutation matrix. The initial parameters of the chaotic map and the secret key …for changing the pixel values are derived from an external secret key. Horizontal and vertical permutations are used to perform pixel shuffling. Shuffling is used to destroy the correlation among the neighbour or adjacent image pixels, also it helps in increasing diffusion in the image. The proposed scheme is tested against a series of tests to measure its performance. Results of such tests indicate that the proposed algorithm is highly sensitive towards the encryption key and showed high resistance against various brute-force or statistical attacks. Show more

Keywords: Image encryption, henon chaotic map, 128 bit secret key

DOI: 10.3233/JIFS-169231

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2885-2892, 2017

Authors: Gopal, | Srivastava, Shefali | Srivastava, Smriti

Article Type: Research Article

Abstract: In biometrics authentication systems, such as palmprint recognition, fingerprint recognition, dorsal hand vein recognition and palm vein recognition etc., image enhancement play a crucial role for most of the low resolution image samples. In this work, a novel adaptive histogram equalization (AHE) variant is proposed referred as effective area-AHE (EA-AHE) with weights. Here, global adaptive histogram equalization is improved using a local AHE technique by varying the effective area with different effective weights. The method is found to improve the biometric authentication identification rate as compared to the typical AHE. To validate the proposed algorithm, IITD palmprint databases of left …and right hand are used in the simulations. Finally, it is validated through results that proposed technique is superior to the existing ones. Show more

Keywords: Adaptive histogram equalization, effective area-AHE, biometrics

DOI: 10.3233/JIFS-169232

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2893-2899, 2017

Authors: Ashok, Aravind | Poornachandran, Prabaharan | Pal, Soumajit | Sankar, Prem | Surendran, K.

Article Type: Research Article

Abstract: Anomalous traffics are those unusual and colossal hits a non-popular domain gets for a small epoch period in a day. Regardless of whether these anomalies are malicious or not, it is important to analyze them as they might have a dramatic impact on a customer or an end user. Identifying these traffic anomalies is a challenge, as it requires mining and identifying patterns among huge volume of data. In this paper, we provide a statistical and dynamic reputation based approach to identify unpopular domains receiving huge volumes of traffic within a short period of time. Our aim is to develop …and deploy a lightweight framework in a monitored network capable of analyzing DNS traffic and provide early warning alerts regarding domains receiving unusual hits to reduce the collateral damage faced by an end–user or customer. The authors have employed statistical analysis, supervised learning and ensemble based dynamic reputation of domains, IP addresses and name servers to distinguish benign and abnormal domains with very low false positives. Show more

Keywords: Domain Name System, anomaly detection, knowledge base, hit analysis, dynamic reputation

DOI: 10.3233/JIFS-169233

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2901-2907, 2017

Authors: Mishra, Preeti | Pilli, Emmanuel S. | Varadharajan, Vijay | Tupakula, Udaya

Article Type: Research Article

Abstract: Cloud Security is of paramount importance in the new era of virtualization technology. Tenant Virtual Machine (VM) level security solutions can be easily evaded by modern attack techniques. Out-VM monitoring allows cloud administrator (CA) to monitor and control a VM from a secure location outside the VM. In this paper, we propose an out-VM monitoring based approach named as ‘P rogram S emantic-Aware I ntrusion Detection at Net work and Hypervisor Layer’ (PSI-NetVisor ) to detect attacks in both network and virtualization layer in cloud. PSI-NetVisor performs network monitoring by employing behavior based intrusion detection approach (BIDA) at …the network layer of centralized Cloud Network Server (CNS); providing the first level of defense from attacks. It incorporates semantic awareness in the intrusion detection approach and enables it to provide network monitoring and process monitoring at the hypervisor layer of Cloud Compute Server (CCoS); providing the second level of defense from attacks. PSI-NetVisor employs Virtual Machine Introspection (VMI) libraries based on software break point injection to extract process execution traces from hypervisor. It further applies depth first search (DFS) to construct program semantics from control flow graph of execution traces. It applies dynamic analysis and machine learning approaches to learn the behavior of anomalies which makes it secure from obfuscation and encryption based attacks. PSI-NetVisor has been validated with latest intrusion datasets (UNSW-NB & Evasive Malware) collected from research centers and results seem to be promising. Show more

Keywords: Intrusion detection, virtual machine introspection, system call flow graph, cloud security, Malware, network attacks

DOI: 10.3233/JIFS-169234

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2909-2921, 2017

Authors: Sharma, Lokesh Kumar | Mittal, Namita

Article Type: Research Article

Abstract: Question Answering (QA) research is a significant and challenging task in Natural Language Processing. QA aims to extract an exact answer from a relevant text snippet or a document. The motivation behind QA research is the need of user who is using state-of-the-art search engines. The user expects an exact answer rather than a list of documents that probably contain the answer. In this paper, we consider a particular issue of QA that is gathering and scoring answer evidence collected from relevant documents. The evidence is a text snippet in the large corpus which supports the answer. For Evidence Scoring …(ES) several efficient features and relations are required to extract for machine learning algorithm. These features include various lexical, syntactic and semantic features. Also, new structural features are extracted from the dependency features of the question and supported document. Experimental results show that structural features perform better, and accuracy is increased when these features are combined with other features. To score the evidence, for an existing question-answer pair, Logical Form Answer Candidate Scorer technique is used. Furthermore, an algorithm is designed for learning answer evidence. Show more

Keywords: Lexical feature, syntactic feature, semantic feature, evidence gathering, feature selection

DOI: 10.3233/JIFS-169235

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2923-2932, 2017

Authors: Milacic, Mitar | James, Alex Pappachen | Dimitrijev, Sima

Article Type: Research Article

Abstract: Automated processing and recognition of human speech commands under unconstrained and noisy recognition situations with a limited number of training samples is a challenging problem of interest to smart devices and systems. In practice, it is impossible to remove noise without losing class discriminative information in the speech signals. Also, any attempts to improve signal quality place an additional burden on the computational capacity in state-of-the-art speech command recognition systems. In this paper, we propose a low-level word processing system using mean-variance normalised frequency-time spectrograms and a new similarity measure that compensates for feature length mismatches such as those resulting …from pronunciation variations in speech segments. We find that padding a local similarity matrix with zero similarity values to disregard the effects of a mismatch in length of speech spectrograms results in improved word recognition accuracies and reduction in between class non-discriminative signals. As opposed to the state-of-the-art approaches in spectrogram comparisons such as DTW, the proposed method, when tested using the TIMIT database, shows improved recognition accuracies, robustness to noise, lower computational requirements, and scalability to large word problems. Show more

Keywords: Similarity measure, metric padding, word recognition, isolated words, speech recognition, mean-variance filters

DOI: 10.3233/JIFS-169236

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2933-2939, 2017

Authors: Menon, Remya R.K. | Joseph, Deepthy | Kaimal, M.R.

Article Type: Research Article

Abstract: Maintaining large collection of documents is an important problem in many areas of science and industry. Different analysis can be performed on large document collection with ease only if a short or reduced description can be obtained. Topic modeling offers a promising solution for this. Topic modeling is a method that learns about hidden themes from a large set of unorganized documents. Different approaches and alternatives are available for finding topics, such as Latent Dirichlet Allocation (LDA), neural networks, Latent Semantic Analysis (LSA), probabilistic LSA (pLSA), probabilistic LDA (pLDA). In topic models the topics inferred are based only on observing …the term occurrence. However, the terms may not be semantically related in a manner that is relevant to the topic. Understanding the semantics can yield improved topics for representing the documents. The objective of this paper is to develop a semantically oriented probabilistic model based approach for generating topic representation from the document collection. From the modified topic model, we generate 2 matrices- a document-topic and a term-topic matrix. The reduced document-term matrix derived from these two matrices has 85% similarity with the original document-term matrix i.e. we get 85% similarity between the original document collection and the documents reconstructed from the above two matrices. Also, a classifier when applied to the document-topic matrix appended with the class label, shows an 80% improvement in F-measure score. The paper also uses the perplexity metric to find out the number of topics for a test set. Show more

Keywords: LDA, LSA, Singular Value Decomposition (SVD), probabilistic model, vector space model

DOI: 10.3233/JIFS-169237

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2941-2951, 2017

Authors: Akhtar, Nadeem | Siddique, Bushra

Article Type: Research Article

Abstract: In the near past, microblogging services like Twitter have gained immense popularity. The vast breadth of user base is responsible for generating information on diverse aspects ranging from product launch to sports match. However, due to the exponentially increasing number of users on Twitter platform, the volume of content generated is tremendously high. In this paper we address the information overload problem of the Twitter and present a framework for event detection with hierarchical visualization specifically for sports events. We propose a novel Event Tree algorithm which detects and generates a hierarchy of events through recursive hierarchical clustering. The different …levels of the hierarchy represent the events at different granularities of time and thus offer dual advantages. Firstly, it takes care of the users with varied level of interest in the particular sports event. Secondly, the users may get finer details for specific segments of the sport holdings as per their appeal. We test and report results of our framework for the Indian Premier League Twenty20 2016 season cricket match dataset. Show more

Keywords: Microblogging services, event detection, hierarchical clustering

DOI: 10.3233/JIFS-169238

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2953-2961, 2017

Authors: Ramkumar, N. | Venkat Rangan, P. | Gopalakrishnan, Uma | Hariharan, Balaji

Article Type: Research Article

Abstract: Current eLearning systems enable streaming of live lectures to distant students facilitating a live instructor-student interaction. However, studies have shown that there exists a marked divide in local students’ (student present in the teacher’s location) experience as compared to distant students’. One of the major factors attributing to this rift is lack of gaze aligned interaction. In this paper, we present a system architecture that receives gesture triggers as input, and dynamically calculates the perspective angle to be captured of the speaking participant, for the listener, facilitating eye contact. The gesture triggers are calculated using Microsoft Kinect sensor which extracts …skeleton joint information of the instructor, and performs gesture recognition with the acquired joint information real-time. This serves as interaction-initiation triggers for dynamic perspective correction for gaze alignment during a conversation. For evaluation, we constructed a five classroom test-bed with dynamic perspective correction and user study results indicate a marked 42% enhancement in experience with the gaze correction in place. Show more

Keywords: Gaze correction, eye contact, gesture recognition, video streaming, eLearning

DOI: 10.3233/JIFS-169239

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2963-2969, 2017

Authors: Sreedasyam, Rachita | Rao, Aishwarya | Sachidanandan, Nidhi | Sampath, Nalini | Vasudevan, Shriram K.

Article Type: Research Article

Abstract: Autism Spectrum Disorder (ASD) is defined as a condition or disorder that begins in childhood and that causes problems in establishing relationships and communicating with other people. Aarya works as a personal well-being companion to children with Autism Spectrum Disorder while they interact with a virtual environment that is gesture based. By making an ASD affected child face real world situations, we try to improve his/her confidence in facing the world and being open to learning various skills. Social interaction and communication are the major challenges faced by children with ASD. In Aarya, we use gesture-based interface that …is the Microsoft Kinect so that the child can find it easier to interact in the real world environment. Through the interactions made with the children and the results obtained, we understand that this tool can be a companion while giving chance for growth and improving their interacting ability. With further refinement and expert inputs, this tool can be built better. Show more

Keywords: Technology for autism, autism, Kinect2Scratch, Kinect, social interaction

DOI: 10.3233/JIFS-169240

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2971-2976, 2017