Статьи журнала - International Journal of Computer Network and Information Security
Все статьи: 1110
An Improvement over a Server-less RFID Authentication Protocol
Статья научная
With the increased radio frequency identification (RFID) applications different authentication schemes have been proposed in order to meet the required properties. In this paper we analyze the security of a server-less RFID authentication protocol which is proposed by Deng et al. in 2014. Deng et al. proposed an improvement over Hoque et al. protocol to overcome its vulnerability against data desynchronization attack. However, in this paper we show that their protocol is still vulnerable against data desynchronization attack. Furthermore we present an improved version of this protocol to prevent this attack.
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An Innovative Approach for Video Steganography
Статья научная
In today's world internet is used for data communication which is not secure enough to transfer highly confidential data so information hiding become a emerging research area. Steganography is an art to hiding secret message in a cover and it will also hide the fact that secret information is being sent over the communication network. In this paper a video steganography method is proposed using hash based round Least Significant Bit technique. Video steganography is a more secure than any other steganography technique in hiding information because of its complex structure it disables the intruder to attack. In this paper secret text message is embedded in the video file using proposed hash based round Least Significant Bit technique. This work will improve the information security and embedding capacity. The proposed technique hash based round Least Significant Bit will be compared with the hash based Least Significant Bit. The technique proposed in this paper is analysed in term of Peak Signal to Noise Ratio, Mean Square Error and Embedding capacity.
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An Intrusion Detection and Prevention System based on Automatic Learning of Traffic Anomalies
Статья научная
The ever changing network traffic reveals new attack types, which represent a security threat that poses a serious risk for enterprise resources. Therefore, the security administrators are in a real need to employ efficient Intrusion Detection and Prevention Systems, IDPS. Such systems might be capable to learn from the network behavior. In this paper, we present an incremental Learnable Model for Anomaly Detection and Prevention of Zero-day attacks, LMAD/PZ. To facilitate the ability of learning from observations that can provide a reliable model for automatic prevention, a comparison has been carried out between supervised and unsupervised learning techniques. Thus, in LMAD/PZ, the intrusion detection step is integrated with an intrusion prevention plan. To ensure that the prevention plan is dependable and automatic, it must be backed and sustained with robust and accurate detection process. Therefore, two incremental data mining techniques are deeply investigated and implemented on NSL-KDD'99 intrusion dataset. The first technique is the Algorithm Quasi-optimal (AQ), which is a supervised Attributional Rules Learner, ARL, while the second is the Cobweb; an unsupervised hierarchical conceptual clustering algorithm. These algorithms categorize the network connections as either normal or anomalous. The performance of AQ is compared to Cobweb, and the best performance result is integrated with the prevention plan, to afford a fully automated system. The experimental results showed that, the model automatically adapts its knowledge base from continuous network streams, in addition to offering the advantage of detecting novel and zero day attacks. Many experiments have verified that AQ performance outperforms the Cobweb clustering, in terms of accuracy, detection rate and false alarm rate.
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An Obstacle Detection Scheme for Vehicles in an Intelligent Transportation System
Статья научная
Road obstacles cause serious accidents that have a severe impact on driver safety, traffic flow efficiency and damage of the vehicle. Detecting obstacles are important to prevent or to reduce such kind of the accidents and fatalities. However, it is difficult and becomes tricky because of some problems like presence of shadow, environmental changes or a sudden action of any moving things (e.g., car overtaking, animal coming) and many more. Thereby, this paper aims to design an obstacle detection technique based on (i) moving cameras and (ii) moving objects. These methods are applied to obstacle detection phase, in order to identify the different obstacles (e.g., potholes, animals, stop sign, obstacles, bumps, road cracks) by considering road dimensions. A new technique is introduced for detecting obstacles from moving camera and moving objects which overcomes several limitations over stationary cameras and moving/stationary objects. Further, paper reviews recent research trends to detect obstacles for moving cameras and moving objects with discussion of key points and limitations of each approach. Finally, the results show that the proposed method is more robust and reliable than the previous approaches based on the stationary cameras.
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An Optimized Authentication Mechanism for Mobile Agents by Using Machine Learning
Статья научная
A mobile agent is a small piece of software which works on direction of its source platform on a regular basis. Because mobile agents roam around wide area networks autonomously, the protection of the agents and platforms is a serious worry. The number of mobile agents-based software applications has increased dramatically over the past year. It has also enhanced the security risks associated with such applications. Most of the security mechanisms in the mobile agent architecture focus solely on platform security, leaving mobile agent safety to be a significant challenge. An efficient authentication scheme is proposed in this article to address the situation of protection and authentication of mobile agent at the hour of migration of across multiple platforms in malicious environment. An authentication mechanism for the mobile agent based on the Hopfield neural network proposed. The mobile agent’s identity and password are authenticate using the specified mechanism at the moment of execution of assigned operation. An evaluative assessment has been offered, along with their complex character, in comparison to numerous agent authentication approaches. The proposed method has been put into practice, and its different aspects have been put to the test. In contrasted to typical client-server and code-on-demand approaches, the analysis shows that computation here is often more safe and simpler.
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Статья научная
From massive and complex spatial database, the useful information and knowledge are extracted using spatial data mining. To analyze the complexity, efficient clustering algorithm for spatial database has been used in this area of research. The geographic areas containing spatial points are discovered using clustering methods in many applications. With spatial attributes, the spatial clustering problem have been designed using many approaches, but non-overlapping constraints are not considered. Most existing data mining algorithms suffer in high dimensions. With non-overlapping named as Non Overlapping Constraint based Optimized K-Means with Density and Distance-based Clustering (NOC-OKMDDC),a multidimensional optimization clustering is designed to solve this problem by the proposed system and the clusters with diverse shapes and densities in spatial databases are fast found. Proposed method consists of three main phases. Using weighted convolutional Neural Networks(Weighted CNN), attributes are reduced from the multidimensional dataset in this first phase. A partition-based algorithm (K-means) used by Optimized K-Means with Density and Distance-based Clustering (OKMDD) and several relatively small spherical or ball-shaped sub clusters are made by Clustering the dataset in this second phase. The optimal sub cluster count is performed with the help of Adaptive Adjustment Factor based Glowworm Swarm Optimization algorithm (AAFGSO). Then the proposed system designed an Enhanced Penalized Spatial Distance (EPSD) Measure to satisfy the non-overlapping condition. According to the spatial attribute values, the spatial distance between two points are well adjusted to achieving the EPSD. In third phase, to merge sub clusters the proposed system utilizes the Density based clustering with relative distance scheme. In terms of adjusted rand index, rand index, mirkins index and huberts index, better performance is achieved by proposed system when compared to the existing system which is shown by experimental result.
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An Optimized Protocol of M2M Authentication for Internet of Things (IoT)
Статья научная
The Internet of Things (IoT) consists of sensors, networks, and services to connect and control production systems. Machine-to-Machine (M2M) communication technology is considered as a key underlying technology for building Industrial IoT environments where devices are enabled to exchange information with each other in an autonomous way without human intervention. Resource-Constrained Devices (RCD) have found an expanding demand in the Internet of Things (IoT) applications as these gadgets are essentially working with delicate information. Thus, information security has ended up vital for both makers and clients. However, the creation of defenseless gadgets still challenging regarding the restriction of involved assets especially with the attackers ‘continuous trials to misuse these restrictions chasing important information. Hence, connecting an open key crypto-system becomes a must to extend gadget proficiency and relieve the chance of touchy data loss. Deployments of Elliptic curve cryptography (ECC) are fundamentally an open key crypto-system with the basic distinction of speedier advancing capacity whereas yielding an assortment of distinctive approaches to the arrangement of the cryptographic calculation. We will submit a proposed protocol to overcome the demands of information security and the speed of data circulation. The proposed protocol is characterized by low computational cost, communication and storage overhead, while achieving mutual authentication, session key agreement, device’s identity confidentiality, and resistance against various attacks.
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An Overview of Vertical Handoff Decision Making Algorithms
Статья научная
Uncontrollable development of wireless and mobile communication technology aims to provide the seamless continuous connection to access various wireless technologies and to have connection with the best network which provides the best quality of service (QoS). Each application requires different QoS, so the network selection may vary accordingly. To achieve this goal and to select the best network for a mobile terminal when moving from one network to another, it is necessary to have a good decision making algorithm which decides the best network for a specific application that the user needs based on QoS parameter. This paper presents an overview of handoff types, handoff process, and classification of vertical handoff, parameters required, existing work and the comparison table.
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An TPM based approach for generation of secret key
Статья научная
As the world becoming so much internet de-pendent and near about all the communications are done via internet, so the security of the communicating data is to be enhanced accordingly. For these purpose many encryption-decryption algorithms are available and many neural network based keys are also available which is used in these algorithms. Neural Network is a technique which is designed to work like a human brain. It has the ability to perform complex calculations with ease. To generate a secret key using neural networks many techniques are available like Tree Parity Machine (TPM) and many others. In TPM there are some flaws like less randomness, less time efficient. There are already three rules available i.e. Hebbian Rule, Anti Hebbian Rule and Random Walk, with same problems. So to overcome these issues, we propose a new approach based on the same concept(TPM, as Tree-structured Neural Network’s execution time is comparatively less than that of the other Neural Networks) which generate random and time-efficient secret key.
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An Ultra-secure Router-to-router Spontaneous Key Exchange System
Статья научная
This paper presents an ultra-secure router-to-router key exchange system. The key exchange process can be initiated by either router at will and can be carried out as often as required. We compare the efficacy of the proposed approach with contemporary quantum key distribution (QKD) systems and show that quantum-level security is attainable without resorting to single photon generators and other attendant instrumentation associated with QKD. Furthermore, the proposed system addresses the extremely limited geographical reach of commercially available QKD systems and other environmental restrictions they must operate in. The proposed system carries out all processing in electronics and is not vulnerable to the man in the middle attack. The medium of transfer can, of course, be optical fibers as is common in telecommunication.
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An Underwater Acoustic Routing protocol based on Hole Projecton
Статья научная
The directed routing protocol ends in failure when it faces a situation of the destination node with a very low velocity in a sparse ad hoc network so that none of nodes exist in its forwarding zone. Illuminated by BFDREAM and ZONER, the paper firstly proposes a novelty routing protocol that is fairly immune to forwarding failure through projecting the present source node on the boundaries of baffle holes of underwater acoustic networks in deep sea. Compared with DREAM and BFDREAM, the experimental results show our protocol achieves a great improvement in decreasing the propagation delay and reducing quantities of the non-effect information. So the new protocol may have a bright application prospect in deep sea acoustic networks.
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An Update based Energy-Efficient Reactive Routing Protocol for Mobile Ad Hoc Networks
Статья научная
Low cost energy-efficient (power based) routing protocols of mobile ad hoc networks (MANETs) increase the lifetime of static networks by using received signal strength (RSS) and battery power status (PS). They require GPS service to find the exact location of mobile nodes. The GPS devices themselves consume power because they need excessive updates to find the stationary nodes for efficient routing. To overcome this, RSS is being used as a metric, followed by, residual battery power. The recent protocols, based on these concepts, provide energy efficient routes during the route discovery phase only. Topological changes make these routes weak in due course of time. To update routes, HELLO process can be used, which however creates unnecessary overhead, delay and consumes power. Hence, these protocols do not update the routes. We propose an energy-efficient reactive routing protocol that uses the RSS and PS of mobile nodes. Proposed Link Failure Prediction (LFP) algorithm uses the link-layer feedback system to update active routes. We use ns2 for simulation of the proposed algorithm. Comparing the results of proposed scheme and existing scheme, in terms of energy consumption, link failure probability, and retransmission of packets, we observe that the proposed scheme outperforms the existing one.
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An analytical study of cellular automata and its applications in cryptography
Статья научная
Security and confidentiality are the major concerns in information technology enabled services wherein data security, user authentication, industrial security and message authentication have a great deal of access to the world anywhere, anytime. The implication is: there is a need for efficient methods to secure digital data across different platforms. The concept of cellular automata finds application in the design of efficient methods to secure digital information. It is a recent field of research and its recognition has been on the rise with its high parallel structure and ability to design complex dynamic systems. In this paper, we study the basic concepts of different types of cellular automata and also discuss its applications in cryptography with various examples.
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An approach to develop a transactional calculus for semi-structured database system
Статья научная
Traditional database system forces all data to adhere to an explicitly specified, rigid schema and most of the limitations of traditional database may be overcome by semi-structured database. Whereas a traditional transaction system guarantee that either all modifications are done or none of these i.e. the database must be atomic (either occurs all or occurs nothing) in nature. In this paper transaction is treating as a mapping from its environment to compensable programs and provides a transaction refinement calculus. The motivation of the Transactional Calculus for Semi Structured Database System (TCSS) is-finally, on a highly distributed network, it is desirable to provide some amount of fault tolerance. The paper proposes a mathematical framework for transactions where a transaction is treated as a mapping from its environment to compensable programs and also provides a transaction refinement calculus. It proposes to show that most of the semi structured transaction can be converted to a calculus based model which is simply consists of a forward activity and a compensation module of CAP (consistency, availability, and partition tolerance) [12] and BASE (basic availability, soft state and eventually consistent) [45] theorem. It proposes to show that most of the semi-structured transaction can be converted to a calculus based model which is simply consists of a forward activity and a compensation module of CAP and BASE theorem. It is important that the service still perform as expected if some nodes crash or communication links fail, Verification of several useful properties of the proposed TCSS includes in this article. Moreover, a detailed comparative analysis has been providing towards evaluation of the proposed TCSS.
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An efficiency optimization for network intrusion detection system
Статья научная
With the enormous rise in the usage of computer networks, the necessity for safeguarding these networks is also increased. Network intrusion detection systems (NIDS) are designed to monitor and inspect the activities in a network. NIDS mainly depends on the features of the input network data as these features give information on the behaviour nature of the network traffic. The irrelevant and redundant network features negatively affect the efficacy and quality of NIDS, particularly its classification accuracy, detection time and processing complexity. In this paper, several feature selection techniques are applied to optimize the efficiency of NIDS. The categories of the applied feature selection techniques are the filter, wrapper and hybrid. Support vector machine (SVM) is employed as the detection model to classify the network connections behaviour into normal and abnormal traffic. NIDS is trained and tested on the benchmark NSL-KDD dataset. The performance of the applied feature selection techniques is compared with each other and the results are discussed. Evaluation results demonstrated the superiority of the wrapper techniques in providing the highest classification accuracy with the lowest detection time and false alarms of the NIDS.
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An efficient image block encryption for key generation using non-uniform cellular automata
Статья научная
Cryptographic image block encryption schemes play a significant role in information enabled services. This paper proposes an image block encryption scheme based on a novel three stage selection (TSS) method in a public cloud with reversible cellular automata. Due to the openness of public cloud, different attacks are possible over user sensitive information. The TSS method has three stages and they generate a robust master key with user plaintext as input and produces an encrypted block as key to be sent to authenticated users. An analysis of experimental results shows that this new method has a large key space and immune to brute force attacks, statistical cryptanalysis attacks and chosen plaintext attacks. Also, the encrypted image entropy value could be increased to 7.9988 making it ideal for a best image block encryption for key generation.
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An efficient indexing technique for AES lookup table to prevent side-channel cache timing attack
Статья научная
In the era of virtualization, co-residency with unknown neighbours is a necessary evil and leakage of information through side-channels is an inevitable fact. Preventing this leakage of information through side-channels, while maintaining high efficiency, has become one of the most challenging parts of any implementation of the Advanced Encryption Standard (AES) that is based on the Rijndael Cipher. Exploiting the associative nature of the cache and susceptible memory access pattern, AES is proved to be vulnerable to side-channel cache-timing attacks. The reason of this vulnerability is primarily ascribed to the existence of correlation between the index Bytes of the State matrix and corresponding accessed memory blocks. In this paper, we idealized the model of cache-timing attack and proposed a way of breaking this correlation through the implementation of a Random Address Translator (RAT). The simplicity of the design architecture of RAT can make itself a good choice as a way of indexing the lookup tables for the implementers of the AES seeking resistance against side-channel cache-timing attacks.
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An efficient steganography technique for images using chaotic bitstream
Статья научная
Steganography is a science of security technique through invisible communication; hiding secret message into cover objects without any suspicion. Protection of the hidden information from an adversary is the main purpose of any steganography system even if the embedding method is obvious. In this paper, an effective image steganography system based on a least significant bit (LSB) algorithm and chaos is proposed. The proposed method contains a spatial domain technique in which the Logistic map is used for generating chaotic bitstream and bitwise XOR operation which is used to create control bit. Control bit determines whether the LSB of a specific pixel is changed or not according to the secret message. This provides direct manipulation over the pixels of the image with a very low precision hence enhances the system security. In this study, gray image is used as secret message and a larger scale image as cover image. Experimental results demonstrate that the proposed method is very efficient to detect LSB replacement in the algorithm. Moreover, the proposed algorithm is highly sensitive to the stego key parameters due to complex structure of chaos which provides high level of security in the whole system and effectively hides and detects the image information.
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Статья научная
This research proposes a model for presenting email to Artificial Neural Network (ANN) to classify spam and legitimate emails. The proposed model based on selecting wise 13 fixed features relevant to spam emails combined with text features. The experiment tests many scenarios to find out the best-suited combination of features representation. These scenarios show the effect of using term frequency (tf), term frequency-inverse document frequency (tf*idf), Level two (L2) normalization, and principal component analysis (PCA) for dimension reduction. Text features vectors are represented in the principal component space as a reduced form of the original features vectors. PCA reduction effect on ANN performance is also studied. Among these tests, best-suited model that improves ANN classification and speeds up training is concluded and suggested. An idea of integrating ANN anti-spam filter into score-based anti-spam systems is also explained in this paper. XEAMS email gateway, the commercial anti-spam, already uses Naïve Bayes (NB) filter as one of its many techniques to identify spam email. The proposed approach influences filtering results by 7.5% closer to XEAMS anti-spam system results than NB filter does on real-life emails of Arabic and English messages.
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An evolutionary approach of attack graph to attack tree conversion
Статья научная
The advancement of modern day computing has led to an increase of threats and intrusions. As a result, advanced security measurements and threat analysis models are necessary to detect these threats and identify protective measures needed to secure a system. Attack graphs and attack trees are the most popular form of attack modeling today. While both of these approaches represent the possible attack steps followed by an attacker, attack trees are architecturally more rigorous than attack graphs and provide more insights regarding attack scenarios. The goal of this research is to identify the possible direction to construct attack trees from attack graphs analyzing a large volume of data, alerts or logs generated through different intrusion detection systems or network configurations. This literature summarizes the different approaches through an extensive survey of the relevant papers and identifies the current challenges, requirements and limitations of an efficient attack modeling approach with attack graphs and attack trees. A discussion of the current state of the art is presented in the later part of the paper, followed by the future direction of research.
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