Biblio

In internet based communication, various types of attacks have been evolved. Hence, attacker easily breaches the securities. Traditional intrusion detection techniques to observe these attacks have failed and thus hefty systems are required to remove these attacks before they expose entire network. With the ability of artificial intelligence systems to adapt high computational speed, boost fault tolerance, and error resilience against noisy information, a hybrid particle swarm optimization(PSO) fuzzy rule based inference engine has been designed in this paper. The fuzzy logic based on degree of truth while the PSO algorithm based on population stochastic technique helps in learning from the scenario, thus their combination will increase the toughness of intrusion detection system. The proposed network intrusion detection system will be able to classify normal as well as anomalism behaviour in the network. DARPA-KDD99 dataset examined on this system to address the behaviour of each connection on network and compared with existing system. This approach improves the result on the basis of precision, recall and F1-score.

In many hostile military environments for instance war zone, unfriendly nature, etc., the systems perform on the specially promoted mode and nature which they tolerate the defined system network architecture. Preparation of Disruption-Tolerant systems (DTN) enhances the network between the remote devices which provided to the soldiers in the war zone, this situation conveys the reliable data transmission under scanner. Cipher text approach are based on the attribute based encryption which mainly acts on the attributes or role of the users, which is a successful cryptographic strategy to maintain the control issues and also allow reliable data transfer. Specially, the systems are not centralized and have more data constrained issues in the systems, implementing the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) was an important issue, where this strategy provides the new security and data protection approach with the help of the Key Revocation, Key Escrows and collaboration of the certain attributes with help of main Key Authorities. This paper mainly concentrates on the reliable data retrieval system with the help of CP-ABE for the Disruption-Tolerant Networks where multiple key authorities deal with respective attributes safely and securely. We performed comparison analysis on existing schemes with the recommended system components which are configured in the respective decentralized tolerant military system for reliable data retrieval.

This article describes an approach to identification and certification in decentralized environment. The protocol proposes a way of integration for blockchain technology and web-of-trust concept to create decentralized public key infrastructure with flexible management for user identificators. Besides changing the current public key infrastructure, this system can be used in the Internet of Things (IoT). Each individual IoT sensor must correctly communicate with other components of the system it's in. To provide safe interaction, components should exchange encrypted messages with ability to check their integrity and authenticity, which is presented by this scheme.

This paper studies the deletion propagation problem in terms of minimizing view side-effect. It is a problem funda-mental to data lineage and quality management which could be a key step in analyzing view propagation and repairing data. The investigated problem is a variant of the standard deletion propagation problem, where given a source database D, a set of key preserving conjunctive queries Q, and the set of views V obtained by the queries in Q, we try to identify a set T of tuples from D whose elimination prevents all the tuples in a given set of deletions on views △V while preserving any other results. The complexity of this problem has been well studied for the case with only a single query. Dichotomies, even trichotomies, for different settings are developed. However, no results on multiple queries are given which is a more realistic case. We study the complexity and approximations of optimizing the side-effect on the views, i.e., find T to minimize the additional damage on V after removing all the tuples of △V. We focus on the class of key-preserving conjunctive queries which is a dichotomy for the single query case. It is surprising to find that except the single query case, this problem is NP-hard to approximate within any constant even for a non-trivial set of multiple project-free conjunctive queries in terms of view side-effect. The proposed algorithm shows that it can be approximated within a bound depending on the number of tuples of both V and △V. We identify a class of polynomial tractable inputs, and provide a dynamic programming algorithm to solve the problem. Besides data lineage, study on this problem could also provide important foundations for the computational issues in data repairing. Furthermore, we introduce some related applications of this problem, especially for query feedback based data cleaning.

Digital image security is now a severe issue, especially when sending images to telecommunications networks. There are many ways where digital images can be encrypted and decrypted from secure communication. Digital images contain data that is important when captured or disseminated to preserve and preserve data. The technique of encryption is one way of providing data on digital images. A key cipher block chaining and Gingerbreadman Map are used in our search to encrypt images. This new system uses simplicity, high quality, enhanced by the vehicle's natural efficiency and the number of the chain. The proposed method is performed for experimental purposes and the experiments are performed in- depth, highly reliable analysis. The results confirm that by referring to several known attacks, the plan cannot be completed. Comparative studies with other algorithms show a slight rise in the security of passwords with the advantages of security of the chain. The results of this experiment are a comparison of button sensitivity and a comparison after encryption and decryption of the initial image using the amount of pixel change rate and unified average change intensity.

A strong designated verifier signature (SDVS) scheme only permits an intended verifier to validate the signature by employing his/her private key. Meanwhile, for the sake of signer anonymity, the designated verifier is also able to generate a computationally indistinguishable transcript, which prevents the designated verifier from arbitrarily transferring his conviction to any third party. To extend the applications of conventional SDVS schemes, in this paper, we propose a group-oriented strong designated verifier signature (GO-SDVS) scheme from bilinear pairings. In particular, our scheme allows a group of signers to cooperatively generate a signature for a designated verifier. A significant property of our mechanism is constant-size signatures, i.e., the signature length remains constant when the number of involved signers increases. We also prove that the proposed GO-SDVS scheme is secure against adaptive chosen-message attacks in the random oracle model and fulfills the essential properties of signer ambiguity and non-transferability.

This paper presents the proposed method of building a digital signature algorithm which is based on the difficulty of solving root problem and some expanded root problems on Zp. The expanded root problem is a new form of difficult problem without the solution, also originally proposed and applied to build digital signature algorithms. This proposed method enable to build a high-security digital signature platform for practical applications.

Distributed consensus is a prototypical distributed optimization and decision making problem in social, economic and engineering networked systems. In collaborative applications investigating the effects of adversaries is a critical problem. In this paper we investigate distributed consensus problems in the presence of adversaries. We combine key ideas from distributed consensus in computer science on one hand and in control systems on the other. The main idea is to detect Byzantine adversaries in a network of collaborating agents who have as goal reaching consensus, and exclude them from the consensus process and dynamics. We describe a novel trust-aware consensus algorithm that integrates the trust evaluation mechanism into the distributed consensus algorithm and propose various local decision rules based on local evidence. To further enhance the robustness of trust evaluation itself, we also introduce a trust propagation scheme in order to take into account evidences of other nodes in the network. The resulting algorithm is flexible and extensible, and can incorporate more complex designs of decision rules and trust models. To demonstrate the power of our trust-aware algorithm, we provide new theoretical security performance results in terms of miss detection and false alarm rates for regular and general trust graphs. We demonstrate through simulations that the new trust-aware consensus algorithm can effectively detect Byzantine adversaries and can exclude them from consensus iterations even in sparse networks with connectivity less than 2f+1, where f is the number of adversaries.

Cryptographic hash functions play a crucial role in information security. Cryptographic hash functions are used in various cryptographic applications to verify the message authenticity and integrity. In this paper we propose a Cellular Automata Based Hashing Algorithm (CABHA) for generating strong cryptographic hash function. The proposed CABHA algorithm uses the cellular automata rules and a custom transformation function to create a strong hash from an input message and a key.

The growing use of MANETs and its vulnerability to attacks due to its fundamental characteristics make secure routing one of the most considerable challenges. In this paper, a new security scheme for mobile ad hoc networks (MANETs) is presented. The proposed scheme used Trivium lightweight stream cipher algorithm in combination with HMAC to secure the routing control packets. This paper compares the performance of the AODV after implementing the security scheme in terms of throughput, delay sum (end-to-end), jitter sum (end-to-end) and packet loss ratio. The proposed scheme shows better performance than original AODV under blackhole attack.

Named data network (NDN) is one of the most promising information-centric networking architectures, where the core concept is to focus on the named data (or contents) themselves. Users in NDN can easily send a request packet to get the desired content regardless of its address. The routers in NDN have cache functionality to make the users instantly retrieve the desired file. Thus, the user can immediately get the desired file from the nearby nodes instead of the remote host. Nevertheless, NDN is a novel proposal and there are still some open issues to be resolved. In view of previous research, it is a challenge to achieve access control on a specific user and support potential receivers simultaneously. In order to solve it, we present a fine-grained access control mechanism tailored for NDN, supporting data confidentiality, potential receivers, and mobility. Compared to previous works, this is the first to support fine-grained access control and potential receivers. Furthermore, the proposed scheme achieves provable security under the DBDH assumption.

Image registration is the process of aligning images of the same scene taken at different instances, from different viewpoints or by heterogeneous sensors. This can be achieved either by area based or by feature based image matching techniques. Feature based image registration focuses on detecting relevant features from the input images and attaching descriptors to these features. Matching visual descriptions of two images is a major task in image registration. This feature matching is currently done using Exhaustive Search (or Brute-Force) and Nearest Neighbour Search. The traditional method used for nearest neighbour search is by representing the data as k-d trees. This nearest neighbour search can also be performed using combinatorial optimization algorithms such as Simulated Annealing. This work proposes a method to perform image feature matching by nearest neighbour search done based on Threshold Accepting, a faster version of Simulated Annealing.The experiments performed suggest that the proposed algorithm can produce better results within a minimum number of iterations than many existing algorithms.

Large graphs are increasingly prevalent in mobile networks, social networks, traffic networks and biological networks. These graphs are often uncertain, where edges are augmented with probabilities that indicates the chance to exist. Recently k-nearest neighbor search has been studied within the field of uncertain graphs, but the scalability and efficiency issues are not well solved. Moreover, solutions are implemented on a single machine and thus cannot fit large uncertain graphs. In this paper, we develop a framework, called DURS, to distribute k-nearest neighbor search into several machines and re-partition the uncertain graphs to balance the work loads and reduce the communication costs. Evaluation results show that DURS is essential to make the system scalable when answering k-nearest neighbor queries on uncertain graphs.

Due to the massive amount of the network traffic, attackers have a great chance to cause a huge damage to the network system or its users. Intrusion detection plays an important role in ensuring security for the system by detecting the attacks and the malicious activities. In this paper, we utilize CIDDS dataset and apply a deep learning approach, Long-Short-Term Memory (LSTM), to implement intrusion detection system. This research achieves a reasonable accuracy of 0.85.

Research in combating misinformation reports many negative results: facts may not change minds, especially if they come from sources that are not trusted. Individuals can disregard and justify lies told by trusted sources. This problem is made even worse by social recommendation algorithms which help amplify conspiracy theories and information confirming one's own biases due to companies' efforts to optimize for clicks and watch time over individuals' own values and public good. As a result, more nuanced voices and facts are drowned out by a continuous erosion of trust in better information sources. Most misinformation mitigation techniques assume that discrediting, filtering, or demoting low veracity information will help news consumers make better information decisions. However, these negative results indicate that some news consumers, particularly extreme or conspiracy news consumers will not be helped. We argue that, given this background, technology solutions to combating misinformation should not simply seek facts or discredit bad news sources, but instead use more subtle nudges towards better information consumption. Repeated exposure to such nudges can help promote trust in better information sources and also improve societal outcomes in the long run. In this article, we will talk about technological solutions that can help us in developing such an approach, and introduce one such model called Trust Nudging.

The giant network of Internet of Things establishes connections between smart devices and people, with protocols to collect and share data. While the data is expanding at a fast pace in this era of Big Data, there are growing concerns about security and privacy policies. In the current Internet of Things ecosystems, at the intersection of the Internet of Things, Big Data, and Cybersecurity lies the subject that attracts the most attention. In aiding users in getting an adequate understanding, this paper introduces HackerNets, an interactive visualization for emerging topics in the crossing of IoT, Big Data, and Cybersecurity over time. To demonstrate the effectiveness and usefulness of HackerNets, we apply and evaluate the technique on the dataset from the social media platform.

This paper introduces an approach based on control theory to model, analyze and select optimal security policies for Moving Target Defense (MTD) deployment strategies. A Markov Decision Process (MDP) scheme is presented to model states of the system from attacking point of view. The employed value iteration method is based on the Bellman optimality equation for optimal policy selection for each state defined in the system. The model is then utilized to analyze the impact of various costs on the optimal policy. The MDP model is then applied to two case studies to evaluate the performance of the model.

Video Steganography is an extension of image steganography where any kind of file in any extension is hidden into a digital video. The video content is dynamic in nature and this makes the detection of hidden data difficult than other steganographic techniques. The main motive of using video steganography is that the videos can store large amount of data in it. This paper focuses on security using the combination of hybrid neural networks and hash function for determining the best bits in the cover video to embed the secret data. For the embedding process, the cover video and the data to be hidden is uploaded. Then the hash algorithm and neural networks are applied to form the stego video. For the extraction process, the reverse process is applied and the secret data is obtained. All experiments are done using MatLab2016a software.

Profiled DPA is a new method combined with machine learning method in side channel attack which is put forward by Whitnall in CHES 2015.[1]The most important part lies in effectiveness of extracting information. This paper introduces a new rule Explained Local Variance (ELV) to extract information in profiled stage for profiled DPA. It attracts information effectively and shields noise to get better accuracy than the original rule. The ELV enables an attacker to use less power traces to get the same result as before. It also leads to 94.6% space reduction and 29.2% time reduction for calculation. For security circuit implementation, a high order masking scheme in modelsim is implemented. A new exchange network is put forward. 96.9% hardware resource is saved due to the usage of this network.

Named Data Networking (NDN) is a promising candidate for future internet architecture. It is one of the implementations of the Information-Centric Networking (ICN) architectures where the focus is on the data rather than the owner of the data. While the data security is assured by definition, these networks are susceptible of various Denial of Service (DoS) attacks, mainly Interest Flooding Attacks (IFA). IFAs overwhelm an NDN router with a huge amount of interests (Data requests). Various solutions have been proposed in the literature to mitigate IFAs; however; these solutions do not make a difference between intentional and unintentional misbehavior due to the network congestion. In this paper, we propose a novel congestion-aware IFA detection and mitigation solution. We performed extensive simulations and the results clearly depict the efficiency of our proposal in detecting truly occurring IFA attacks.

With the wide use of smart device made huge amount of information arise. This information needed new methods to deal with it from that perspective big data concept arise. Most of the concerns on big data are given to handle data without concentrating on its security. Encryption is the best use to keep data safe from malicious users. However, ordinary encryption methods are not suitable for big data. Selective encryption is an encryption method that encrypts only the important part of the message. However, we deal with uncertainty to evaluate the important part of the message. The problem arises when the important part is not encrypted. This is the motivation of the paper. In this paper we propose security framework to secure important and unimportant portion of the message to overcome the uncertainty. However, each will take a different encryption technique for better performance without losing security. The framework selects the important parts of the message to be encrypted with a strong algorithm and the weak part with a medium algorithm. The important of the word is defined according to how its origin frequently appears. This framework is applied on amazon EC2 (elastic compute cloud). A comparison between the proposed framework, the full encryption method and Toss-A-Coin method are performed according to encryption time and throughput. The results showed that the proposed method gives better performance according to encryption time, throughput than full encryption.

Cloud computing undoubtedly is the most unparalleled technique in rapidly developing industries. Protecting sensitive files stored in the clouds from being accessed by malicious attackers is essential to the success of the clouds. In proxy re-encryption schemes, users delegate their encrypted files to other users by using re-encryption keys, which elegantly transfers the users' burden to the cloud servers. Moreover, one can adopt conditional proxy re-encryption schemes to employ their access control policy on the files to be shared. However, we recognize that the size of re-encryption keys will grow linearly with the number of the condition values, which may be impractical in low computational devices. In this paper, we combine a key-aggregate approach and a proxy re-encryption scheme into a key-aggregate proxy re-encryption scheme. It is worth mentioning that the proposed scheme is the first key-aggregate proxy re-encryption scheme. As a side note, the size of re-encryption keys is constant.

Digital signature over elliptic curve is one of the most important applications of security because it is effective. Recently, it has been developed and defined in the various standard of security. The application of the digital signature are signer authentication, data integrity, and non-repudiation. Currently, the requirements to implement authentication process on a computer hardware with limited resource such as energy, memory and computing power are increasing. The developer should consider these factors along with security factor for the effective implement on the computer hardware with limited resource. In this paper, we propose the Schnorr signature scheme using Koblitz curve over a field of characteristic two. The advantage of Schnorr signature scheme is a good combination with Koblitz curve over a field of characteristic two, therefore its arithmetic can be performed in any computer. Moreover, we use Double-and-Add scalar multiplication to reduce time in the process of systems. In addition, this paper shows a result of time in the process of the system to compare the performance of the Schnorr signature scheme on Koblitz curve using Double-andAdd scalar multiplication with the Schnorr signature scheme on Koblitz curve using typical scalar multiplication. The result of this study is that both systems working correctly. However, the Schnorr signature scheme on Koblitz curve using Double-andAdd performs better in time efficiency than of Schnorr signature scheme on Koblitz curve using typical scalar multiplication.

In cognitive radio networks (CRNs), secondary users (SUs) are vulnerable to malicious attacks because an SU node's opportunistic access cannot be protected from adversaries. How to design a channel hopping scheme to protect SU nodes from jamming attacks is thus an important issue in CRNs. Existing anti-jamming channel hopping schemes have some limitations: Some require SU nodes to exchange secrets in advance; some require an SU node to be either a receiver or a sender, and some are not flexible enough. Another issue for existing anti-jamming channel hopping schemes is that they do not consider different nodes may have different traffic loads. In this paper, we propose an anti-jamming channel hopping protocol, Load Awareness Anti-jamming channel hopping (LAA) scheme. Nodes running LAA are able to change their channel hopping sequences based on their sending and receiving traffic. Simulation results verify that LAA outperforms existing anti-jamming schemes.

In the past few years, Internet of Things (IoT) devices have emerged and spread everywhere. Many researchers have been motivated to study the security issues of IoT devices due to the sensitive information they carry about their owners. Privacy is not simply about encryption and access authorization, but also about what kind of information is transmitted, how it used and to whom it will be shared with. Thus, IoT manufacturers should be compelled to issue Privacy Policy Agreements for their respective devices as well as ensure that the actual behavior of the IoT device complies with the issued privacy policy. In this paper, we implement a test bed for ensuring compliance of Internet of Things data disclosure to the corresponding privacy policy. The fundamental approach used in the test bed is to capture the data traffic between the IoT device and the cloud, between the IoT device and its application on the smart-phone, and between the IoT application and the cloud and analyze those packets for various features. We test 11 IoT manufacturers and the results reveal that half of those IoT manufacturers do not have an adequate privacy policy specifically for their IoT devices. In addition, we prove that the action of two IoT devices does not comply with what they stated in their privacy policy agreement.