Biblio

Nowadays, mobile devices have become one of the most popular instruments used by a person on its regular life, mainly due to the importance of their applications. In that context, mobile devices store user's personal information and even more data, becoming a personal tracker for daily activities that provides important information about the user. Derived from this gathering of information, many tools are available to use on mobile devices, with the restrain that each tool only provides isolated information about a specific application or activity. Therefore, the present work proposes a tool that allows investigators to obtain a complete report and timeline of the activities that were performed on the device. This report incorporates the information provided by many sources into a unique set of data. Also, by means of an example, it is presented the operation of the solution, which shows the feasibility in the use of this tool and shows the way in which investigators have to apply the tool.

This paper proposes a framework for predicting and mitigating insider collusion threat in relational database systems. The proposed model provides a robust technique for database architect and administrators to predict insider collusion threat when designing database schema or when granting privileges. Moreover, it proposes a real time monitoring technique that monitors the growing knowledgebases of insiders while executing transactions and the possible collusion insider attacks that may be launched based on insiders accesses and inferences. Furthermore, the paper proposes a mitigating technique based on the segregation of duties principle and the discovered collusion insider threat to mitigate the problem. The proposed model was tested to show its usefulness and applicability.

In this paper, we propose a frozen bit selection scheme for polar coding scheme combined with physical layer security that enhances the security of two legitimate users on a wiretap channel. By flipping certain frozen bits, the bit-error rate (BER) of an eavesdropper is maximized while the BER of the legitimate receiver is unaffected. An ARQ protocol is proposed that only feeds back a small proportion of the frozen bits to the transmitter, which increases the secrecy rate. The scheme is evaluated on a wiretap channel affected by impulsive noise and we consider cases where the eavesdropper's channel is actually more impulsive than the main channel. Simulation results show that the proposed scheme ensures the eavesdropper's BER is high even when only one frozen bit is flipped and this is achieved even when their channel is more impulsive than the main channel.

The notion of attribute-based encryption with outsourced decryption (OD-ABE) was proposed by Green, Hohenberger, and Waters. In OD-ABE, the ABE ciphertext is converted to a partially-decrypted ciphertext that has a shorter bit length and a faster decryption time than that of the ABE ciphertext. In particular, the transformation can be performed by a powerful third party with a public transformation key. In this paper, we propose a generic approach for constructing ABE with outsourced decryption from standard ABE, as long as the later satisfies some additional properties. Its security can be reduced to the underlying standard ABE in the selective security model by a black-box way. To avoid the drawback of selective security in practice, we further propose a modified decryption outsourcing mode so that our generic construction can be adapted to satisfying adaptive security. This partially solves the open problem of constructing an OD-ABE scheme, and its adaptive security can be reduced to the underlying ABE scheme in a black-box way. Then, we present some concrete constructions that not only encompass existing ABE outsourcing schemes of Green et al., but also result in new selectively/adaptively-secure OD-ABE schemes with more efficient transformation key generation algorithm. Finally, we use the PBC library to test the efficiency of our schemes and compare the results with some previous ones, which shows that our schemes are more efficient in terms of decryption outsourcing and transformation key generation.

Short messages usage has been tremendously increased such as SMS, tweets and status updates. Due to its popularity and ease of use, many companies use it for advertisement purpose. Hackers also use SMS to defraud users and steal personal information. In this paper, the use of Graphs centrality metrics is proposed for spam SMS detection. The graph centrality measures: degree, closeness, and eccentricity are used for classification of SMS. Graphs for each class are created using labeled SMS and then unlabeled SMS is classified using the centrality scores of the token available in the unclassified SMS. Our results show that highest precision and recall is achieved by using degree centrality. Degree centrality achieved the highest precision i.e. 0.81 and recall i.e., 0.76 for spam messages.

This paper presents necessary modeling and control enhancements for Modular Multilevel Converters (MMC) to provide Fault-Ride-Through capability and fast fault current injection as required by the new German Technical Connection Rules for HVDC. HVDC converters have to be able to detect and control the grid voltage and grid currents accurately during all fault conditions. That applies to the positive as well as negative sequence components, hence a Decoupled Double Synchronous Reference Frame - Phase-Locked-Loop (DDSRF-PLL) and Current Control (DDSRF-CC) are implemented. In addition, an enhanced current limitation and an extension of the horizontal balancing control are proposed to complement the control structure for safe operation.

As chips become more and more connected, they are more exposed (both to network and to physical attacks). Therefore one shall ensure they enjoy a sufficient protection level. Security within chips is accordingly becoming a hot topic. Incident detection and reporting is one novel function expected from chips. In this talk, we explain why it is worthwhile to resort to Artificial Intelligence (AI) for security event handling. Drivers are the need to aggregate multiple and heterogeneous security sensors, the need to digest this information quickly to produce exploitable information, and so while maintaining a low false positive detection rate. Key features are adequate learning procedures and fast and secure classification accelerated by hardware. A challenge is to embed such security-oriented AI logic, while not compromising chip power budget and silicon area. This talk accounts for the opportunities permitted by the symbiotic encounter between chip security and AI.

Quantum Key Distribution (QKD) is a technique for sharing encryption keys between two adjacent nodes. It provides unconditional secure communication based on the laws of physics. From the viewpoint of network research, QKD is considered to be a component for providing secure communication in network systems. A QKD network enables each node to exchange encryption keys with arbitrary nodes. However previous research did not focus on the processing speed of the key management method essential for a QKD network. This paper focuses on the key management method assuming a high-speed QKD system for which we clarify the design, propose a high-speed method, and evaluate the throughput. The proposed method consists of four modules: (1) local key manager handling the keys generated by QKD, (2) one-time pad tunnel manager establishing the transparent encryption link, (3) global key manager generating the keys for application communication, and (4) web API providing keys to the application. The proposed method was implemented in software and evaluated by emulating QKD key generation and application key consumption. The evaluation result reveals that it is capable of handling the encryption keys at a speed of 414 Mb/s, 185 Mb/s, 85 Mb/s and 971 Mb/s, for local key manager, one-time pad tunnel manager, global key manager and web API, respectively. These are sufficient for integration with a high-speed QKD system. Furthermore, the method allows the high-speed QKD system consisting of two nodes to expand corresponding to the size of the QKD network without losing the speed advantage.

Recently, data protection has become increasingly important in cloud environments. The cloud platform has global user information, rich storage resource allocation information, and a fuller understanding of data attributes. At the same time, there is an urgent need for data access control to provide data security, and software-defined network, as a ready-made facility, has a global network view, global network management capabilities, and programable network rules. In this paper, we present an approach, named High-Performance Software-Defined Data Access Network (HP-SDDAN), providing software-defined data access network architecture, global data attribute management and attribute-based data access network. HP-SDDAN combines the excellent features of cloud platform and software-defined network, and fully considers the performance to implement software-defined data access network. In evaluation, we verify the effectiveness and efficiency of HP-SDDAN implementation, with only 1.46% overhead to achieve attribute-based data access control of attribute-based differential privacy.

In today's time Software Defined Network (SDN) gives the complete control to get the data flow in the network. SDN works as a central point to which data is administered centrally and traffic is also managed. SDN being open source product is more prone to security threats. The security policies are also to be enforced as it would otherwise let the controller be attacked the most. The attacks like DDOS and DOS attacks are more commonly found in SDN controller. DDOS is destructive attack that normally diverts the normal flow of traffic and starts the over flow of flooded packets halting the system. Machine Learning techniques helps to identify the hidden and unexpected pattern of the network and hence helps in analyzing the network flow. All the classified and unclassified techniques can help detect the malicious flow based on certain parameters like packet flow, time duration, accuracy and precision rate. Researchers have used Bayesian Network, Wavelets, Support Vector Machine and KNN to detect DDOS attacks. As per the review it's been analyzed that KNN produces better result as per the higher precision and giving a lower falser rate for detection. This paper produces better approach of hybrid Machine Learning techniques rather than existing KNN on the same data set giving more accuracy of detecting DDOS attacks on higher precision rate. The result of the traffic with both normal and abnormal behavior is shown and as per the result the proposed algorithm is designed which is suited for giving better approach than KNN and will be implemented later on for future.

In this article, to deal with data security requirements of electric vehicle users, a key management scheme for smart charging has been studied. According to the characteristics of the network, three elements and a two-subnetwork model between the charging and the electric vehicle users have been designed. Based on the hypergraph theory, the hypergraph structure of the smart charging network is proposed. And the key management scheme SCHKM is designed to satisfy the operational and security requirements of this structure. The efficiency of SCHKM scheme is analyzed from the cost experiment of key generation and key storage. The experimental results show that compared with the LKH, OFT and GKMP, the proposed key management scheme has obvious advantages in multi-user and key generation cost.

Classifying Hyperspectral images with few training samples is a challenging problem. The generative adversarial networks (GAN) are promising techniques to address the problems. GAN constructs an adversarial game between a discriminator and a generator. The generator generates samples that are not distinguishable by the discriminator, and the discriminator determines whether or not a sample is composed of real data. In this paper, by introducing multilayer features fusion in GAN and a dynamic neighborhood voting mechanism, a novel algorithm for HSIs classification based on 1-D GAN was proposed. Extracting and fusing multiple layers features in discriminator, and using a little labeled samples, we fine-tuned a new sample 1-D CNN spectral classifier for HSIs. In order to improve the accuracy of the classification, we proposed a dynamic neighborhood voting mechanism to classify the HSIs with spatial features. The obtained results show that the proposed models provide competitive results compared to the state-of-the-art methods.

Vehicle ad-hoc network (VANET) is the main driving force to alleviate traffic congestion and accelerate the construction of intelligent transportation. However, the rapid growth of the number of vehicles makes the construction of the safety system of the vehicle network facing multiple tests. This paper proposes an identity-based aggregate signature scheme to protect the privacy of vehicle identity, receive messages in time and authenticate quickly in VANET. The scheme uses aggregate signature algorithm to aggregate the signatures of multiple users into one signature, and joins the idea of batch authentication to complete the authentication of multiple vehicular units, thereby improving the verification efficiency. In addition, the pseudoidentity of vehicles is used to achieve the purpose of vehicle anonymity and privacy protection. Finally, the secure storage of message signatures is effectively realized by using reliable cloud storage technology. Compared with similar schemes, this paper improves authentication efficiency while ensuring security, and has lower storage overhead.

Information security is winding up noticeably more vital in information stockpiling and transmission. Images are generally utilised for various purposes. As a result, the protection of image from the unauthorised client is critical. Established encryption techniques are not ready to give a secure framework. To defeat this, image encryption is finished through DNA encoding which is additionally included with confused 1D and 2D logistic maps. The key communication is done through the quantum channel using the BB84 protocol. To recover the encrypted image DNA decoding is performed. Since DNA encryption is invertible, decoding can be effectively done through DNA subtraction. It decreases the complexity and furthermore gives more strength when contrasted with traditional encryption plans. The enhanced strength of the framework is measured utilising measurements like NPCR, UACI, Correlation and Entropy.

The core operation of all cryptosystems based on Elliptic Curve Cryptography is Elliptic Curve Point Multiplication. Depending on implementation it can be vulnerable to different Side Channel Analysis attacks exploiting information leakage, such as power consumption or execution time. Multiple countermeasures against these attacks have been developed over time, each having different impact on parameters of the cryptosystem. This paper summarizes popular countermeasures for simple and differential power analysis attacks on Elliptic Curve cryptosystems. Presented secure algorithms were implemented in Verilog hardware description language and synthesized to logic gates for power trace generation.

Network intrusion detection is an important component of network security. Currently, the popular detection technology used the traditional machine learning algorithms to train the intrusion samples, so as to obtain the intrusion detection model. However, these algorithms have the disadvantage of low detection rate. Deep learning is more advanced technology that automatically extracts features from samples. In view of the fact that the accuracy of intrusion detection is not high in traditional machine learning technology, this paper proposes a network intrusion detection model based on convolutional neural network algorithm. The model can automatically extract the effective features of intrusion samples, so that the intrusion samples can be accurately classified. Experimental results on KDD99 datasets show that the proposed model can greatly improve the accuracy of intrusion detection.

Chinese Remainder Theorem (CRT) is one of the spatial domain methods that is more implemented in the data hiding method watermarking. CRT is used to improve security and imperceptibility in the watermarking method. CRT is rarely studied in studies that discuss steganographic images. Steganography research focuses more on increasing imperceptibility, embedded payload, and message security, so methods like LSB are still popular to be developed to date. CRT and LSB have some similarities such as default payload capacity and both are methods in the spatial domain which can produce good imperceptibility quality of stego image. But CRT is very superior in terms of security, so CRT is also widely used in cryptographic algorithms. Some ways to increase imperceptibility in image steganography are edge detection and spread spectrum embedding. This research proposes a combination of edge detection techniques and spread-spectrum embedding based on the CRT method to produce imperceptibility and safe image steganography method. Based on the test results it is proven that the combination of the proposed methods can increase imperceptibility of CRT-based steganography based on SSIM metric.

In this paper, we focus on versatile and scalable key management for Advanced Metering Infrastructure (AMI) in Smart Grid (SG). We show that a recently proposed key graph based scheme for AMI systems (VerSAMI) suffers from efficiency flaws in its broadcast key management protocol. Then, we propose a new key management scheme (iVerSAMI) by modifying VerSAMI's key graph structure and proposing a new broadcast key update process. We analyze security and performance of the proposed broadcast key management in details to show that iVerSAMI is secure and efficient in terms of storage and communication overheads.

To preserve the privacy of social networks, most existing methods are applied to satisfy different anonymity models, but there are some serious problems such as huge large information losses and great structural modifications of original social network. Therefore, an improved privacy protection method called k-subgraph is proposed, which is based on k-degree anonymous graph derived from k-anonymity to keep the network structure stable. The method firstly divides network nodes into several clusters by label propagation algorithm, and then reconstructs the sub-graph by means of moving edges to achieve k-degree anonymity. Experimental results show that our k-subgraph method can not only effectively improve the defense capability against malicious attacks based on node degrees, but also maintain stability of network structure. In addition, the cost of information losses due to anonymity is minimized ideally.

Deep learning has undergone tremendous advancements in computer vision studies. The training of deep learning neural networks depends on a considerable amount of ground truth datasets. However, labeling ground truth data is a labor-intensive task, particularly for large-volume video analytics applications such as video surveillance and vehicles detection for autonomous driving. This paper presents a rapid and accurate method for associative searching in big image data obtained from security monitoring systems. We developed a semi-automatic moving object annotation method for improving deep learning models. The proposed method comprises three stages, namely automatic foreground object extraction, object annotation in subsequent video frames, and dataset construction using human-in-the-loop quick selection. Furthermore, the proposed method expedites dataset collection and ground truth annotation processes. In contrast to data augmentation and data generative models, the proposed method produces a large amount of real data, which may facilitate training results and avoid adverse effects engendered by artifactual data. We applied the constructed annotation dataset to train a deep learning you-only-look-once (YOLO) model to perform vehicle detection on street intersection surveillance videos. Experimental results demonstrated that the accurate detection performance was improved from a mean average precision (mAP) of 83.99 to 88.03.

The rapid growth of computer systems which generate graph data necessitates employing privacy-preserving mechanisms to protect users' identity. Since structure-based de-anonymization attacks can reveal users' identity's even when the graph is simply anonymized by employing naïve ID removal, recently, k- anonymity is proposed to secure users' privacy against the structure-based attack. Most of the work ensured graph privacy using fake edges, however, in some applications, edge addition or deletion might cause a significant change to the key property of the graph. Motivated by this fact, in this paper, we introduce a novel method which ensures privacy by adding fake nodes to the graph. First, we present a novel model which provides k- anonymity against one of the strongest attacks: seed-based attack. In this attack, the adversary knows the partial mapping between the main graph and the graph which is generated using the privacy-preserving mechanisms. We show that even if the adversary knows the mapping of all of the nodes except one, the last node can still have k- anonymity privacy. Then, we turn our attention to the privacy of the graphs generated by inter-domain routing against degree attacks in which the degree sequence of the graph is known to the adversary. To ensure the privacy of networks against this attack, we propose a novel method which tries to add fake nodes in a way that the degree of all nodes have the same expected value.

In today's interconnected world, universities recognize the importance of protecting their information assets from internal and external threats. Being the possible insider threats to Information Security, employees are often coined as the weakest link. Both employees and organizations should be aware of this raising challenge. Understanding staff perception of compliance behaviour is critical for universities wanting to leverage their staff capabilities to mitigate Information Security risks. Therefore, this research seeks to get insights into staff perception based on factors adopted from several theories by using proposed constructs i.e. "perceived" practices/policies and "perceived" intention to comply. Drawing from the General Deterrence Theory, Protection Motivation Theory, Theory of Planned Behaviour and Information Reinforcement, within the context of Palestine universities, this paper integrates staff awareness of Information Security Policies (ISP) countermeasures as antecedents to ``perceived'' influencing factors (perceived sanctions, perceived rewards, perceived coping appraisal, and perceived information reinforcement). The empirical study is designed to follow a quantitative research approaches, use survey as a data collection method and questionnaires as the research instruments. Partial least squares structural equation modelling is used to inspect the reliability and validity of the measurement model and hypotheses testing for the structural model. The research covers ISP awareness among staff and seeks to assert that information security is the responsibility of all academic and administrative staff from all departments. Overall, our pilot study findings seem promising, and we found strong support for our theoretical model.

Multipath fading as well as shadowing is liable for the leakage of confidential information from the wireless channels. In this paper a solution to this information leakage is proposed, where a source transmits signal through a α-μ/α-μ composite fading channel considering an eavesdropper is present in the system. Secrecy enhancement is investigated with the help of two fading parameters α and μ. To mitigate the impacts of shadowing a α-μ distribution is considered whose mean is another α-μ distribution which helps to moderate the effects multipath fading. The mathematical expressions of some secrecy matrices such as the probability of non-zero secrecy capacity and the secure outage probability are obtained in closed-form to analyze security of the wireless channel in light of the channel parameters. Finally, Monte-Carlo simulations are provided to justify the correctness of the derived expressions.

Correct compilers perform program transformations preserving input/output behaviours of programs. Yet, correctness does not prevent program optimisations from introducing information-flow leaks that would make the target program more vulnerable to side-channel attacks than the source program. To tackle this problem, we propose a notion of Information-Flow Preserving (IFP) program transformation which ensures that a target program is no more vulnerable to passive side-channel attacks than a source program. To protect against a wide range of attacks, we model an attacker who is granted arbitrary memory accesses for a pre-defined set of observation points. We propose a compositional proof principle for proving that a transformation is IFP. Using this principle, we show how a translation validation technique can be used to automatically verify and even close information-flow leaks introduced by standard compiler passes such as dead-store elimination and register allocation. The technique has been experimentally validated on the CompCert C compiler.

Spams are unsolicited and unnecessary messages which may contain harmful codes or links for activation of malicious viruses and spywares. Increasing popularity of social networks attracts the spammers to perform malicious activities in social networks. So an efficient spam detection method is necessary for social networks. In this paper, feed forward neural network with back propagation based spam detection model is proposed. The quality of the learning process is improved by tuning initial weights of feed forward neural network using proposed enhanced step size firefly algorithm which reduces the time for finding optimal weights during the learning process. The model is applied for twitter dataset and the experimental results show that, the proposed model performs well in terms of accuracy and detection rate and has lower false positive rate.