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

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1235-1235, 2018

Authors: Thampi, Sabu M. | El-Alfy, El-Sayed M. | Mitra, Sushmita | Trajkovic, Ljiljana

Article Type: Editorial

DOI: 10.3233/JIFS-169420

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1237-1241, 2018

Authors: Vijayanand, R. | Devaraj, D. | Kannapiran, B.

Article Type: Research Article

Abstract: Intrusion detection is an important requirement in wireless mesh network and the intrusion detection system (IDS) provides security by monitoring data traffic in real time. This work proposes support vector machine (SVM) classifier to identify the intrusion in the network. The traffic data collected from the wireless mesh network (WMN) is given as input to the SVM. The irrelevant and redundant input variables increase the complexity of designing IDS and may degrade its performance. Hence, feature selection techniques, which select the relevant features from the original input is essential to improve the performance of IDS in WMN. In this work, …a hybrid genetic algorithm (GA) and mutual information (MI) based feature selection technique is proposed for IDS. The performance of IDS with the proposed feature selection technique is analyzed with IDS having mutual information, genetic algorithm and GA+MI based feature selection techniques using ADFA-LD dataset. Experimental results have demonstrated the effectiveness of proposed intrusion detection system with hybrid feature selection technique in wireless mesh network. The superiority of SVM classifier with hybrid feature selection technique is also verified by comparing with artificial neural network classifier. Show more

Keywords: IDS for WMN, SVM based IDS, ADFA-LD dataset, GA with MI technique, Hybrid feature selection

DOI: 10.3233/JIFS-169421

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1243-1250, 2018

Authors: Kumar, Malay | Vardhan, Manu

Article Type: Research Article

Abstract: Cloud computing offers an economical, convenient and elastic pool of computing resources over the internet. It enables computationally weak client to execute large computations by outsourcing their computation load to the cloud servers. However, outsourcing of data and computation to the third-party cloud servers bring multifarious security and privacy challenges that needed to be understood and address before the development of outsourcing algorithm. In this paper, the authors propose solutions for matrix-chain multiplication (MCM) problem. Our goal is to minimize the execution burden on the client without sacrificing the confidentiality and integrity of the input/output. Conventionally, the complexity of matrix-chain …multiplication is O  (n 3 ). After leveraging the facility of outsourcing, the client-side complexity reduces to O  (n 2 ). In the proposed algorithm, the client employs some efficient linear transformation schemes, which preserve the data confidentiality. It also developed a novel result verification scheme, which verifies the result with modest burden and high probability and maintain the integrity of computed result. The analytical analysis of algorithm depicted that the algorithm is simultaneously meeting the design goals of correctness, security, efficiency and verifiability. We conduct many experiments to validate the algorithm and demonstrate its practical usability. The algorithm is implemented on public cloud “Amazon EC2 ”, and found that the proposed outsource algorithm performs 11.655 times faster computation of matrix-chain multiplication than the direct implementation. Show more

Keywords: Matrix-chain multiplication, cloud computing, secure outsourcing, security, verifiability

DOI: 10.3233/JIFS-169422

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1251-1263, 2018

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

Article Type: Research Article

Abstract: In recent years, domain generation algorithms (DGAs) are the foundational mechanisms for many malware families. Mainly, due to the fact that DGA can generate immense number of pseudo random domain names to associate to a command and control (C2) infrastructures. This paper focuses on to detect and classify the pseudo random domain names without relying on the feature engineering or any other linguistic, contextual or semantics and statistical information by adopting deep learning approaches. A deep learning approach is a complex model of traditional machine learning mechanism that has received renewed interest by solving the long-standing tasks in artificial intelligence …(AI) related to the field of natural language processing, image recognition, speech processing and many others. They have immense capability to extract optimal feature representations by taking input as in the form of raw input texts. To leverage this and to transfer the performance enhancement in aforementioned areas towards characterize, detect and classify the DGA generated domain names to a specific malware family, this paper adopts deep learning mechanisms with a known one million benign domain names from Alexa, OpenDNS and a corpus of malicious domain names generated from 17 DGA malware families in real time for training in character and bigram level and a trained model has been evaluated on the OSNIT data set in real-time. Specifically, to understand the effectiveness of various deep learning mechanisms, we used 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. Additionally, to find out an optimal architecture, experiments are done with various configurations of network parameters and network structures. All experiments run up to 1000 epochs with a learning rate set in the range [0.01-0.5]. Overall, deep learning approaches, particularly family of recurrent neural network and a hybrid network (where the first layer is CNN and a subsequent layer is LSTM) have showed significant performance with a highest detection rate 0.9945 and 0.9879 respectively. The main reason is deep learning approaches have inherent mechanisms to capture hierarchical feature extraction and long range-dependencies in sequence inputs. Show more

Keywords: Domain generation algorithms (DGAs), deep learning mechanisms, recurrent neural network (RNN), identity-recurrent neural network (IRNN), long short-term memory (LSTM), convolution neural network (CNN), convolutional neural network-long short-term memory (CNN-LSTM)

DOI: 10.3233/JIFS-169423

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1265-1276, 2018

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

Article Type: Research Article

Abstract: Long Short-term Memory (LSTM) is a sub set of recurrent neural network (RNN) which is specifically used to train to learn long-term temporal dynamics with sequences of arbitrary length. In this paper, long short-term memory (LSTM) architecture is followed for Android malware detection. The data set for evaluation contains real known benign and malware applications from static and dynamic analysis. To achieve acceptable malware detection rates with low computational cost, various LSTM network topologies with several network parameters are used on all extracted features. A stacked LSTM with 32 memory blocks containing one cell each has performed well on detection …of all individual behaviors of malicious applications in comparison to other traditional static machine learning classifier. The architecture quantifies experimental results up to 1000 epochs with learning rate 0.1. This is primarily due to the reason that LSTM has the potential to store long-range dependencies across time-steps and to correlate with successive connection sequences information. The experiment achieved the Android malware detection of 0.939 on dynamic analysis and 0.975 on static analysis on well-known datasets. Show more

Keywords: Android malware detection: static and dynamic analysis, deep learning: recurrent neural network (RNN), Long Short-term Memory (LSTM)

DOI: 10.3233/JIFS-169424

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1277-1288, 2018

Authors: Madhawa, Surendar | Balakrishnan, P. | Arumugam, Umamakeswari

Article Type: Research Article

Abstract: Without an iota of doubt, security, safety, and privacy are the most critical aspects of any Industrial Internet of Things (IIoT) environment. Among the existing intrusion detection methods, knowledge-based methods discover only the recognized attacks, the behavior-based methods suffer from high false positives, and specification-based methods demand the complete knowledge about the elements present in the IIoT environment. Examining the heterogeneous data from different and distributed sensors and sending the correct commands to actuators are vital to the increasingly industrialized economy. This work proposes an Intrusion Detection System (IDS) for the IIoT environment that combines both the anomaly and specification-based …approaches. The resulting system overcomes the limitations of the contemporary techniques by detecting unidentified attacks. All kinds of data emanating from any IIoT setup comprising sensors and actuators are logged, and specification rules are constructed from it. Any violations of the created rules are treated as attacks. The validation is carried out through simulation using the Mininet tool with the dataset obtained from the real-world water treatment facility at the Singapore University of Technology and Design (SUTD). The results show only 3.2% of false positives with the detection rate of 96.4%. Show more

Keywords: Industrial internet of things, software defined networking, intrusion detection, specification

DOI: 10.3233/JIFS-169425

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1289-1300, 2018

Authors: Nangrani, S.P. | Bhat, S.S.

Article Type: Research Article

Abstract: Smart grid networks to deliver power, are futuristic well planned, designed and managed systems in real time. The potential threat to the dynamic security of such systems is chaotic nature of real power flow on transmission lines. Smart grid needs to be secure in static and dynamic sense under contingent conditions. At present static security assessment is based on monitoring of conventional mega-watt performance index along with other voltage and current related performance indices. This paper proposes an intelligent technique based on novel interleaved performance index. Proposed technique uses interleaving of two features. One feature is related to computation of …Lyapunov Exponent for chaotic behavior related to the divergent trend of power flow increase and other related to conventional megawatt performance index for overload monitoring. Novel proposed index is named as interleaved Mega Watt-Lyapunov Exponent performance index. Chaos quantification and overload of real power flow on transmission lines, both can be evaluated using proposed index. This paper presents formulation and computation of novel proposition for smart grids. Benchmark model with computed values of novel performance index under different contingencies and load profiles along with a comparison to conventional index, is demonstrated and discussed at length in the paper. Show more

Keywords: Discrete Lyapunov Exponent (DLE), Mega Watt Performance Index (MW-PI), Security monitoring

DOI: 10.3233/JIFS-169426

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1301-1310, 2018

Authors: Mathi, Senthilkumar | Srilakshmy,

Article Type: Research Article

Abstract: The mobile Internet Protocol (IP) is a mobility based communication protocol that provides guidelines for the routing of mobile nodes in a network. The mobile IP manipulates IP addresses as a natural identifier for each mobile communicant. Here, each mobile device recognizes itself via two IP addresses: a home-of address and a care-of address . Owing to the mobility nature of the these devices, the location update of their current care-of address plays a vital role in receiving continuous services without interference. Many investigations have been explored on the location update of mobile devices along with the security and …computation issues. However, the efforts on the security services have not received much attention in these investigations. Consequently, there is an increasing need for optimized binding update that balances security and efficiency. In this paper, a new Binding Update using Twofold Encryption (BUTE) is proposed, for balancing both security and efficiency of binding update for IPv6 mobility. It exhibits the alleviation of the attacks such as rerun, man-in-the-middle, false binding update and denial-of-service. The proposed BUTE is simulated using network simulator-2 and the experimental results are analyzed. Also, it is validated for security attributes using Automated Validation of Internet Security Protocols and Applications (AVISPA) – a security tool. Finally, the numerical results reveal that the proposed BUTE provides a significant reduction in communication cost and binding update delays. Show more

Keywords: Binding update, asymmetric encryption, authentication, return routability, message latency, tunneling

DOI: 10.3233/JIFS-169427

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1311-1322, 2018

Authors: Ahmad, Musheer | Alam, Mohammad Zaiyan | Ansari, Subia | Lambić, Dragan | AlSharari, Hamed D.

Article Type: Research Article

Abstract: Recently, a color image encryption algorithm is suggested by Lakshmanan et al. in [IEEE Transactions on Neural Networks and Learning Systems 2016, doi: 10.1109/TNNLS.2016.2619345]. In encryption algorithm, a piece-wise linear chaotic map is adopted to create a permutation matrix to perform shuffling of pixels of plain-image. The encryption of shuffled image is proceeded by extracting keystreams from chaotic inertial delayed neural network. This paper evaluates the security of encryption algorithm to unveil its inherent defects and proposes to present a complete cryptanalysis. To demonstrate the break of algorithm, we apply proposed chosen-plaintext attack that successfully recovers the exact plain-image …from encrypted image without secret key. The proposed cryptanalysis shows that the encryption algorithm is inapt to realize a secure communication, the security claims by authors are not valid as the algorithm has serious security flaws and susceptible to proposed attack. Show more

Keywords: Cryptanalysis, image encryption, chaotic inertial neural network, permutation matrix, chosen-plaintext attack

DOI: 10.3233/JIFS-169428

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1323-1332, 2018