Biblio

The complete digitization of telecommunications allows new attack scenarios, which have not been possible with legacy phone technologies before. The reason is that physical access to legacy phone technologies was necessary. Regarding internet-based communication like voice over the internet protocol (VoIP), which can be established between random nodes, eavesdropping can happen everywhere and much easier. Additionally, injection of undesirable communication like SPAM or SPIT in digital networks is simpler, too. Encryption is not sufficient because it is also necessary to know which participants are talking to each other. For that reason, the research project INTEGER has been started with the main goals of providing secure authentication and integrity of a VoIP communication by using a digital signature. The basis of this approach is the Trusted Platform Module (TPM) of the Trusted Computing Group (TCG) which works as a hardware-based trusted anchor. The TPM will be used inside of wireless IP devices with VoIP softphones. The question is if it is possible to fulfill the main goals of the project in wireless scenarios with Wi-Fi technologies. That is what this contribution aims to clarify.

Industrial Internet of Things (IIoT) is a trend of the smart industry. By collecting field data from sensors, the industry can make decisions dynamically in time for better performance. In most cases, IIoT is built on private networks and cannot be reached from the Internet. Currently, data transmission in most of IIoT network protocols is in plaintext without encryption protection. Once an attacker breaks into the field, the attacker can intercept data and injects malicious commands to field agents. In this paper, we propose a compound approach for defending command injection attacks in IIOT. First, we leverage the power of Software Defined Networking (SDN) to detect the injection attack. When the injection attack event is detected, the system owner is alarmed that someone tries to pretend a controller or a field agent to deceive the other entity. Second, we develop a lightweight authentication scheme to ensure the identity of the command sender. Command receiver can verify commands first before processing commands.

Secure multiparty computation (SMC) is a promising technology for privacy-preserving collaborative computation. In the last years several feasibility studies have shown its practical applicability in different fields. However, it is recognized that administration, and management overhead of SMC solutions are still a problem. A vital next step is the incorporation of SMC in the emerging fields of the Internet of Things and (smart) dynamic environments. In these settings, the properties of these contexts make utilization of SMC even more challenging since some vital premises for its application regarding environmental stability and preliminary configuration are not initially fulfilled. We bridge this gap by providing FlexSMC, a management and orchestration framework for SMC which supports the discovery of nodes, supports a trust establishment between them and realizes robustness of SMC session by handling nodes failures and communication interruptions. The practical evaluation of FlexSMC shows that it enables the application of SMC in dynamic environments with reasonable performance penalties and computation durations allowing soft real-time and interactive use cases.

Recently, gray-box attacks on white-box cryptographic implementations have succeeded. These attacks are more efficient than white-box attacks because they can be performed without detailed knowledge of the target implementation. The success of the gray-box attack is reportedly due to the unbalanced encodings used to generate the white-box lookup table. In this paper, we propose a method to protect the gray-box attack against white-box implementations. The basic idea is to apply the masking technique before encoding intermediate values during the white-box lookup table generation. Because we do not require any random source in runtime, it is possible to perform efficient encryption and decryption using our method. The security and performance analysis shows that the proposed method can be a reliable and efficient countermeasure.

The number of Virtual Reality applications has increased tremendously in the recent years to the point where every single digital entertainment company is investing heavily in VR systems. This increase in VR products demands the improvement in the evaluation of VR experience since current evaluations require an attendee per survey taker and can only move onto the next survey taker after the current survey is done. Traditional evaluations also require many evaluation machines if done digitally, costing survey takers unnecessary expenses. "MasQueRade" is a QR code based instant user feedback online system. This system allows users to scan the QR code on their VR sanitary masks and access an online evaluation system on their own mobile phones. This enables users to conduct the evaluation on their own free time and decreases the expenses surveyors have to spend on machines, therefore greatly decreases the manpower and time required to conduct the evaluations. While this approach to solving the issue of obtaining user feedback may sound elementary, the amount of efforts and resources "MasQueRade" saves by transferring the evaluation from a paper or digital form into an online database gives near infinite possibilities in the future of gathering feedback and evaluation. This paper seeks to explain the functions of "MasQueRade" and the results the team obtains during Anime Expo 2017 and propose a real-time live user VR commentary system drawing inputs form the attendees.

Web attacks have proliferated across the whole Internet in recent years. To protect websites, security vendors and researchers collect attack information using web honeypots. However, web attackers can hide themselves by using stepping stones (e.g., VPN, encrypted proxy) or anonymous networks (e.g., Tor network). Conventional web honeypots lack an effective way to gather information about an attacker's identity, which raises a big obstacle for cybercrime traceability and forensics. Traditional forensics methods are based on traffic analysis; it requires that defenders gain access to the entire network. It is not suitable for honeypots. In this paper, we present the design, implementation, and deployment of the Micro-Honeypot, which aims to use the browser fingerprinting technique to track a web attacker. Traditional honeypot lure attackers and records attacker's activity. Micro-Honeypot is deployed in a honeypot. It will run and gather identity information when an attacker visits the honeypot. Our preliminary results show that Micro-Honeypot could collect more information and track attackers although they might have used proxies or anonymous networks to hide themselves.

Modern mobile operating systems store a lot of excessive information that can be used against its owner or organization, like a call history or various system logs. This article describes a universal way of preventing any mobile operating system or application from saving its data in device's internal storage without reducing their functionality. The goal of this work is creation of a software that solves the described problem and works on the bootloading stage. A general algorithm of the designed software, along with its main solutions and requirements, is presented in this paper. Hardware requirement, software testing results and general applications of this software are also listed in this paper.

We study the problem of dynamic symmetric searchable encryption. In that setting, it is crucial to minimize the information revealed to the server as a result of update operations (insertions and deletions). Two relevant privacy properties have been defined in that context: forward and backward privacy. The first makes it hard for the server to link an update operation with previous queries and has been extensively studied in the literature. The second limits what the server can learn about entries that were deleted from the database, from queries that happen after the deletion. Backward privacy was formally studied only recently (Bost et al., CCS 2017) in a work that introduced a formal definition with three variable types of leakage (Type-I to Type-III ordered from most to least secure), as well as the only existing schemes that satisfy this property. In this work, we introduce three novel constructions that improve previous results in multiple ways. The first scheme achieves Type-II backward privacy and our experimental evaluation shows it has 145-253X faster search computation times than previous constructions with the same leakage. Surprisingly, it is faster even than schemes with Type-III leakage which makes it the most efficient implementation of a forward and backward private scheme so far. The second one has search time that is asymptotically within a polylogarithmic multiplicative factor of the theoretical optimal (i.e., the result size of a search), and it achieves the strongest level of backward privacy (Type-I). All previous Type-I constructions require time that is at least linear in the total number of updates for the requested keywords, even the (arbitrarily many) previously deleted ones. Our final scheme improves upon the second one by reducing the number of roundtrips for a search at the cost of extra leakage (Type-III).

Cloud offers flexible and cost effective storage for big data but the major challenge is access control of big data processing. CP-ABE is a desirable solution for data access control in cloud. However, in CP-ABE the access policy may leak user's private information. To address this issue, Hidden Policy CP-ABE schemes proposed but those schemes still causing data leakage problem because the access policies are partially hidden and create more computational cost. In this paper, we propose a New Hidden Policy Ciphertext Policy Attribute Based Encryption (HP-CP-ABE) to ensure Big Data Access Control with Privacy-preserving Policy in Cloud. In proposed method, we used Multi Secret Sharing Scheme(MSSS) to reduce the computational overhead, while encryption and decryption process. We also applied mask technique on each attribute in access policy and embed the access policy in ciphertext, to protect user's private information from access policy. The security analysis shows that HP-CP-ABE is more secure and preserve the access policy privacy. Performance evaluation shows that our schemes takes less computational cost than existing scheme.

The manufacturing of integrated circuits (IC) outside of the design houses makes it possible for the adversary to easily perform a reverse engineering attack against intellectual property (IP)/IC. The aim of this attack can be the IP piracy, overproduction, counterfeiting or inserting hardware Trojan (HT) throughout the supply chain of the IC. Preventing hardware Trojan insertion is a significant issue in the context of hardware security (HS) and has not been considered in most of the previous logic encryption methods. To eliminate this problem, in this paper an Anti-Trojan insertion algorithm is presented. The idea is based on the fact that reducing the signals with low-observability (LO) and low-controllability (LC) can prevent HT insertion significantly. The security of logic encryption methods depends on the algorithm and the encryption key. However, the security of these methods has been compromised by SAT attacks over recent years. SAT attacks, can decode the correct key from most logic encryption techniques. In this article, by using the PUF-based encryption, the applied key in the encryption is randomized and SAT attack cannot be performed. Based on the output of PUF, a unique encryption has been made for each chip that preventing from counterfeiting and IP piracy.

We propose a novel framework for outsourced private function evaluation with privacy policy enforcement (OPFE-PPE). Suppose an evaluator evaluates a function with private data contributed by a data contributor, and a client obtains the result of the evaluation. OPFE-PPE enables a data contributor to enforce two different kinds of privacy policies to the process of function evaluation: evaluator policy and client policy. An evaluator policy restricts entities that can conduct function evaluation with the data. A client policy restricts entities that can obtain the result of function evaluation. We demonstrate our construction with three applications: personalized medication, genetic epidemiology, and prediction by machine learning. Experimental results show that the overhead caused by enforcing the two privacy policies is less than 10% compared to function evaluation by homomorphic encryption without any privacy policy enforcement.

Data's security being important aspect of the today's internet is gaining more importance day by day. With the increase in online data exchange, transactions and payments; secure payment and secure data transfers have become an area of concern. Cryptography makes the data transmission over the internet secure by various methods, algorithms. Cryptography helps in avoiding the unauthorized people accessing the data by authentication, confidentiality, integrity and non-repudiation. In order to securely transmit the data many cryptographic algorithms are present, but the algorithm to be used should be robust, efficient, cost effective, high performance and easily deployable. Choosing an algorithm which suits the customer's requirement is an utmost important task. The proposed work discusses different symmetric key cryptographic algorithms like DES, 3DES, AES and Blowfish by considering encryption time, decryption time, entropy, memory usage, throughput, avalanche effect and energy consumption by practical implementation using java. Practical implementation of algorithms has been highlighted in proposed work considering tradeoff performance in terms of cost of various parameters rather than mere theoretical concepts. Battery consumption and avalanche effect of algorithms has been discussed. It reveals that AES performs very well in overall performance analysis among considered algorithms.

Big Data has rapidly developed into a hot research topic in many areas that attracts attention from academia and industry around the world. Many organization demands efficient solution to store, process, analyze and search huge amount of information. With the rapid development of cloud computing, organization prefers cloud storage services to reduce the overhead of storing data locally. However, the security and privacy of big data in cloud computing is a major source of concern. One of the positive ways of protecting data is encrypting it before outsourcing to remote servers, but the encrypted significant amounts of cloud data brings difficulties for the remote servers to perform any keyword search functions without leaking information. Various privacy-preserving keyword search (PPKS) schemes have been proposed to mitigate the privacy issue of big data encrypted on cloud storage. This paper presents an extensive analysis of the existing PPKS techniques in terms of verifiability, efficiency and data privacy. Through this analysis, we present some valuable directions for future work.

We investigate how to protect features corresponding to personal information using homomorphic encryption when matching people in several camera views. Homomorphic encryption can compute a distance between features without decryption. Thus, our method is able to use a computing server on a public network while protecting personal information. To apply homomorphic encryption, our method uses linear quantization to represent each element of the feature as integers. Experimental results show that there is no significant difference in the accuracy of person re-identification with or without homomorphic encryption and linear quantization.

Deep neural network (DNN) as a popular machine learning model is found to be vulnerable to adversarial attack. This attack constructs adversarial examples by adding small perturbations to the raw input, while appearing unmodified to human eyes but will be misclassified by a well-trained classifier. In this paper, we focus on the black-box attack setting where attackers have almost no access to the underlying models. To conduct black-box attack, a popular approach aims to train a substitute model based on the information queried from the target DNN. The substitute model can then be attacked using existing white-box attack approaches, and the generated adversarial examples will be used to attack the target DNN. Despite its encouraging results, this approach suffers from poor query efficiency, i.e., attackers usually needs to query a huge amount of times to collect enough information for training an accurate substitute model. To this end, we first utilize state-of-the-art white-box attack methods to generate samples for querying, and then introduce an active learning strategy to significantly reduce the number of queries needed. Besides, we also propose a diversity criterion to avoid the sampling bias. Our extensive experimental results on MNIST and CIFAR-10 show that the proposed method can reduce more than 90% of queries while preserve attacking success rates and obtain an accurate substitute model which is more than 85% similar with the target oracle.

Database outsourcing introduces a new paradigm, called Database as a Service (DBaaS). Database Service Providers (DSPs) have the ability to host outsourced databases and provide efficient facilities for their users. However, the data and the execution of database queries are under the control of the DSP, which is not always a trusted authority. Therefore, our problem is to ensure the outsourced database security. To address this problem, we propose a Secure scheme to provide Confidentiality and Integrity of Query results for Cloud Databases (SCIQ-CD). The performance analysis shows that our proposed scheme is secure and efficient for practical deployment.

Information Centric Networking (ICN) has been regarded as an ideal architecture for the next-generation network to handle users' increasing demand for content delivery with in-network cache. While making better use of network resources and providing better delivery service, an effective access control mechanism is needed due to wide dissemination of contents. However, in the existing solutions, making cache-enabled routers or content providers authenticate users' requests causes high computation overhead and unnecessary delay. Also, straightforward utilization of advanced encryption algorithms increases the opportunities for DoS attacks. Besides, privacy protection and service accountability are rarely taken into account in this scenario. In this paper, we propose a secure, efficient, and accountable access control framework, called SEAF, for ICN, in which authentication is performed at the network edge to block unauthorized requests at the very beginning. We adopt group signature to achieve anonymous authentication, and use hash chain technique to greatly reduce the overhead when users make continuous requests for the same file. Furthermore, the content providers can affirm the service amount received from the network and extract feedback information from the signatures and hash chains. By formal security analysis and the comparison with related works, we show that SEAF achieves the expected security goals and possesses more useful features. The experimental results also demonstrate that our design is efficient for routers and content providers, and introduces only slight delay for users' content retrieval.

For location-aware applications in wireless sensor networks (WSNs), it is important to ensure that sensor nodes can get correct locations in a hostile WSNs. Sybil attacks, which are vital threats in WSNs, especially in the distributed WSNs. They can forge one or multiple identities to decrease the localization accuracy, or sometimes to collapse the whole localization systems. In this paper, a novel lightweight sybilfree (SF)-APIT algorithm is presented to solve the problem of sybil attacks in APIT localization scheme, which is a popular range-free method and performs at individual node in a purely distributed fashion. The proposed SF-APIT scheme requires minimal overhead for wireless devices and works well based on the received signal strength. Simulations demonstrate that SF-APIT is an effective scheme in detecting and defending against sybil attacks with a high detection rate in distributed wireless localization schemes.

Recently, the security of state estimation has been attracting significant research attention due to the need for trustworthy situation awareness in emerging (e.g., industrial) cyber-physical systems. In this paper, we investigate secure estimation based on Kalman filtering (SEKF) using partially homomorphically encrypted data. The encryption will enhance the confidentiality not only of data transmitted in the communication network, but also key system information required by the estimator. We use a multiplicative homomorphic encryption scheme, but with a modified decryption algorithm. SEKF is able to conceal comprehensive information (i.e., system parameters, measurements, and state estimates) aggregated at the sink node of the estimator, while retaining the effectiveness of normal Kalman filtering. Therefore, even if an attacker has gained unauthorized access to the estimator and associated communication channels, he will not be able to obtain sufficient knowledge of the system state to guide the attack, e.g., ensure its stealthiness. We present an implementation structure of the SEKF to reduce the communication overhead compared with traditional secure multiparty computation (SMC) methods. Finally, we demonstrate the effectiveness of the SEKF on an IEEE 9-bus power system.

As a result of increasing the interest in developing the communication systems that use public channels for transmitting information, many channel problems are raised up. Among these problems, the important one should be addressed is the information security. This paper presents a proposed communication system with high security uses two encryption levels based on chaotic systems. The first level is chaotic scrambling, while the second one is chaotic masking. This configuration increases the information security since the key space becomes too large. The MATLAB simulation results showed that the Segmental Spectral Signal to Noise Ratio (SSSNR) of the first level (chaotic scrambling) is reduced by -5.195 dB comparing to time domain scrambling. Furthermore, in the second level (chaotic masking), the SSSNR is reduced by -20.679 dB. It is also showed that when the two levels are combined, the overall reduction obtained is -21.755 dB.

A secure voice communications channel is on demand to avoid unwanted eavesdropping of voice messages. This paper reports the development of communicaiton channel prototype equipped with Chaotic cryptographic algorithm with Cipher Feedback mode, implemented on FPGA due to its high processing speed and low delay required for voice channel. Two Spartan-3 FPGA board was used for the purpose, one as transmitter in encryption process and the other as receiver of decryption process. The experimental tests reveal that the voice channel is successfully secured using the encryption-decription cycle for asynchronous communication. In the non-ecrypted channel, the average values of MSE, delay, and THD-N parameters are 0.3513 V2, 202 μs, and 17.52%, respectively, while the secured channel produce MSE of 0.3794 V2, delay 202 μs, and THD-N 20.45%. Therefore, the original information sent in the encrypted channel can be restored with similar quality compared to the non-encrypted channel.

Smart grid systems data has been exposed to several threats and attacks from different perspectives and have resulted in several system failures. Obtaining security of data and key exposure and enhancing system ability in data collection and transmission process are challenging, on the grounds smart grid data is sensitive and enormous sum. In this paper we introduce smart grid data security method along with advanced Cipher text policy attribute based encryption (CP-ABE). Cloud supported IoT is widely used in smart grid systems. Smart IoT devices collect data and perform status management. Data obtained from the IOT devices will be divided into blocks and encrypted data will be stored in different cloud server with different encrypted keys even when one cloud server is assaulted and encrypted key is exposed data cannot be decrypted, thereby the transmission and encryption process are done in correspondingly. We protect access-tree structure information even after the data is shared to user by solving revocation problem in which cloud will inform data owner to revoke and update encryption key after user has downloaded the data, which preserves the data privacy from unauthorized users. The analysis of the system concludes that our proposed system can meet the security requirements in smart grid systems along with cloud-Internet of things.

The Data Encryption Standard (DES) of the multimedia cryptography possesses the weak point of key conducting that is why it reaches to the triple form of DES. However, the triple DES obtains the better characteristic to secure the protection of data to against the attacks, it still contains an extremely inappropriate performance (speed) and efficiency in doing so. This paper provides the effective performance and the results of a single and triple chaotic cryptography using chaos in digital filter, compare to DES and triple DES. This comparison has been made pair-to-pair of single structure respectively to the triple form. Finally the implementation aspects of a single chaotic cryptography using chaos in digital filter can stand efficiently as better performance speed with the small complexity algorithm, points out the resemblances to DES and triple DES with the similar security confirmation results without reaching to the triple form of the structure. Simulation has been conducted using Matlab simulation with the input of grayscale image.

We developed SNS Door Phone by making an interphone system an IoT device. We integrated SNS and QR-code recognition function with an interphone system. Thanks to connection with SNS, we can know the visit of the parcel delivery service anytime through SNS even if during going out. Thanks to introduction of QR-code recognition function, if a parcel deliveryman only showed the QR-code of the parcel in front of SNS Door Phone, the re-delivery operation information would be sent to a user automatically through SNS. Then, the user can call or ask re-delivery arrangement using smart phone without inputting any additional data. We can consider this kind of seamless re-delivery operation to be a good example of Robotic Process Automation.

Blockchain normalizes applications that run on the internet through the standardization of decentralized data structure, computational requirements and trust in transactions. This new standard has now spawned hundreds of legitimate internet applications in addition to the cryptocurrency revolution. This next frontier that standardizes internet applications will dramatically increase productivity to levels never seen before, especially when applied to Internet of Things (IoT) applications. The blockchain framework relies on cryptographic private keys to sign digital data as its foundational principle. Without the security of private keys to sign data blocks, there can be no trust in blockchain. Central storage of these keys for managing IoT machines and users, while convenient to implement, will be highly detrimental to the assumed safety and security of this next frontier. In this paper, we will introduce decentralized and device agnostic cryptographic signing solutions suitable for securing users and machines in blockchain and IoT applications.