Journal of Intelligent & Fuzzy Systems - Volume 34, issue 3

Authors: Vinayakumar, R. | Soman, K.P. | Poornachandran, Prabaharan

Article Type: Research Article

Abstract: Malicious uniform resource locator (URL), termed as malicious website is a foundation mechanisms for many of internet criminal activities such as phishing, spamming, identity theft, financial fraud and malware. It has been considered as a common and serious threat to the Cybersecurity. Blacklisting mechanism and many machine learning based solutions found by researchers with the aim to effectively signalize and classify the malicious URL’s in internet. Blacklisting is completely ineffective at finding both variations of malicious URL or newly generated URL. Additionally, it requires human input and ends up as a time consuming approach in real-time scenarios. Machine learning based …solutions implicitly rely on feature engineering phase to extract hand crafted features including linguistic, lexical, contextual or semantics, statistical information of URL string, n-gram, bag-of-words, link structures, content composition, DNS information, network traffic, etc. As a result feature engineering in machine learning based solutions has to evolve with the new malicious URL’s. In recent times, deep learning is the most talked due to the significant results in various artificial intelligence (AI) tasks in the field of image processing, speech processing, natural language processing and many others. They have an ability to extract features automatically by taking the raw input texts. To leverage this and to transform the efficacy of deep learning algorithms to the task of malicious URL’s detection, we evaluate various deep learning architectures specifically recurrent neural network (RNN), identity-recurrent neural network (I-RNN), long short-term memory (LSTM), convolution neural network (CNN), and convolutional neural network-long short-term memory (CNN-LSTM) architectures by modeling the real known benign and malicious URL’s in character level language. The optimal parameter for deep learning architecture is found by conducting various experiments with various configurations of network parameters and network structures. All the experiments run till 1000 epochs with a learning rate in the range [0.01-0.5]. In our experiments, deep learning mechanisms outperformed the hand crafted feature mechanism. Specifically, LSTM and hybrid network of CNN and LSTM have achieved highest accuracy as 0.9996 and 0.9995 respectively. This might be due to the fact that the deep learning mechanisms have ability to learn hierarchical feature representation and long range-dependencies in sequences of arbitrary length. Show more

Keywords: Malicious uniform resource locator (URL) or malicious website, deep learning mechanisms: Recurrent Neural Network (RNN), Identity-Recurrent Neural Network (I-RNN), Long Short-Term Memory (LSTM), Convolution Neural Network (CNN), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)

DOI: 10.3233/JIFS-169429

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1333-1343, 2018

Authors: Handa, Rohit | Rama Krishna, C. | Aggarwal, Naveen

Article Type: Research Article

Abstract: With the advancement of cloud computing, data-owners prefer to outsource their confidential data to cloud as it provides storage facility at reduced administration and maintenance cost. When these confidential documents leave the user’s premises, security of the data becomes a prime concern, as cloud service provider (CSP) and end users are in different trust domains. To provide data privacy, it is preferred that the end user should encrypt the data before outsourcing to the cloud. Encryption provides security but makes data utilization a challenging task, i.e., searching encrypted documents is difficult. Various schemes exist in the literature but they are …either restricted to single keyword search or are inefficient in terms of search time required. In this paper, we propose an efficient approach for secure information retrieval using the concept of bucketization. This reduces the average number of comparisons per query to sub-linear. Experimental analysis on Reuters-21578 data-set demonstrates that the proposed scheme provides a recall of 100% and precision of 98.45% while decreasing the search time by 98.85% using bucketization. Show more

Keywords: Privacy-preserving, multi-keyword, conjunctive search, bucketization, cloud storage

DOI: 10.3233/JIFS-169430

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1345-1353, 2018

Authors: Vinayakumar, R. | Soman, K.P. | Poornachandran, Prabaharan

Article Type: Research Article

Abstract: Threats related to computer security constantly evolving and attacking the networks and internet all the time. New security threats and the sophisticated methods that hackers use can bypass the detection and prevention mechanisms. A new approach which can handle and analyze massive amount of logs from diverse sources such as network packets, Domain name system (DNS) logs, proxy logs, system/service logs etc. required. This approach can be typically termed as big data. This approach can protect and provide solution to various security issues such as fraud detection, malicious activities and other advanced persistent threats. Apache spark is a distributed big …data based cluster computing platform which can store and process the security data to give real time protection. In this paper, we collect only DNS logs from client machines in local area network (LAN) and store it in a server. To find the domain name as either benign or malicious, we propose deep learning based approach. For comparison, we have evaluated the effectiveness of various deep learning approaches such as recurrent neural network (RNN), long short-term memory (LSTM) and other traditional machine learning classifiers. Deep learning based approaches have performed well in comparison to the other classical machine learning classifiers. The primary reason is that deep learning algorithms have the capability to obtain the right features implicitly. Moreover, LSTM has obtained highest malicious detection rate in all experiments in comparison to the other deep learning approaches. Show more

Keywords: Big data approach, log files, Apache Spark, Domain Name Service (DNS), machine learning, and deep learning: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM)

DOI: 10.3233/JIFS-169431

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1355-1367, 2018

Authors: Al-Utaibi, Khaled A. | El-Alfy, El-Sayed M.

Article Type: Research Article

Abstract: With the increasingly growing internal and external attacks on computer systems and online services, cybersecurity has become a vibrant research area. Countering intrusive attacks is a daunting task with no universal magic solution that can successfully handle all scenarios. A variety of machine-learning and computational intelligence techniques have been applied extensively to detect and classify these attacks. However, the effectiveness of these techniques greatly depends on the adopted data preprocessing methods for feature extraction and engineering. This paper presents an extended taxonomy of the work related to intrusion detection and reviews the state-of-the-art techniques for data preprocessing. It offers a …critical up-to-date survey which can be an instrumental pedagogy to help junior researchers conceive the vast amount of research work and gain a holistic view and awareness of various contemporary research directions in this domain. Show more

Keywords: Information systems, cybersecurity, intrusion detection, machine learning, computational intelligence, feature normalization, feature discretization, feature engineering, feature selection, dimensionality reduction

DOI: 10.3233/JIFS-169432

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1369-1383, 2018

Authors: Srivastava, Smriti | Gopal, | Bhardwaj, Saurabh

Article Type: Research Article

Abstract: The present work describes different research techniques for collecting and organizing speech database in different scenario at the institute and successfully structuring the text independent speaker identification database in Indian context. In order to get the Multi-Scenario dataset, each speaker performed multiple sessions recording in reading style with English and Hindi language with same passages but under different conditions. This work analyzed different scenario affecting the performance of speaker recognition system when tested under dissimilar training conditions. Here four different scenarios are considered; sensor and environment, language, aging and health. To study the effect of sensor, language and environment on …the performance of ASR system a database of 200 speaker was created. Under different environmental conditions, four different types of sensors in parallel configuration were used to study the sensor mismatch conditions over testing and training phase. The database contains speech samples of the individual in English and Hindi in read speech styles under two environment i.e. a controlled recording chamber and library. To study the aging effect, an aging NSIT speaker database (AG-NSIT-SD) of 53 famous personalities was collected from online source varying over a period of 10–20 years. Further to study the effect of health, a cough and cold NSIT speaker database (CC-NSIT-SD) of 38 speakers was also collected to study the performance of system. Apart from this, the effect of different noise types on the speaker identification was also studied on different sensors. Show more

Keywords: Speaker identification, speaker database, aging database, cough and cold database

DOI: 10.3233/JIFS-169433

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1385-1392, 2018

Authors: Ibrayev, Timur | Myrzakhan, Ulan | Krestinskaya, Olga | Irmanova, Aidana | James, Alex Pappachen

Article Type: Research Article

Abstract: Hierarchical Temporal Memory is a new machine learning algorithm intended to mimic the working principle of the neocortex, part of the human brain, responsible for learning, classification, and making predictions. Although many works illustrate its effectiveness as a software algorithm, hardware design for HTM remains an open research problem. Hence, this work proposes an architecture for HTM Spatial Pooler and Temporal Memory with learning mechanism, which creates a single image for each class based on important and unimportant features of all images in the training set. In turn, the reduction in the number of templates within database reduces the memory …requirements and increases the processing speed. Moreover, face recognition analysis indicates that for a large number of training images, the proposed design provides higher accuracy results (83.5%) compared to only Spatial Pooler design presented in the previous works. Show more

Keywords: HTM, temporal memory, spatial pooler, memristor, face recognition

DOI: 10.3233/JIFS-169434

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1393-1402, 2018

Authors: Roy, Sandip | Chatterjee, Santanu | Mahapatra, Gautam

Article Type: Research Article

Abstract: A well connected of network of smart devices that can be accessed through internet is broadly known as Internet of Things (IoT). IoT has diversified application exploiting various technologies that requires integration of computing devices with end users. The use of IoT provides efficient management large and heterogeneous assets, establishes a real time network for strong connectivity and set up useful coordination among various resources. For example, in defense system, IoT can be applicable in monitoring war fighter’s health, border area surveillance, proactive equipment maintenance etc. However, maintaining privacy and security through secure remote authentication is an essential prerequisite for …hazard-free use of IoT based services. In this paper, we provide an efficient and secured authentication protocol for remote IoT based services. Our proposed scheme does not exploit computation costly operation and avoids heavy storage in medical application server, thus making it suitable for battery limited devices. Finally, we perform the formal security verification using the widely accepted verification tool, called the ProVerif 1.93, to show that presented scheme is secure. Show more

Keywords: Remote user authentication, internet of things, biometrics, security, ProVerif 1.93

DOI: 10.3233/JIFS-169435

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1403-1410, 2018

Authors: Mohanraj, V. | Sibi Chakkaravarthy, S. | Gogul, I. | Sathiesh Kumar, V. | Kumar, Ranajit | Vaidehi, V.

Article Type: Research Article

Abstract: Face Recognition is widely used applications such as of mobile phone unlocking, credit card authentication and person authentication in airports. The face biometric authentication system can be easily spoofed by printed photograph, replay video of the legitimate user and 3D face mask. This paper proposes hybrid feature descriptors to detect the face spoofing attack (printed photograph and replay video attacks). The proposed method extracts three different feature descriptors such as Color moment, Haralick texture and Color Local Binary Pattern (CLBP) feature descriptors. The extracted features are concatenated and classified by Logistic Regression. The performance of the proposed method is evaluated …on the Michigan State University Mobile Face Spoofing Database (MSU-MFSD) dataset and found to achieve better results than state-of-the-art methods. Show more

Keywords: Face recognition, spoof detection, color moment, Local Binary Pattern (LBP), Haralick texture

DOI: 10.3233/JIFS-169436

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1411-1419, 2018

Authors: Sivakumar, Trivandrum T. | Nair, Shali S. | Zacharias, Geevar C. | Nair, Madhu S. | Joseph, Anna P.

Article Type: Research Article

Abstract: Biometric refers to the automatic identification of a person based on physiological or behavioural characteristics. Current modes of biometric systems are fingerprint, voice, face, signature, palm print, iris scan etc. The conventional biometric systems are unable to meet these authentication requirements as it can be forged. Hence, a novel biometric system which can overcome these limitations is proposed. Tongue is a unique vital organ which is well protected within the mouth and not affected by external factors. Dorsum of the tongue exhibits a great amount of information along with its visual differences in shape, texture and pattern which can be …called the tongue print. As tongue exhibits rich textural patterns, Local Binary Pattern (LBP) algorithm is used for extracting features. Extracted features are then trained by a linear Support Vector Machine (SVM) for personal identification. From the database consisting of 136 tongue print images of 34 individuals, we achieved an accuracy of 97.05% for identification. Our study is the first of its kind where texture patterns are extracted from tongue images using Local Binary Pattern for biometric authentication. We achieved a level of accuracy compared to the technique used in other studies. Show more

Keywords: Tongue print, biometric, identification, Local Binary Pattern, Support Vector Machine

DOI: 10.3233/JIFS-169437

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1421-1426, 2018

Authors: Sooraj, S. | Manjusha, K. | Anand Kumar, M. | Soman, K.P.

Article Type: Research Article

Abstract: Spell checking plays an important role in conveying correct information and hence helps in clear communication. Spell checkers for English language are well established. But in case of Indian languages, especially Malayalam lacks a well developed spell checker. The spell checkers that currently exist for Indian languages are based on traditional approaches such as rule based or dictionary based. The rich morphological nature of Malayalam makes spell checking a difficult task. The proposed work is a novel attempt and first of its kind that focuses on implementing a spell checker for Malayalam using deep learning. The spell checker comprises of …two processes: error detection and error correction. The error detection section employs a LSTM based neural network which is trained to identify the misspelled words and the position where the error has occurred. The error detection accuracy is measured using the F1 score. Error correction is achieved by the selecting the most probable word from the candidate word suggestions. Show more

Keywords: Malayalam, spell checker, deep learning, natural language processing, long short term memory

DOI: 10.3233/JIFS-169438

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1427-1434, 2018