Статьи журнала - International Journal of Computer Network and Information Security
Все статьи: 1110
A chaotic cryptosystem using conjugate transcendental fractal function
Статья научная
A cryptosystem designed by using the combined features of fractal function and chaotic map, provides a secure and real time encryption environment. In this paper, a 2D-chaotic map is employed to create a chaotic key sequence to comply with the requirement of the key sensitivity. The set of initial values of the chaotic map has derived by iterating a conjugate transcendental fractal function (CTFF) i.e. z_(n+1)=conj(sin(z_n^2 ) )+c. The fractal function produced three sets of initial values after iterating it using Picard, Mann, and Ishikawa iteration methods. Resultantly, three chaotic key sequences will be generated by executing 2D Sine Tent composite map (2D-STCM) for each set of initial values. Afterwards, perform zigzag scanning to each key stream to decorrelate the adjacent image pixels and combined them using XOR operation. By using a different summation of plain image pixels for each pixel encryption, improves the cryptosystem resistant against known/chosen-plaintext attack. Moreover, an encryption of a plain image pixel achieved using corresponding key sequence pixel and a previously ciphered pixel value. The proposed encryption/decryption scheme is evaluated using key space analysis, key sensitivity analysis, differential analysis and other statistical analyses. The performance result indicates the given scheme is efficient and reliable to be used with great potential for a secure image transmission application.
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A classification framework to detect DoS attacks
Статья научная
The exponent increase in the use of online information systems triggered the demand of secure networks so that any intrusion can be detected and aborted. Intrusion detection is considered as one of the emerging research areas now days. This paper presents a machine learning based classification framework to detect the Denial of Service (DoS) attacks. The framework consists of five stages, including: 1) selection of the relevant Dataset, 2) Data pre-processing, 3) Feature Selection, 4) Detection, and 5) reflection of Results. The feature selection stage incudes the Decision Tree (DT) classifier as subset evaluator with four well known selection techniques including: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Best First (BF), and Rank Search (RS). Moreover, for detection, Decision Tree (DT) is used with bagging technique. Proposed framework is compared with 10 widely used classification techniques including Naïve Bayes (NB), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (kNN), Decision Tree (DT), Radial Basis Function (RBF), One Rule (OneR), PART, Bayesian Network (BN) and Random Tree (RT). A part of NSL-KDD dataset related to Denial of Service attack is used for experiments and performance is evaluated by using various accuracy measures including: Precision, Recall, F measure, FP rate, Accuracy, MCC, and ROC. The results reflected that the proposed framework outperformed all other classifiers.
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A combined TCP-friendly rate control with WFQ approach for congestion control for MANET
Статья научная
Congestion control techniques are extensively used to avoid congestion over the wireless network. But these techniques are incapability of to handle the increased utilization of the various application which raising high congestion and packet loss over the network and causing inconvenient to different services. The TCP-friendly rate control (TFRC) protocol is primarily considered to describe the effective and finest potential provision for such applications which is following it preeminent in the wired and wireless environment. But it also suffers due to slow start and time-consuming process which required several round-trip-time (RTT) to reach an optimal level of the communication rate. As the TRFC transmission rate is highly affected by the increase RTTs and this results in an raise in the packet loss and a corresponding significant decrease in the throughput. In this paper, we propose an integrated TFRC with weighted fair queue (WFQ) approach to overcoming the congestion and minimize the RTTs. The WFQ mechanism manages the incoming heavy traffic to ease the data rate control for smooth data flow to improve throughput. The simulation evaluation of the approach shows an improvisation in throughput with the low delay in different data flow conditions.
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Статья научная
This paper will present several research results evaluating the performance of ContikiMAC and XMAC protocols in data collection application with the RPL routing protocol. Simulation results show that ContikiMAC protocol gets better efficiency compared with XMAC protocol in both successful data delivery ratio and average energy consumption in the network. ContikiMAC protocol also performs well in high-density network condition. Meanwhile, successful data delivery ratio of XMAC protocol significantly reduced when the network density increases. The evaluating simulation results in this paper are an important basis for scientists to continue developing applications for wireless sensor networks in the future.
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A comparative study of recent steganography techniques for multiple image formats
Статья научная
Steganography is the technique for exchanging concealed secret information in a way to avoid suspicion. The aim of Steganography is to transfer secrete message to another party by hiding the data in a cover object, so that the imposter who monitors the traffic should not distinguish between genuine secret message and the cover object. This paper presents the comparative study and performance analysis of different image Steganography methods using various types of cover media ((like BMP/JPEG/PNG etc.) with the discussion of their file formats. We also discuss the embedding domains along with a discussion on salient technical properties, applications, limitations, and Steganalysis.
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A comprehensive review of congestion control techniques in M2M and cellular networks
Статья научная
Long Term Evolution (LTE) emerges as the promising communication technology for data and voice service in Human to Human Communication (H2H). Currently, LTE network also includes Machine to Machine (M2M) communication. Combining the legacy systems and evolving Machine type devices, increases the access rate to a particular base station leading to congestion in Access network. Congestion in Random Access Network (RAN) that occurs during the Random Access Process affects the performance of H2H communication. The objective of the paper is to analyze the existing congestion control techniques and to provide a comprehensive summary on classified poll and event based mechanisms.
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A critical appraisal on password based authentication
Статья научная
There is no doubt that, even after the development of many other authentication schemes, passwords remain one of the most popular means of authentication. A review in the field of password based authentication is addressed, by introducing and analyzing different schemes of authentication, respective advantages and disadvantages, and probable causes of the ‘very disconnect’ between user and password mechanisms. The evolution of passwords and how they have deep-rooted in our life is remarkable. This paper addresses the gap between the user and industry perspectives of password authentication, the state of art of password authentication and how the most investigated topic in password authentication changed over time. The author’s tries to distinguish password based authentication into two levels ‘User Centric Design Level’ and the ‘Machine Centric Protocol Level’ under one framework. The paper concludes with the special section covering the ways in which password based authentication system can be strengthened on the issues which are currently holding-in the password based authentication.
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A fault-tolerant improved OLSR protocol using k-Connected m-Dominating set
Статья научная
The inherent properties of ad hoc networks such as limited energy, short transmission range and absence of routers along with node mobility, node failures and link failures make routing a challenging task. In order to facilitate routing, virtual backbone has been proposed as a viable solution in the literature. Optimize Link State Routing (OLSR) protocol, a proactive routing protocol, uses Multipoint Relay (MPR) set to construct virtual backbone. Prior research has, however, identified various issues with the MPR selection scheme that needs improvement. One of the alternatives that could be used to construct virtual backbone is Connected Dominating Set (CDS). Although CDS generates a smaller virtual backbone, its 1-connected 1-domination nature may render a virtual backbone obsolete in case of networks which witness frequent node mobility, node failures and link failures. To overcome this, k-Connected m-Dominating CDS (kmCDS) could be used to construct fault- tolerant virtual backbone structure. In this direction, the present paper proposes a Fault-Tolerant Improved Optimized Link State Routing (FT-IOLSR) protocol that uses kmCDS to form fault-tolerant virtual backbone, effectively replacing the MPR set of OLSR protocol. Simulations are carried out to assess the performance of the FT-IOLSR protocol in relation to the OLSR protocol, with respect to various node speed and pause time combinations, and varying network size. The results show that the FT-IOLSR protocol is better in terms of packet delivery ratio under varying mobility and varying network size. Also it has been observed that, with increase in k-connectivity and m-domination factor, there is improvement in the performance of the protocol.
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A feed-forward and pattern recognition ANN model for network intrusion detection
Статья научная
Network security is an essential element in the day-to-day IT operations of nearly every organization in business. Securing a computer network means considering the threats and vulnerabilities and arrange the countermeasures. Network security threats are increasing rapidly and making wireless network and internet services unreliable and insecure. Intrusion Detection System plays a protective role in shielding a network from potential intrusions. In this research paper, Feed Forward Neural Network and Pattern Recognition Neural Network are designed and tested for the detection of various attacks by using modified KDD Cup99 dataset. In our proposed models, Bayesian Regularization and Scaled Conjugate Gradient, training functions are used to train the Artificial Neural Networks. Various performance measures such as Accuracy, MCC, R-squared, MSE, DR, FAR and AROC are used to evaluate the performance of proposed Neural Network Models. The results have shown that both the models have outperformed each other in different performance measures on different attack detections.
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A lightweight Data Exchange Format for Mobile Transactions
Статья научная
XML and JSON are commonly used data exchange formats that are widely in use in wireless networking environments. The verbose and redundant nature of XML documents incurs huge transportation overheads in data communications. JSON is a data format that reduces the document size; but its scope is confined to text and numeric data. Also due to the reasons such as lack of schema and limited interoperability features, JSON is more suitable for web based applications, compared to wireless or mobile environments. Since the literature reports serious concerns about the performance of existing data exchange formats in resource constraint networks, there is scope for a lightweight data exchange mechanism. This paper introduces a new lightweight, schema aware data exchange format for data representation and interchange. The proposed format, called LXML, is schema aware and non-binary format based on the XML standards and has the potential to be an alternative format for XML and JSON in a wireless environment. Experimental findings indicate that LXML is a less verbose and efficient data exchange format and its performance is found to be better than the existing non binary data exchange formats.
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A mobile application of augmented reality for aircraft maintenance of fan cowl door opening
Статья научная
Maintenance error such as failure of fan cowl door opening is one of the causes of aircraft accidents. In this research, we aim to develop an augmented reality application that allows the animation of each maintenance procedure of fan cowl door opening to be superimposed on the fan cowl door of aircraft. The marker detectability of the augmented reality application is checked based on different camera angles and distances. Hence, by using the developed application, the aircraft maintenance technician can interpret the information of the fan cowl door opening procedure in the form of texts, three-dimensional models and animations.
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A multi QoS genetic-based adaptive routing in wireless mesh networks with pareto solutions
Статья научная
Wireless Mesh Networks(WMN) is an active research topic for wireless networks designers and researchers. Routing has been studied in the last two decades in the field of optimization due to various applications in WMN. In this paper, Adaptive Genetic Algorithm (AGA) for identifying the shortest path in WMN satisfying multi- QoS measure is introduced. The proposed algorithm is adaptive in the sense that it uses various selection methods during the reproduction process and the one with the best multi- QoS measure is adopted in that generation. The multi-objective QoS measure defined as the combination of the minimum number of hops, minimum delay, and maximum bandwidth. The multi-objective optimization has been formulated and solved using weighted sum approach with Pareto optimal solution techniques. The simulation experiments have been carried out in MATLAB environment with a wireless network modeled as weighted graph of fifty nodes and node coverage equals to 200 meter, and the outcomes demonstrated that the proposed AGA performs well and finds the shortest route of the WMN proficiently, rapidly, and adapts to the dynamic nature of the wireless network and satisfying all of the constraints and objective measures imposed on the networks.
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A multi-agent system-based method of detecting DDoS attacks
Статья научная
Distributed denial of service attacks are the acts aiming at the exhaustion of the limited service resources within a target host and leading to the rejection of the valid user service request. During a DDoS attack, the target host is attacked by multiple, coordinated attack programs, often with disastrous results. Therefore, the effective detection, identification, treatment, and prevention of DDoS attacks are of great significance. Based on the research of DDoS attack principles, features and methods, combined with the possible scenarios of DDoS attacks, a Multi-Agent System-based DDoS attack detection method is proposed in this paper to implement DDoS attack detection for high-load communication scenarios. In this paper, we take the multi-layer communication protocols into consideration to carry out categorizing and analyzing DDoS attacks. Especially given the high-load communication scenarios, we make an effort to exploring a possible DDoS attack detection method with employing a target-driven multi-agent modeling methodology to detect DDoS attacks relying on considering the inherent characteristics of DDoS attacks. According to the experiments verification, the proposed DDoS attack detection method plays a better detection performance and is less relevant with the data unit granularity. Meanwhile, the method can effectively detect the target attacks after the sample training. The detection scheme based on the agent technology can reasonably perform the pre-set behaviors and with good scalability to meet the follow-further requirements of designing and implementing the prototype software.
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A new Immunity Intrusion Detection Model Based on Genetic Algorithm and Vaccine Mechanism
Статья научная
After analyzing the characteristics of Immunity Intrusion Detection System, by utilizing prominent characteristics of genetic algorithm and vaccine mechanism, a new hybird immunity intrusion detection model based on genetic algorithm and vaccine mechanism was established. The modeling process is described in detail, such as feature extraction of vaccine, genetic operates to memory detectors and the improvement for detection method. Via application vaccine mechanism into intrusion detection system, the new model has the function of misuse detection and anomaly detection simultaneously. In order to improve the detection matching efficiency, we also present a novel matching algorithm RBNDM. Finally, we evaluated our model using the KDD Cup 1999 Data set. The experiments show that this model can increase the true positive rate of the IDS.
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A new approach for data hiding based on pixel pairs and chaotic map
Статья научная
In this paper, a new data hiding algorithm based on pixel pairs using chaotic map is proposed. Data hiding scheme is created by applying modulo function to pixel pairs. In here, pseudo random number generator (PRNG) is obtained from chaotic maps. The PRNG is very important for this algorithm since the data hiding coefficients are chosen by PRNG. For example, if the coefficient is 0, subtraction operator is used between pixel pairs. If coefficient is 1, summary operator is used for selected pixel pairs. The proposed algorithm is evaluated by embedding different sized secret data into different test images. This method is compared with the determined studies in the literature and the obtained results is evaluated. In this study, special rules are also defined to pixels which have boundary values for resolve overflow/underflow problem. Minimal changes are performed to reach the desired value of the pixel values. According to the results obtained, the proposed algorithm has high visual quality, good running time, secure and high payload capacity.
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A new classification based model for malicious PE files detection
Статья научная
Malware presents a major threat to the security of computer systems, smart devices, and applications. It can also endanger sensitive data by modifying or destroying them. Thus, electronic exchanges through different communicating entities can be compromised. However, currently used signature-based methods cannot provide accurate detection of zero-day attacks, polymorphic and metamorphic programs which have the ability to change their code during propagation. In order to solve this issue, static and dynamic malware analysis is being used along with machine learning algorithms for malware detection and classification. Machine learning methods play an important role in automated malware detection. Several approaches have been applied to classify and to detect malware. The most challenging task is selecting a rele-vant set of features from a large dataset so that the classification model can be built in less time with higher accuracy. The purpose of this work is firstly to make a general review on the existing classification and detection methods, and secondly to develop an automated system to detect malicious Portable Executable files based on their headers with low performance and more efficiency. Experimental results will be presented for the best classifier selected in this study, namely Random Forest; accuracy and time performance will be discussed.
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A novel approach to thwart security attacks on mobile pattern authentication systems
Статья научная
Providing security to mobile devices by means of password authentication using robust cryptographic techniques is vitally important today, because they protect sensitive data. Especially for pattern locking systems of Android, there is a lack of security awareness in the people about various pre-computation attacks such as dictionary attacks, rainbow tables and brute-forcing. Hash functions such as SHA-1 are not secure for pattern authentication, because they suffer from dictionary attacks. The latest OS versions of Android such as Marshmallow make use of salted hash functions for pattern locks, but they do need additional hardware support such as TEE (Trusted Execution Environment) and a Gatekeeper function. If random salts are used for pattern passwords, they are also vulnerable, because the stored salt may be compromised and consequently the passwords can be speculated using brute-forcing. To avoid such a security breaches on pattern passwords, many methodologies have been proposed so far such as an elliptic curve based salt generation techniques. But security is never easy to obtain 100%. The attacker may perform brute-forcing successfully on pattern password hashes by gaining some information about the application. Brute-forcing becomes harder always by using longer salts and passwords and by stretching the execution time of hash generation. Therefore the current research addresses these difficulties and finds a solution to these problems by extending the existing salt generation scheme, by generating a dynamic 128-bit pepper (or a long salt) value for SHA-1 hashes to avoid such attacks without using an added hardware, for mobile computers using elliptic curves. The current scheme employs genetic algorithms to generate the pepper and finally makes brute-forcing even harder for the cryptanalysts. A comparison of this new hashing technique, with the existing techniques such as SHA-1 and salted SHA-1 with respect to brute-force analysis, Strict Avalanche Criterion and execution times is also presented in this paper.
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A novel infrared (IR) based sensor system for human presence detection in targeted locations
Статья научная
Human presence detection is a continuously sought of an issue by the scientific community. Visual camera-based technologies have emerged recently with low cost and easy usage. However, these technologies have been increased the user privacy issues. Hence it is highly essential to design a human detection system without compromising the user privacy, comfort, cost and easy deployment. The pyroelectric infrared (PIR) based sensor systems are introduced however this technology is incapable to detect the presence of stationary human because it can detect the fluctuating signals only. In this paper, we have proposed a novel infrared (IR) based sensor system to detect the human presence either mobile or immobile in targeted locations with high accuracy. The proposed infrared (IR) sensor is designed to sense the heat radiation emitted by the human body, it detects the human presence accurately in targeted locations. The proposed IR based sensor system has successfully deployed in a targeted location and tested successfully for detecting the human presence and also other objects.
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A novel scheme for isolation of distributed denial of service attack in VANETs
Статья научная
A network in which the vehicular nodes are free to join or leave the network is known as vehicular ad hoc network (VANET). Either vehicle to vehicle or vehicle to infrastructure types of communication is performed in this decentralized type of network. The identification and elimination of Distributed-Denial of Service (DDoS) attacks from VANETs is the major objective of this research. The nodes that can flood victim nodes with large numbers of rough packets are chosen by the malicious nodes in this kind of attack. Identifying such malicious nodes from the network is an important research objective to be achieved. The technique which is proposed in this research is based on the two step verification. In the two steps verification technique, when the network performance is reduced to threshold value then the traffic is monitored that which node is sending data on such high rate. NS2 simulator is used to implement the proposed technique. With respect to various performance parameters, the proposed technique is analyzed. A comparative evaluation of results achieved from proposed and existing techniques is also done to conclude the level of improvement achieved.
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A passive approach for detecting image splicing using deep learning and haar wavelet transform
Статья научная
Passive image forgery detection has attracted many researchers in the recent years. Image manipulation becomes easier than before because of the fast development of digital image editing software. Image splicing is one of the most widespread methods for tampering images. Research on detection of image splicing still carries great challenges. In this paper, an algorithm based on deep learning approach and wavelet transform is proposed to detect the spliced image. In the deep learning approach, Convolution Neural Network (CNN) is employed to automatically extract features from the spliced image. CNN is applied and then Haar Wavelet Transform (HWT) is used. Support Vector Machine (SVM) is used later for classification. Additional experiments are performed. That is, Discrete Cosine Transform (DCT) replaces HWT and then Principle Component Analysis (PCA) is applied. The proposed algorithm is evaluated on a publicly available image splicing datasets (CASIA v1.0 and CASIA v2.0). It achieves high accuracy while using a relatively low dimension feature vector. Our results demonstrate that the proposed algorithm is effective and accomplishes better performance for detecting the spliced image.
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