Biblio

In recent trends, privacy preservation is the most predominant factor, on big data analytics and cloud computing. Every organization collects personal data from the users actively or passively. Publishing this data for research and other analytics without removing Personally Identifiable Information (PII) will lead to the privacy breach. Existing anonymization techniques are failing to maintain the balance between data privacy and data utility. In order to provide a trade-off between the privacy of the users and data utility, a Mondrian based k-anonymity approach is proposed. To protect the privacy of high-dimensional data Deep Neural Network (DNN) based framework is proposed. The experimental result shows that the proposed approach mitigates the information loss of the data without compromising privacy.

Once an individual employs the use of the Internet for accessing information; carrying out transactions and sharing of data on the Cloud, they are connected to diverse computers on the network. As such, security of such transmitted data is most threatened and then potentially creating privacy risks of users on the federated identity management system in the Cloud. Usually, User's attributes or Personal Identifiable Information (PII) are needed to access Services on the Cloud from different Service Providers (SPs). Sometime these SPs may by themselves violate user's privacy by the reuse of user's attributes offered them for the release of services to the users without their consent and then carrying out activities that may appear malicious and then causing damage to the users. Similarly, it should be noted that sensitive user's attributes (e.g. first name, email, address and the likes) are received in their original form by needed SPs in plaintext. As a result of these problems, user's privacy is being violated. Since these SPs may reuse them or connive with other SPs to expose a user's identity in the cloud environment. This research is motivated to provide a protective and novel approach that shall no longer release original user's attributes to SPs but pseudonyms that shall prevent the SPs from violating user's privacy through connivance to expose the user's identity or other means. The paper introduces a conceptual framework for the proposed user's attributes privacy protection in a federated identity management system for the cloud. On the proposed system, the use of pseudonymous technique also called Privacy Token (PT) is employed. The pseudonymous technique ensures users' original attributes values are not sent directly to the SP but auto generated pseudo attributes values. The PT is composed of: Pseudo Attribute values, Timestamp and SPİD. These composition of the PT makes it difficult for the User's PII to be revealed and further preventing the SPs from being able to keep them or reuse them in the future without the user's consent for any purpose. Another important feature of the PT is its ability to forestall collusion among several collaborating service providers. This is due to the fact that each SP receives pseudo values that have no direct link to the identity of the user. The prototype was implemented with Java programming language and its performance tested on CloudAnalyst simulation.

File update operations generate many invalid flash pages in Solid State Drives (SSDs) because of the-of-place update feature. If these invalid flash pages are not securely deleted, they will be left in the “missing” state, resulting in leakage of sensitive information. However, deleting these invalid pages in real time greatly reduces the performance of SSD. In this paper, we propose a Per-File Secure Deletion (PSD) scheme for SSD to achieve non-real-time secure deletion. PSD assigns a globally unique identifier (GUID) to each file to quickly locate the invalid data blocks and uses Security-TRIM command to securely delete these invalid data blocks. Moreover, we propose a PSD-MLC scheme for Multi-Level Cell (MLC) flash memory. PSD-MLC distributes the data blocks of a file in pairs of pages to avoid the influence of programming crosstalk between paired pages. We evaluate our schemes on different hardware platforms of flash media, and the results prove that PSD and PSD-MLC only have little impact on the performance of SSD. When the cache is disabled and enabled, compared with the system without the secure deletion, PSD decreases SSD throughput by 1.3% and 1.8%, respectively. PSD-MLC decreases SSD throughput by 9.5% and 10.0%, respectively.

As an efficient deletion method, unlinking is widely used in cloud storage. While unlinking is a kind of incomplete deletion, `deleted data' remains on cloud and can be recovered. To make `deleted data' unrecoverable, overwriting is an effective method on cloud. Users lose control over their data on cloud once deleted, so it is difficult for them to confirm overwriting. In face of such a crucial problem, we propose a Provable and Traceable Assured Deletion (PTAD) scheme in cloud storage based on blockchain. PTAD scheme relies on overwriting to achieve assured deletion. We reference the idea of data integrity checking and design algorithms to verify if cloud overwrites original blocks properly as specific patterns. We utilize technique of smart contract in blockchain to automatically execute verification and keep transaction in ledger for tracking. The whole scheme can be divided into three stages-unlinking, overwriting and verification-and we design one specific algorithm for each stage. For evaluation, we implement PTAD scheme on cloud and construct a consortium chain with Hyperledger Fabric. The performance shows that PTAD scheme is effective and feasible.

Machine learning is a major area in artificial intelligence, which enables computer to learn itself explicitly without programming. As machine learning is widely used in making decision automatically, attackers have strong intention to manipulate the prediction generated my machine learning model. In this paper we study about the different types of attacks and its countermeasures on machine learning model. By research we found that there are many security threats in various algorithms such as K-nearest-neighbors (KNN) classifier, random forest, AdaBoost, support vector machine (SVM), decision tree, we revisit existing security threads and check what are the possible countermeasures during the training and prediction phase of machine learning model. In machine learning model there are 2 types of attacks that is causative attack which occurs during the training phase and exploratory attack which occurs during the prediction phase, we will also discuss about the countermeasures on machine learning model, the countermeasures are data sanitization, algorithm robustness enhancement, and privacy preserving techniques.

Event logs that originate from information systems enable comprehensive analysis of business processes, e.g., by process model discovery. However, logs potentially contain sensitive information about individual employees involved in process execution that are only partially hidden by an obfuscation of the event data. In this paper, we therefore address the risk of privacy-disclosure attacks on event logs with pseudonymized employee information. To this end, we introduce PRETSA, a novel algorithm for event log sanitization that provides privacy guarantees in terms of k-anonymity and t-closeness. It thereby avoids disclosure of employee identities, their membership in the event log, and their characterization based on sensitive attributes, such as performance information. Through step-wise transformations of a prefix-tree representation of an event log, we maintain its high utility for discovery of a performance-annotated process model. Experiments with real-world data demonstrate that sanitization with PRETSA yields event logs of higher utility compared to methods that exploit frequency-based filtering, while providing the same privacy guarantees.

Signal processing in encrypted domain has become an important mean to protect privacy in an untrusted network environment. Due to the limitations of the underlying encryption methods, many useful algorithms that are sophisticated are not well implemented. Considering that QR decomposition is widely used in many fields, in this paper, we propose to implement QR decomposition in homomorphic encrypted domain. We firstly realize some necessary primitive operations in homomorphic encrypted domain, including division and open square operation. Gram-Schmidt process is then studied in the encrypted domain. We propose the implementation of QR decomposition in the encrypted domain by using the secure implementation of Gram-Schmidt process. We conduct experiments to demonstrate the effectiveness and analyze the performance of the proposed outsourced QR decomposition.

This paper proposes an audio watermarking algorithm having good balance between perceptual transparency, robustness, and payload. The proposed algorithm is based on Cordic QR decomposition and multi-resolution decomposition meeting all the necessary audio watermarking design requirements. The use of Cordic QR decomposition provides good robustness and use of detailed coefficients of multi-resolution decomposition help to obtain good transparency at high payload. Also, the proposed algorithm does not require original signal or the embedded watermark for extraction. The binary data embedding capacity of the proposed algorithm is 960.4 bps and the highest SNR obtained is 35.1380 dB. The results obtained in this paper show that the proposed method has good perceptual transparency, high payload and robustness under various audio signal processing attacks.

In the field of network traffic analysis, Deep Packet Inspection (DPI) technology is widely used at present. However, the increase in network traffic has brought tremendous processing pressure on the DPI. Consequently, detection speed has become the bottleneck of the entire application. In order to speed up the traffic detection of DPI, a lot of research works have been applied to improve signature matching algorithms, which is the most influential factor in DPI performance. In this paper, we present a novel method from a different angle called Precisely Guided Signature Matching (PGSM). Instead of matching packets with signature directly, we use supervised learning to automate the rules of specific protocol in PGSM. By testing the performance of a packet in the rules, the target packet could be decided when and which signatures should be matched with. Thus, the PGSM method reduces the number of aimless matches which are useless and numerous. After proposing PGSM, we build a framework called PGSM-DPI to verify the effectiveness of guidance rules. The PGSM-DPI framework consists of PGSM method and open source DPI library. The framework is running on a distributed platform with better throughput and computational performance. Finally, the experimental results demonstrate that our PGSM-DPI can reduce 59.23% original DPI time and increase 21.31% throughput. Besides, all source codes and experimental results can be accessed on our GitHub.

Surveillance cameras, which is a form of Cyber Physical System, are deployed extensively to provide visual surveillance monitoring of activities of interest or anomalies. However, these cameras are at risks of physical security attacks against their physical attributes or configuration like tampering of their recording coverage, camera positions or recording configurations like focus and zoom factors. Such adversarial alteration of physical configuration could also be invoked through cyber security attacks against the camera's software vulnerabilities to administratively change the camera's physical configuration settings. When such Cyber Physical attacks occur, they affect the integrity of the targeted cameras that would in turn render these cameras ineffective in fulfilling the intended security functions. There is a significant measure of research work in detection mechanisms of cyber-attacks against these Cyber Physical devices, however it is understudied area with such mechanisms against integrity attacks on physical configuration. This research proposes the use of the novel use of deep learning algorithms to detect such physical attacks originating from cyber or physical spaces. Additionally, we proposed the novel use of deep learning-based video frame interpolation for such detection that has comparatively better performance to other anomaly detectors in spatiotemporal environments.

This paper presents a methodology for utilizing Phasor Measurement units (PMUs) for procuring real time synchronized measurements for assessing the security of the power system dynamically. The concept of wide-area dynamic security assessment considers transient instability in the proposed methodology. Intelligent framework based approach for online dynamic security assessment has been suggested wherein the database consisting of critical features associated with the system is generated for a wide range of contingencies, which is utilized to build the data mining model. This data mining model along with the synchronized phasor measurements is expected to assist the system operator in assessing the security of the system pertaining to a particular contingency, thereby also creating possibility of incorporating control and preventive measures in order to avoid any unforeseen instability in the system. The proposed technique has been implemented on IEEE 39 bus system for accurately indicating the security of the system and is found to be quite robust in the case of noise in the measurement data obtained from the PMUs.

Due to greater network capacity and faster data speed, fifth generation (5G) technology is expected to provide a huge improvement in Internet of Things (IoTs) applications, Augmented & Virtual Reality (AR/VR) technologies, and Machine Type Communications (MTC). Consumer will be able to send/receive high quality multimedia data. For the protection of sensitive multimedia data, a large number of encryption algorithms are available, however, these encryption schemes does not provide light-weight encryption solution for real-time application requirements. This paper proposes a new multi-chaos computational efficient encryption for digital images. In the proposed scheme, plaintext image is transformed using Lifting Wavelet Transform (LWT) and only one-fourth part of the transformed image is encrypted using light-weight Chebyshev and Intertwining maps. Both chaotic maps were chaotically coupled for the confusion and diffusion processes which further enhances the image security. Encryption/decryption speed and other security measures such as correlation coefficient, entropy, Number of Pixels Change Rate (NPCR), contrast, energy, homogeneity confirm the superiority of the proposed light-weight encryption scheme.

In last few years, Proxy Re-Encryption has been used for forwarding the encrypted message to the user, these users are the one who has not been a part of encryption. In the past several scheme were developed in order to provide the efficient and secure proxy re-encryption. However, these methodology mainly focused on features like maximum key privacy, minimal trust proxy and others. In such cases the efficiency and security was mainly ignored. Hence, in order to provide the efficient and secure proxy re-encryption, we proposed an algorithm named as MLBC (Modified Lattice Based Cryptography) is proposed. Our method is based on the PKE (Public Key Encryption) and it provides more efficiency when compared to the other cryptography technique. Later in order to evaluate the algorithm simulation is done based on several parameter such as encryption time, proxy key generation time, Re-encryption time and Total computation time. Later, it is compared with the existing algorithm and the plotted graph clearly shows that our algorithm outperforms the existing algorithm.

Recent breakthrough in neural network has led to the birth of Convolutional neural network (CNN) which has been found to be very efficient especially in the areas of image recognition and classification. This success is traceable to the availability of large datasets and its capability to learn salient and complex data features which subsequently produce a reusable output model (Fθ). The Fθ are often made available (e.g. on cloud as-a-service) for others (client) to train their data or do transfer learning, however, an adversary can perpetrate a model inversion attack on the model Fθ to recover training data, hence compromising the sensitivity of the model buildup data. This is possible because CNN as a variant of deep neural network does memorize most of its training data during learning. Consequently, this has pose a privacy concern especially when a medical or financial data are used as model buildup data. Existing researches that proffers privacy preserving approach however suffer from significant accuracy degradation and this has left privacy preserving model on a theoretical desk. In this paper, we proposed an ϵ-tuple differential privacy approach that is based on neuron impact factor estimation to preserve privacy of CNN model without significant accuracy degradation. We experiment our approach on two large datasets and the result shows no significant accuracy degradation.

Modern large scale technical systems often face iterative changes on their behaviours with the requirement of validated quality which is not easy to achieve completely with traditional testing. Regression verification is a powerful tool for the formal correctness analysis of software-driven systems. By proving that a new revision of the software behaves similarly as the original version of the software, some of the trust that the old software and system had earned during the validation processes or operation histories can be inherited to the new revision. This trust inheritance by the formal analysis relies on a number of implicit assumptions which are not self-evident but easy to miss, and may lead to a false sense of safety induced by a misunderstood regression verification processes. This paper aims at pointing out hidden, implicit assumptions of regression verification in the context of cyber-physical systems by making them explicit using practical examples. The explicit trust inheritance analysis would clarify for the engineers to understand the extent of the trust that regression verification provides and consequently facilitate them to utilize this formal technique for the system validation.

An experiment and numerical simulations analyze low-speed OSC derived XPM-induced phase noise penalty in 100-Gbps WDM systems. WDM transmission performance exhibits signal bit-pattern dependence on OSC, which is due to deterioration in SD-FEC performance.

Quick UDP Internet Connections (QUIC) is an experimental transport protocol designed to primarily reduce connection establishment and transport latency, as well as to improve security standards with default end-to-end encryption in HTTPbased applications. QUIC is a multiplexed and secure transport protocol fostered by Google and its design emerged from the urgent need of innovation in the transport layer, mainly due to difficulties extending TCP and deploying new protocols. While still under standardisation, a non-negligble fraction of the Internet's traffic, more than 7% of a European Tier1-ISP, is already running over QUIC and it constitutes more than 30% of Google's egress traffic [1].

This article presents a practical approach for secure key exchange exploiting reciprocity in wireless transmission. The method relies on the reciprocal channel phase to mask points of a Phase Shift Keying (PSK) constellation. Masking is achieved by adding (modulo 2π) the measured reciprocal channel phase to the PSK constellation points carrying some of the key bits. As the channel phase is uniformly distributed in [0, 2π], knowing the sum of the two phases does not disclose any information about any of its two components. To enlarge the key size over a static or slow fading channel, the Radio Frequency (RF) propagation path is perturbed to create independent realizations of multi-path fading. Prior techniques have relied on quantizing the reciprocal channel state measured at the two ends and thereby suffer from information leakage in the process of key consolidation (ensuring the two ends have access to the same key). The proposed method does not suffer from such shortcomings as raw key bits can be equipped with Forward Error Correction (FEC) without affecting the masking (zero information leakage) property. To eavesdrop a phase value shared in this manner, the Eavesdropper (Eve) would require to solve a system of linear equations defined over angles, each equation corresponding to a possible measurement by the Eve. Channel perturbation is performed such that each new channel state creates an independent channel realization for the legitimate nodes, as well as for each of Eves antennas. As a result, regardless of the Eves Signal-to-Noise Ratio (SNR) and number of antennas, Eve will always face an under-determined system of equations. On the other hand, trying to solve any such under-determined system of linear equations in terms of an unknown phase will not reveal any useful information about the actual answer, meaning that the distribution of the answer remains uniform in [0, 2π].

It seems impossible to certify that a remote hosting service does not leak its users' data - or does quantum mechanics make it possible? We investigate if a server hosting data can information-theoretically prove its definite deletion using a "BB84-like" protocol. To do so, we first rigorously introduce an alternative to privacy by encryption: privacy delegation. We then apply this novel concept to provable deletion and remote data storage. For both tasks, we present a protocol, sketch its partial security, and display its vulnerability to eavesdropping attacks targeting only a few bits.

The EU SoftFIRE project has built an experimentation platform for NFV and SDN experiments, tailored for testing and evaluating 5G network applications and solutions. The platform is a fully orchestrated virtualisation testbed consisting of multiple component testbeds across Europe. Users of the platform can deploy their virtualisation experiments via the platform's Middleware. This paper introduces the SoftFIRE testbed and its Middleware, and presents a set of KPI results for evaluation of experiment deployment performance.

In current, RFID (Radio Frequency Identification) Middleware is widely used in nearly all RFID applications, and provides service for raw data capturing, security data reading/writing as well as sensors controlling. However, as the existing Middlewares were organized with rigorous data comparison and data encryption, it is seriously affecting the usefulness and execution efficiency. Thus, in order to improve the utilization rate of effective data, increase the reading/writing speed as well as preserving the security of RFID, this paper proposed a PSO (Particle swarm optimization) clustering scheme to accelerate the procedure of data operation. Then with the help of PSO cluster, the RFID Middleware can provide better service for both data security and data availability. At last, a comparative experiment is proposed, which is used to further verify the advantage of our proposed scheme.

With the emergence of the Internet of Things (IoT) and the increasing number of resource-constrained interconnected smart devices, there is a noticeable increase in the number of cyber security crimes. In the face of the possible attacks on IoT networks such as network intrusion, denial of service, spoofing and so on, there is a need to develop efficient methods to locate vulnerabilities and mitigate attacks in IoT networks. Without loss of generality, we consider only intrusion-related threats to IoT. A honeypot is a system used to understand the potential dynamic threats and act as a proactive measure to detect any intrusion into the network. It is used as a trap for intruders to control unauthorized access to the network by analyzing malicious traffic. However, a sophisticated attacker can detect the presence of a honeypot and abort the intrusion mission. Therefore it is essential for honeypots to be undetectable. In this paper, we study and analyze possible techniques for SSH and telnet honeypot detection. Moreover, we propose a new methodology for probabilistic estimation of honeypot detection and an automated software implemented this methodology.

Mobile Ad-hoc NETwork (MANET) is a collection of mobile devices which interchange information without the use of predefined infrastructures or central administration. It is employed in many domains such as military and commercial sectors, data and sensors networks, low level applications, etc. The important constraints in this network are the limitation of bandwidth, processing capabilities and battery life. The choice of an effective routing protocol is primordial. From many routing protocols developed for MANET, OLSR protocol is a widely-used proactive routing protocol which diffuses topological information periodically. Thus, every node has a global vision of the entire network. The protocol assumes, like the other protocols, that the nodes cooperate in a trusted environment. So, all control messages are transmitted (HELLO messages) to all 1-hop neighbor nodes or broadcasted (TC and MID messages) to the entire network in clear. However, a node, which listens to OLSR control messages, can exploit this property to lead an attack. In this paper, we investigate on MultiPoint Relay (MPR) attack considered like one of the efficient OLSR attacks by using a simulation in progressive size gridMANET.

The nodes in Mobile Ad hoc Network (MANET) can self-assemble themselves, locomote unreservedly and can interact with one another without taking any help from a centralized authority or fixed infrastructure. Due to its continuously changing and self-organizing nature, MANET is vulnerable to a variety of attacks like spoofing attack, wormhole attack, black hole attack, etc. This paper compares and analyzes the repercussion of the wormhole attack on MANET's two common routing protocols of reactive category, specifically, Dynamic Source Routing (DSR) and Ad-hoc On-Demand Distance Vector (AODV) by increasing the number of wormhole tunnels in MANET. The results received by simulation will reveal that DSR is greatly affected by this attack. So, as a solution, a routing algorithm for DSR which is based on trust is proposed to prevent the routes from caching malicious nodes.

Mobile ad hoc network is a group of mobile nodes which have no centralized administrator. MANETs have dynamic topology since the nodes are moving. For this reason it is more prone to attacks that any node may be a misbehaving node. Every node acts as a router thereby it may lead the network with wrong routing. For these reasons MANETs have to be more protected than the wired networks. The mobile nodes will lavishly consume energy and so a security scheme that consumes less energy still provides ample protection to the messages have to be introduced. Here we propose an encryption scheme for the messages passing through MANET. The security scheme is based on selective encryption that is very robust, simple and with less computational capability.