Biblio

The RFID based communication between objects within the framework of IoT is potentially very efficient in terms of power requirements and system complexity. The new design incorporating the emerging chipless RFID tags has the potential to make the system more efficient and simple. However, these systems are prone to privacy and security risks and these challenges associated with such systems have not been addressed appropriately in the broader IoT framework. In this context, a lightweight collision free algorithm based on n-bit pseudo random number generator, X-OR hash function, and rotations for chipless RFID system is presented. The algorithm has been implemented on an 8-bit open-loop resonator based chipless RFID tag based system and is validated using BASYS 2 FPGA board based platform. The proposed scheme has been shown to possess security against various attacks such as Denial of Service (DoS), tag/reader anonymity, and tag impersonation.

The RFID technology is now widely used and combined with everyday life. RFID Tag is a wireless device used to identify individuals and objects, in fact, it is a combination of the chip and antenna that sends the necessary information to an RFID Reader. On the other hand, an RFID Reader converts received radio waves into digital information and then provides facilities such as sending data to the computer and processing them. Radio frequency identification is a comprehensive processing technology that has led to a revolution in industry and medicine as an alternative to commercial barcodes. RFID Tag is used to tracking commodities and personal assets in the chain stores and even the human body and medical science. However, security and privacy problems have not yet been solved satisfactorily. There are many technical and economic challenges in this direction. In this paper, some of the latest technical research on privacy and security problems has been investigated in radio-frequency identification and security bit method, and it has been shown that in order to achieve this level of individual security, multiple technologies of RFID security development should combine with each other. These solutions should be cheap, efficient, reliable, flexible and long-term.

In the Tor anonymity network, the distribution of topology information relies on the correct behavior of five out of the nine trusted directory authority servers. This centralization is concerning since a powerful adversary might compromise these servers and conceal information about honest nodes, leading to the full de-anonymization of all Tor users. Our work aims at distributing the work of these trusted authorities, such increasing resilience against attacks on core infrastructure components of the Tor network. In particular, we leverage several emerging technologies, such as blockchains, smart contracts, and trusted execution environments to design and prototype a system called SmarTor. This system replaces the directory authorities with a smart contract and a distributed network of untrusted entities responsible for bandwidth measurements. We prototyped SmarTor using Ethereum smart contracts and Intel SGX secure hardware. In our evaluation, we show that SmarTor produces significantly more reliable and precise measurements compared to the current measurement system. Overall, our solution improves the decentralization of the Tor network, reduces trust assumptions and increases resilience against powerful adversaries like law enforcement and intelligence services.

Searchable encryption (SE) supports privacy-preserving searches over encrypted data. Recent studies on SE have focused on improving efficiency of the schemes. However, it was shown that most of the previous SE schemes could reveal the client's queries even if they are encrypted, thereby leading to privacy violation. In order to solve the problem, several forward private SE schemes have been proposed in a single client environment. However, the previous forward private SE schemes have never been analyzed in multi-client settings. In this paper, we briefly review the previous forward private SE schemes. Then, we conduct a comparative analysis of them in terms of performance and forward privacy. Our analysis demonstrates the previous forward secure SE schemes highly depend on the file-counter. Lastly, we show that they are not scalable in multi-client settings due to the performance and security issue from the file-counter.

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.

Internet of Things (IoT) is a contemporary concept for connecting the existing things in our environment with the Internet for a sake of making the objects information are accessible from anywhere and anytime to support a modern life style based on the Internet. With the rapid development of the IoT technologies and widely spreading in most of the fields such as buildings, health, education, transportation and agriculture. Thus, the IoT applications require increasing data collection from the IoT devices to send these data to the applications or servers which collect or analyze the data, so it is a very important to secure the data and ensure that do not reach a malicious adversary. This paper reviews some attacks in the IoT applications and the security weaknesses in the IoT environment. In addition, this study presents the challenges of IoT in terms of hardware, network and software. Moreover, this paper summarizes and points to some attacks on the smart car, smart home, smart campus, smart farm and healthcare.

Location-based services, which provide services based on locations of consumers' interests, are becoming essential for our daily lives. Since the location of a consumer's interest contains private information, several studies propose location privacy protection mechanisms using an anonymizer, which sends queries specifying anonymous location sets, each of which contains k - 1 locations in addition to a location of a consumer's interest, to an LBS provider based on the k-anonymity principle. The anonymizer is, however, assumed to be trusted/honest, and hence it is a single point of failure in terms of privacy leakage. To address this privacy issue, this paper designs a semi-honest anonymizer to protect location privacy in NDN networks. This study first reveals that session anonymity and location anonymity must be achieved to protect location privacy with a semi-honest anonymizer. Session anonymity is to hide who specifies which anonymous location set and location anonymity is to hide a location of a consumer's interest in a crowd of locations. We next design an architecture to achieve session anonymity and an algorithm to generate anonymous location sets achieving location anonymity. Our evaluations show that the architecture incurs marginal overhead to achieve session anonymity and anonymous location sets generated by the algorithm sufficiently achieve location anonymity.

This paper proposes a content-centric privacy (CCP) model that enables a privacy-preserving monitoring services in surveillance systems without cloud dependency. We design a simple yet powerful method that could not be obtained from a cloud-like system. The CCP model includes two key ideas: (1) the separation of the private data (i.e., target object images) from the public data (i.e., background images), and (2) the service authentication with the classification model. Deploying the CCP model over ICN enables the privacy central around the content itself rather than relying on a cloud system. Our preliminary analysis shows that the ICN-based CCP model can preserve privacy with respect to the W3 -privacy in which the private information of target object are decoupled from the queries and cameras.

There is growing recognition that machine learning (ML) exposes new security and privacy vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited but expanding. In this talk, we explore the threat model space of ML algorithms through the lens of Saltzer and Schroeder's principles for the design of secure computer systems. This characterization of the threat space prompts an investigation of current and future research directions. We structure our discussion around three of these directions, which we believe are likely to lead to significant progress. The first seeks to design mechanisms for assembling reliable records of compromise that would help understand the degree to which vulnerabilities are exploited by adversaries, as well as favor psychological acceptability of machine learning applications. The second encompasses a spectrum of approaches to input verification and mediation, which is a prerequisite to enable fail-safe defaults in machine learning systems. The third pursues formal frameworks for security and privacy in machine learning, which we argue should strive to align machine learning goals such as generalization with security and privacy desirata like robustness or privacy. Key insights resulting from these three directions pursued both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. We conclude by systematizing best practices in our growing community.

Revealing private and sensitive information on Social Network Sites (SNSs) like Facebook is a common practice which sometimes results in unwanted incidents for the users. One approach for helping users to avoid regrettable scenarios is through awareness mechanisms which inform a priori about the potential privacy risks of a self-disclosure act. Privacy heuristics are instruments which describe recurrent regrettable scenarios and can support the generation of privacy awareness. One important component of a heuristic is the group of people who should not access specific private information under a certain privacy risk. However, specifying an exhaustive list of unwanted recipients for a given regrettable scenario can be a tedious task which necessarily demands the user's intervention. In this paper, we introduce an approach based on decision trees to instantiate the audience component of privacy heuristics with minor intervention from the users. We introduce Disclosure- Acceptance Trees, a data structure representative of the audience component of a heuristic and describe a method for their generation out of user-centred privacy preferences.

The success and widespread adoption of the Internet of Things (IoT) has increased many folds over the last few years. Industries, technologists and home users recognise the importance of IoT in their lives. Essentially, IoT has brought vast industrial revolution and has helped automate many processes within organisations and homes. However, the rapid growth of IoT is also a cause for significant concern. IoT is not only plagued with security, authentication and access control issues, it also doesn't work as well as it should with fourth industrial revolution, commonly known as Industry 4.0. The absence of effective regulation, standards and weak governance has led to a continual downward trend in the security of IoT networks and devices, as well as given rise to a broad range of privacy issues. This paper examines the IoT industry and discusses the urgent need for standardisation, the benefits of governance as well as the issues affecting the IoT sector due to the absence of regulation. Additionally, through this paper, we are introducing an IoT security framework (IoTSFW) for organisations to bridge the current lack of guidelines in the IoT industry. Implementation of the guidelines, defined in the proposed framework, will assist organisations in achieving security, privacy, sustainability and scalability within their IoT networks.

Mobile interfaces will be central in connecting end-users to the smart grid and enabling their active participation. Services and features supporting this participation do, however, rely on high-frequency collection and transmission of energy usage data by smart meters which is privacy-sensitive. The successful communication of privacy to end-users via consumer interfaces will therefore be crucial to ensure smart meter acceptance and consequently enable participation. Current understanding of user privacy concerns in this context is not very differentiated, and user privacy requirements have received little attention. A preliminary user questionnaire study was conducted to gain a more detailed understanding of the differing perceptions of various privacy risks and the relative importance of different privacy-ensuring measures. The results underline the significance of open communication, restraint in data collection and usage, user control, transparency, communication of security measures, and a good customer relationship.

The design of optimal energy management strategies that trade-off consumers' privacy and expected energy cost by using an energy storage is studied. The Kullback-Leibler divergence rate is used to assess the privacy risk of the unauthorized testing on consumers' behavior. We further show how this design problem can be formulated as a belief state Markov decision process problem so that standard tools of the Markov decision process framework can be utilized, and the optimal solution can be obtained by using Bellman dynamic programming. Finally, we illustrate the privacy-enhancement and cost-saving by numerical examples.

In Smart Grids (SGs), data aggregation process is essential in terms of limiting packet size, data transmission amount and data storage requirements. This paper presents a novel Domingo-Ferrer additive privacy based Secure Data Aggregation (SDA) scheme for Fog Computing based SGs (FCSG). The proposed protocol achieves end-to-end confidentiality while ensuring low communication and storage overhead. Data aggregation is performed at fog layer to reduce the amount of data to be processed and stored at cloud servers. As a result, the proposed protocol achieves better response time and less computational overhead compared to existing solutions. Moreover, due to hierarchical architecture of FCSG and additive homomorphic encryption consumer privacy is protected from third parties. Theoretical analysis evaluates the effects of packet size and number of packets on transmission overhead and the amount of data stored in cloud server. In parallel with the theoretical analysis, our performance evaluation results show that there is a significant improvement in terms of data transmission and storage efficiency. Moreover, security analysis proves that the proposed scheme successfully ensures the privacy of collected data.

An advanced metering infrastructure (AMI) allows real-time fine-grained monitoring of the energy consumption data of individual consumers. Collected metering data can be used for a multitude of applications. For example, energy demand forecasting, based on the reported fine-grained consumption, can help manage the near future energy production. However, fine- grained metering data reporting can lead to privacy concerns. It is, therefore, imperative that the utility company receives the fine-grained data needed to perform the intended demand response service, without learning any sensitive information about individual consumers. In this paper, we propose an anonymous privacy preserving fine-grained data aggregation scheme for AMI networks. In this scheme, the utility company receives only the distribution of the energy consumption by the consumers at different time slots. We leverage a network tree topology structure in which each smart meter randomly reports its energy consumption data to its parent smart meter (according to the tree). The parent node updates the consumption distribution and forwards the data to the utility company. Our analysis results show that the proposed scheme can preserve the privacy and security of individual consumers while guaranteeing the demand response service.

Smart meters migrate conventional electricity grid into digitally enabled Smart Grid (SG), which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users' demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those “espionage meters". To enjoy the benefits of smart meter measured data without compromising the users' privacy, in this paper, we try to integrate distributed differential privacy (DDP) techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users' energy profiles. Briefly, we add differential private noises to the users' energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users' demand distribution, the utility provider aggregates a given set of historical users' differentially private data, estimates the users' demands, and formulates the data- driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company's real data analysis.

Malware detection is an indispensable factor in security of internet oriented machines. The combinations of different features are used for dynamic malware analysis. The different combinations are generated from APIs, Summary Information, DLLs and Registry Keys Changed. Cuckoo sandbox is used for dynamic malware analysis, which is customizable, and provide good accuracy. More than 2300 features are extracted from dynamic analysis of malware and 92 features are extracted statically from binary malware using PEFILE. Static features are extracted from 39000 malicious binaries and 10000 benign files. Dynamically 800 benign files and 2200 malware files are analyzed in Cuckoo Sandbox and 2300 features are extracted. The accuracy of dynamic malware analysis is 94.64% while static analysis accuracy is 99.36%. The dynamic malware analysis is not effective due to tricky and intelligent behaviours of malwares. The dynamic analysis has some limitations due to controlled network behavior and it cannot be analyzed completely due to limited access of network.

Motivation: The security of any system is a direct consequence of stakeholders' decisions regarding security requirements. Such decisions are taken with varying degrees of expertise, and little is currently understood about how various demographics - security experts, general computer scientists, managers - approach security decisions and the strategies that underpin those decisions. What are the typical decision patterns, the consequences of such patterns and their impact on the security of the system in question? Nor is there any substantial understanding of how the strategies and decision patterns of these different groups contrast. Is security expertise necessarily an advantage when making security decisions in a given context? Answers to these questions are key to understanding the "how" and "why" behind security decision processes. The Game: In this talk1, we present a tabletop game: Decisions and Disruptions (D-D)2 that tasks a group of players with managing the security of a small utility company while facing a variety of threats. The game is kept short - 2 hours - and simple enough to be played without prior training. A cyber-physical infrastructure, depicted through a Lego\textregistered board, makes the game easy to understand and accessible to players from varying backgrounds and security expertise, without being too trivial a setting for security experts. Key insights: We played D-D with 43 players divided into homogeneous groups: 4 groups of security experts, 4 groups of nontechnical managers and 4 groups of general computer scientists. • Strategies: Security experts had a strong interest in advanced technological solutions and tended to neglect intelligence gathering, to their own detriment. Managers, too, were technology-driven and focused on data protection while neglecting human factors more than other groups. Computer scientists tended to balance human factors and intelligence gathering with technical solutions, and achieved the best results of the three demographics. • Decision Processes: Technical experience significantly changes the way players think. Teams with little technical experience had shallow, intuition-driven discussions with few concrete arguments. Technical teams, and the most experienced in particular, had much richer debates, driven by concrete scenarios, anecdotes from experience, and procedural thinking. Security experts showed a high confidence in their decisions - despite some of them having bad consequences - while the other groups tended to doubt their own skills - even when they were playing good games. • Patterns: A number of characteristic plays were identified, some good (balance between priorities, open-mindedness, and adapting strategies based on inputs that challenge one's pre-conceptions), some bad (excessive focus on particular issues, confidence in charismatic leaders), some ugly ("tunnel vision" syndrome by over-confident players). These patterns are documented in the full paper - showing the virtue of the positive ones, discouraging the negative ones, and inviting the readers to do their own introspection. Conclusion: Beyond the analysis of the security decisions of the three demographics, there is a definite educational and awareness-raising aspect to D-D (as noted consistently by players in all our subject groups). Game boxes will be brought to the conference for demonstration purposes, and the audience will be invited to experiment with D-D themselves, make their own decisions, and reflect on their own perception of security.

The central building block of secure and privacy-preserving Vehicular Communication (VC) systems is a Vehicular Public-Key Infrastructure (VPKI), which provides vehicles with multiple anonymized credentials, termed pseudonyms. These pseudonyms are used to ensure message authenticity and integrity while preserving vehicle (and thus passenger) privacy. In the light of emerging large-scale multi-domain VC environments, the efficiency of the VPKI and, more broadly, its scalability are paramount. In this extended abstract, we leverage the state-of-the-art VPKI system and enhance its functionality towards a highly-available and dynamically-scalable design; this ensures that the system remains operational in the presence of benign failures or any resource depletion attack, and that it dynamically scales out, or possibly scales in, according to the requests' arrival rate. Our full-blown implementation on the Google Cloud Platform shows that deploying a VPKI for a large-scale scenario can be cost-effective, while efficiently issuing pseudonyms for the requesters.

Early detection of new kinds of malware always plays an important role in defending the network systems. Especially, if intelligent protection systems could themselves detect an existence of new malware types in their system, even with a very small number of malware samples, it must be a huge benefit for the organization as well as the social since it help preventing the spreading of that kind of malware. To deal with learning from few samples, term ``one-shot learning'' or ``fewshot learning'' was introduced, and mostly used in computer vision to recognize images, handwriting, etc. An approach introduced in this paper takes advantage of One-shot learning algorithms in solving the malware classification problem by using Memory Augmented Neural Network in combination with malware's API calls sequence, which is a very valuable source of information for identifying malware behavior. In addition, it also use some advantages of the development in Natural Language Processing field such as word2vec, etc. to convert those API sequences to numeric vectors before feeding to the one-shot learning network. The results confirm very good accuracies compared to the other traditional methods.

The increasing amount of malware variants seen in the wild is causing problems for Antivirus Software vendors, unable to keep up by creating signatures for each. The methods used to develop a signature, static and dynamic analysis, have various limitations. Machine learning has been used by Antivirus vendors to detect malware based on the information gathered from the analysis process. However, adversarial examples can cause machine learning algorithms to miss-classify new data. In this paper we describe a method for malware analysis by converting malware binaries to images and then preparing those images for training within a Generative Adversarial Network. These unsupervised deep neural networks are not susceptible to adversarial examples. The conversion to images from malware binaries should be faster than using dynamic analysis and it would still be possible to link malware families together. Using the Generative Adversarial Network, malware detection could be much more effective and reliable.

With the increase in the popularity of computerized online applications, the analysis, and detection of a growing number of newly discovered stealthy malware poses a significant challenge to the security community. Signature-based and behavior-based detection techniques are becoming inefficient in detecting new unknown malware. Machine learning solutions are employed to counter such intelligent malware and allow performing more comprehensive malware detection. This capability leads to an automatic analysis of malware behavior. The proposed oblique random forest ensemble learning technique is efficient for malware classification. The effectiveness of the proposed method is demonstrated with three malware classification datasets from various sources. The results are compared with other variants of decision tree learning models. The proposed system performs better than the existing system in terms of classification accuracy and false positive rate.

Lately, we are facing the Malware crisis due to various types of malware or malicious programs or scripts available in the huge virtual world - the Internet. But, what is malware? Malware can be a malicious software or a program or a script which can be harmful to the user's computer. These malicious programs can perform a variety of functions, including stealing, encrypting or deleting sensitive data, altering or hijacking core computing functions and monitoring users' computer activity without their permission. There are various entry points for these programs and scripts in the user environment, but only one way to remove them is to find them and kick them out of the system which isn't an easy job as these small piece of script or code can be anywhere in the user system. This paper involves the understanding of different types of malware and how we will use Machine Learning to detect these malwares.

In view of the great threat posed by malware and the rapid growing trend about malware variants, it is necessary to determine the category of new samples accurately for further analysis and taking appropriate countermeasures. The network behavior based classification methods have become more popular now. However, the behavior profiling models they used usually only depict partial network behavior of samples or require specific traffic selection in advance, which may lead to adverse effects on categorizing advanced malware with complex activities. In this paper, to overcome the shortages of traditional models, we raise a comprehensive behavior model for profiling the behavior of malware network activities. And we also propose a corresponding malware classification method which can extract and compare the major behavior of samples. The experimental and comparison results not only demonstrate our method can categorize samples accurately in both criteria, but also prove the advantage of our profiling model to two other approaches in accuracy performance, especially under scenario based criteria.

Malware or Malicious Software, are an important threat to information technology society. Deep Neural Network has been recently achieving a great performance for the tasks of malware detection and classification. In this paper, we propose a convolutional gated recurrent neural network model that is capable of classifying malware to their respective families. The model is applied to a set of malware divided into 9 different families and that have been proposed during the Microsoft Malware Classification Challenge in 2015. The model shows an accuracy of 92.6% on the available dataset.