Biblio

While the potential advantages of geographic forwarding in wireless sensor networks (WSN) have been demonstrated for a while now, research in applying Information Centric Networking (ICN) has only gained momentum in the last few years. In this paper, we bridge these two worlds by proposing an ICN-compliant and secure implementation of geographic forwarding for ICN. We implement as a proof of concept the Greedy Perimeter Stateless Routing (GPSR) algorithm and compare its performance to that of vanilla ICN forwarding. We also evaluate the cost of security in 802.15.4 networks in terms of energy, memory and CPU footprint. We show that in sparse but large networks, GPSR outperforms vanilla ICN forwarding in both memory footprint and CPU consumption. However, GPSR is more energy intensive because of the cost of communications.

Maintaining a clean and hygienic civic environment is an indispensable yet formidable task, especially in developing countries. With the aim of engaging citizens to track and report on their neighborhoods, this paper presents a novel smartphone app, called SpotGarbage, which detects and coarsely segments garbage regions in a user-clicked geo-tagged image. The app utilizes the proposed deep architecture of fully convolutional networks for detecting garbage in images. The model has been trained on a newly introduced Garbage In Images (GINI) dataset, achieving a mean accuracy of 87.69%. The paper also proposes optimizations in the network architecture resulting in a reduction of 87.9% in memory usage and 96.8% in prediction time with no loss in accuracy, facilitating its usage in resource constrained smartphones.

Technological changes bring great efficiencies and opportunities; however, they also bring new threats and dangers that users are often ill prepared to handle. Some individuals have training at work or school while others have family or friends to help them. However, there are few widely known or ubiquitous educational programs to inform and motivate users to develop safe cybersecurity practices. Additionally, little is known about learning strategies in this domain. Understanding how active Internet users have learned their security practices can give insight into more effective learning methods. I surveyed 800 online labor workers to discover their learning processes. They shared how they had to construct their own schema and negotiate meaning in a complex domain. Findings suggest a need to help users build a dynamic mental model of security. Participants recommend encouraging participatory and constructive learning, multi-model dissemination, and ubiquitous opportunities for learning security behaviors.

With the increasingly pervasive role of software in society, security is becoming an important quality concern, emphasizing security by design, but it requires intensive specialization. Security in families of systems is even harder, as diverse variants of security solutions must be considered, with even different security goals per product. Furthermore, security is not a static object but a moving target, adding variability. For this, an approach to systematically address security concerns in software product lines is needed. It should consider security separate from other variability dimensions. The main challenges to realize this are: (i) expressing security and its variability, (ii) selecting the right solution, (iii) properly instantiating a solution, and (iv) verifying and validating it. In this paper, we present our research agenda towards addressing the aforementioned challenges.

This panel will discuss and debate what role(s) the information technology discipline should have in cybersecurity. Diverse viewpoints will be considered including current and potential ACM curricular recommendations, current and potential ABET and NSA accreditation criteria, the emerging cybersecurity discipline(s), consideration of government frameworks, the need for a multi-disciplinary approach to cybersecurity, and what aspects of cybersecurity should be under information technology's purview.

Contemporary cloud environments are built on low-assurance components, so they cannot provide a high level of assurance about the isolation and protection of information. A ``multi-level'' secure cloud environment thus typically consists of multiple, isolated clouds, each of which handles data of only one security level. Not only are such environments duplicative and costly, data ``sharing'' must be implemented by massive, wasteful copying of data from low-level domains to high-level domains. The requirements for certifiable, scalable, multi-level cloud security are threefold: 1) To have trusted, high-assurance components available for use in creating a multi-level secure cloud environment; 2) To design a cloud architecture that efficiently uses the high-assurance components in a scalable way, and 3) To compose the secure components within the scalable architecture while still verifiably maintaining the system security properties. This paper introduces a trusted, high-assurance file server and architecture that satisfies all three requirements. The file server is built on mature technology that was previously certified and deployed across domains from TS/SCI to Unclassified and that supports high-performance, low-to-high and high-to-low file sharing with verifiable security.

Zero-day attack is a critical network attack. The zero-day attack period (ZDAP) is the period from the release of malware/exploit until a patch becomes available. IDS/IPS cannot effectively block zero-day attacks because they use pattern-based signatures in general. This paper proposes a Prophetic Defender (PD) by which ZDAP can be minimized. Prior to actual attack, hackers scan networks to identify hosts with vulnerable ports. If this port scanning can be detected early, zero-day attacks will become detectable. PD architecture makes use of a honeypot-based pseudo server deployed to detect malicious port scans. A port-scanning honeypot was operated by us in 6 years from 2009 to 2015. By analyzing the 6-year port-scanning log data, we understand that PD is effective for detecting and blocking zero-day attacks. The block rate of the proposed architecture is 98.5%.

Syntax extension mechanisms are powerful, but reasoning about syntax extensions can be difficult. Recent work on type-specific languages (TSLs) addressed reasoning about composition, hygiene and typing for extensions introducing new literal forms. We supplement TSLs with typed syntax macros (TSMs), which, unlike TSLs, are explicitly invoked to give meaning to delimited segments of arbitrary syntax. To maintain a typing discipline, we describe two avors of term-level TSMs: synthetic TSMs specify the type of term that they generate, while analytic TSMs can generate terms of arbitrary type, but can only be used in positions where the type is otherwise known. At the level of types, we describe a third avor of TSM that generates a type of a specified kind along with its TSL and show interesting use cases where the two mechanisms operate in concert.

The optimal design of a fault-tolerant quantum computer involves finding an appropriate balance between the burden of large-scale integration of noisy components and the load of improving the reliability of hardware technology. This balance can be evaluated by quantitatively modeling the execution of quantum logic operations on a realistic quantum hardware containing limited computational resources. In this work, we report a complete performance simulation software tool capable of (1) searching the hardware design space by varying resource architecture and technology parameters, (2) synthesizing and scheduling a fault-tolerant quantum algorithm within the hardware constraints, (3) quantifying the performance metrics such as the execution time and the failure probability of the algorithm, and (4) analyzing the breakdown of these metrics to highlight the performance bottlenecks and visualizing resource utilization to evaluate the adequacy of the chosen design. Using this tool, we investigate a vast design space for implementing key building blocks of Shor’s algorithm to factor a 1,024-bit number with a baseline budget of 1.5 million qubits. We show that a trapped-ion quantum computer designed with twice as many qubits and one-tenth of the baseline infidelity of the communication channel can factor a 2,048-bit integer in less than 5 months.

A3 is an execution management environment that aims to make network-facing applications and services resilient against zero-day attacks. A3 recently underwent two adversarial evaluations of its defensive capabilities. In one, A3 defended an App Store used in a Capture the Flag (CTF) tournament, and in the other, a tactically relevant network service in a red team exercise. This paper describes the A3 defensive technologies evaluated, the evaluation results, and the broader lessons learned about evaluations for technologies that seek to protect critical systems from zero-day attacks.

Poisoning attack in which an adversary misleads the learning process by manipulating its training set significantly affect the performance of classifiers in security applications. This paper proposed a robust learning method which reduces the influences of attack samples on learning. The sensitivity, defined as the fluctuation of the output with small perturbation of the input, in Localized Generalization Error Model (L-GEM) is measured for each training sample. The classifier's output on attack samples may be sensitive and inaccurate since these samples are different from other untainted samples. An import score is assigned to each sample according to its localized generalization error bound. The classifier is trained using a new training set obtained by resampling the samples according to their importance scores. RBFNN is applied as the classifier in experimental evaluation. The proposed model outperforms than the traditional one under the well-known label flip poisoning attacks including nearest-first and farthest-first flips attack.

The smart grid changes the way energy is produced and distributed. In addition both, energy and information is exchanged bidirectionally among participating parties. Therefore heterogeneous systems have to cooperate effectively in order to achieve a common high-level use case, such as smart metering for billing or demand response for load curtailment. Furthermore, a substantial amount of personal data is often needed for achieving that goal. Capturing and processing personal data in the smart grid increases customer concerns about privacy and in addition, certain statutory and operational requirements regarding privacy aware data processing and storage have to be met. An increase of privacy constraints, however, often limits the operational capabilities of the system. In this paper, we present an approach that automates the process of finding an optimal balance between privacy requirements and operational requirements in a smart grid use case and application scenario. This is achieved by formally describing use cases in an abstract model and by finding an algorithm that determines the optimum balance by forward mapping privacy and operational impacts. For this optimal balancing algorithm both, a numeric approximation and - if feasible - an analytic assessment are presented and investigated. The system is evaluated by applying the tool to a real-world use case from the University of Southern California (USC) microgrid.

This article proposes Probabilistic Replacement Policy (PRP), a novel replacement policy that evicts the line with minimum estimated hit probability under optimal replacement instead of the line with maximum expected reuse distance. The latter is optimal under the independent reference model of programs, which does not hold for last-level caches (LLC). PRP requires 7% and 2% metadata overheads in the cache and DRAM respectively. Using a sampling scheme makes DRAM overhead negligible, with minimal performance impact. Including detailed overhead modeling and equal cache areas, PRP outperforms SHiP, a state-of-the-art LLC replacement algorithm, by 4% for memory-intensive SPEC-CPU2006 benchmarks.

Ever-growing performance of supercomputers nowadays brings demanding requirements of energy efficiency and resilience, due to rapidly expanding size and duration in use of the large-scale computing systems. Many application/architecture-dependent parameters that determine energy efficiency and resilience individually have causal effects with each other, which directly affect the trade-offs among performance, energy efficiency and resilience at scale. To enable high-efficiency management for large-scale High-Performance Computing (HPC) systems nowadays, quantitatively understanding the entangled effects among performance, energy efficiency, and resilience is thus required. While previous work focuses on exploring energy-saving and resilience-enhancing opportunities separately, little has been done to theoretically and empirically investigate the interplay between energy efficiency and resilience at scale. In this article, by extending the Amdahl’s Law and the Karp-Flatt Metric, taking resilience into consideration, we quantitatively model the integrated energy efficiency in terms of performance per Watt and showcase the trade-offs among typical HPC parameters, such as number of cores, frequency/voltage, and failure rates. Experimental results for a wide spectrum of HPC benchmarks on two HPC systems show that the proposed models are accurate in extrapolating resilience-aware performance and energy efficiency, and capable of capturing the interplay among various energy-saving and resilience factors. Moreover, the models can help find the optimal HPC configuration for the highest integrated energy efficiency, in the presence of failures and applied resilience techniques.

Machine learning is being used in a wide range of application domains to discover patterns in large datasets. Increasingly, the results of machine learning drive critical decisions in applications related to healthcare and biomedicine. Such health-related applications are often sensitive, and thus, any security breach would be catastrophic. Naturally, the integrity of the results computed by machine learning is of great importance. Recent research has shown that some machine-learning algorithms can be compromised by augmenting their training datasets with malicious data, leading to a new class of attacks called poisoning attacks. Hindrance of a diagnosis may have life-threatening consequences and could cause distrust. On the other hand, not only may a false diagnosis prompt users to distrust the machine-learning algorithm and even abandon the entire system but also such a false positive classification may cause patient distress. In this paper, we present a systematic, algorithm-independent approach for mounting poisoning attacks across a wide range of machine-learning algorithms and healthcare datasets. The proposed attack procedure generates input data, which, when added to the training set, can either cause the results of machine learning to have targeted errors (e.g., increase the likelihood of classification into a specific class), or simply introduce arbitrary errors (incorrect classification). These attacks may be applied to both fixed and evolving datasets. They can be applied even when only statistics of the training dataset are available or, in some cases, even without access to the training dataset, although at a lower efficacy. We establish the effectiveness of the proposed attacks using a suite of six machine-learning algorithms and five healthcare datasets. Finally, we present countermeasures against the proposed generic attacks that are based on tracking and detecting deviations in various accuracy metrics, and benchmark their effectiveness.

This paper examines the problem of locating outlier columns in a large, otherwise low-rank, matrix. We propose a simple two-step adaptive sensing and inference approach and establish theoretical guarantees for its performance. Our results show that accurate outlier identification is achievable using very few linear summaries of the original data matrix - as few as the squared rank of the low-rank component plus the number of outliers, times constant and logarithmic factors. We demonstrate the performance of our approach experimentally in two stylized applications, one motivated by robust collaborative filtering tasks, and the other by saliency map estimation tasks arising in computer vision and automated surveillance.

Privacy analysis is essential in the society. Data privacy preservation for access control, guaranteed service in wireless sensor networks are important parts. In programs' verification, we not only consider about these kinds of safety and liveness properties but some security policies like noninterference, and observational determinism which have been proposed as hyper properties. Fairness is widely applied in verification for concurrent systems, wireless sensor networks and embedded systems. This paper studies verification and analysis for proving security-relevant properties and hyper properties by proposing deductive proof rules under fairness requirements (constraints).

Cloud Computing is one of the large and essential environment now a days to work for the storage collection and privacy preserve to that data. Cloud data security is most important and major concern for the client while use of the cloud services provided by the different service providers. There can be some major security concern and conflicts between the client and the service provider. To get out from those issues, a third party auditor uses as an auditor for assurance of data in the environment. Storage systems for the cloud has many fundamental challenges still today. All basic as well critical challenges among which storage space and security is generally the top concern in the cloud environment. To give the appropriate security issues we have proposed third party authentication system. The cloud not only for the simplified data storage but also secure data acquisition in cloud environment. At last we have perform different security analysis as well performance analysis. It give the results that proposed scheme has significant increases in efficiency for maintaining highly secure data storage and acquisition. The proposed method also helps to minimize the cost in environment and also increases communication efficiency in the cloud environment.

Based on the analysis relationships of challenger and attestation in remote attestation process, we propose a dynamic remote attestation model based on concerns. By combines the trusted root and application of dynamic credible monitoring module, Convert the Measurement for all load module of integrity measurement architecture into the Attestation of the basic computing environments, dynamic credible monitoring module, and request service software module. Discuss the rationality of the model. The model used Merkel hash tree to storage applications software integrity metrics, both to protect the privacy of the other party application software, and also improves the efficiency of remote attestation. Experimental prototype system shows that the model can verify the dynamic behavior of the software, to make up for the lack of static measure.

Language vector space models (VSMs) have recently proven to be effective across a variety of tasks. In VSMs, each word in a corpus is represented as a real-valued vector. These vectors can be used as features in many applications in machine learning and natural language processing. In this paper, we study the effect of vector space representations in cyber security. In particular, we consider a passive traffic analysis attack (Website Fingerprinting) that threatens users' navigation privacy on the web. By using anonymous communication, Internet users (such as online activists) may wish to hide the destination of web pages they access for different reasons such as avoiding tyrant governments. Traditional website fingerprinting studies collect packets from the users' network and extract features that are used by machine learning techniques to reveal the destination of certain web pages. In this work, we propose the packet to vector (P2V) approach where we model website fingerprinting attack using word vector representations. We show how the suggested model outperforms previous website fingerprinting works.

The growth of ICTs and the resulting data explosion could pave the way for the surveillance of our lives and diminish our democratic freedoms, at an unimaginable scale. Consumer mistrust of an organization's ability to safeguard their data is at an all time high and this has negative implications for Big Data. The timing is right to be proactive about designing privacy into technologies, business processes and networked infrastructures. Inclusiveness of all objectives can be achieved through consultation, co-operation, and collaboration (3 C's). If privacy is the default, without diminishing functionality or other legitimate interests, then trust will be preserved and innovation will flourish.

Strength of security and privacy of any cryptographic mechanisms that use random numbers require that the random numbers generated have two important properties namely 1. Uniform distribution and 2. Independence. With the growth of Internet many devices are connected to Internet that host sensors. One idea proposed is to use sensor data as seed for Random Number Generator (RNG) since sensors measure the physical phenomena that exhibit randomness over time. The random numbers generated from sensor data can be used for cryptographic algorithms in Internet activities. These sensor data also pose weaknesses where sensors may be under adversarial control that may lead to generating expected random sequence which breaks the security and privacy. This paper proposes a wash-rinse-spin approach to process the raw sensor data that increases randomness in the seed value. The generated sequences from two sensors are combined by Decimation method to improve unpredictability. This makes the sensor data to be more secure in generating random numbers preventing attackers from knowing the random sequence through adversarial control.

The new era of information communication and technology (ICT), everyone wants to store/share their Data or information in online media, like in cloud database, mobile database, grid database, drives etc. When the data is stored in online media the main problem is arises related to data is privacy because different types of hacker, attacker or crackers wants to disclose their private information as publically. Security is a continuous process of protecting the data or information from attacks. For securing that information from those kinds of unauthorized people we proposed and implement of one the technique based on the data modification concept with taking the iris database on weka tool. And this paper provides the high privacy in distributed clustered database environments.

Searchable encryption is a new developing information security technique and it enables users to search over encrypted data through keywords without having to decrypt it at first. In the last decade, many researchers are engaging in the field of searchable encryption and have proposed a series of efficient search schemes over encrypted cloud data. It is the time to survey this field to conclude a comprehensive framework by analyzing individual contributions. This paper focuses on the searchable encryption schemes in cloud. We firstly summarize the general model and threat model in searchable encryption schemes, and then present the privacy-preserving issues in these schemes. In addition, we compare the efficiency and security between semantic search and preferred search in detail. At last, some open issues and research challenges in the future are proposed.

Steganography is the art of the hidden data in such a way that it detection of hidden knowledge prevents. As the necessity of security and privacy increases, the need of the hiding secret data is ongoing. In this paper proposed an enhanced detection of the 1-2-4 LSB steganography and RSA cryptography in Gray Scale and Color images. For color images, we apply 1-2-4 LSB on component of the RGB, then encrypt information applying RSA technique. For Gray Images, we use LSB to then encrypt information and also detect edges of gray image. In the experimental outcomes, calculate PSNR and MSE. We calculate peak signal noise ratio for quality and brightness. This method makes sure that the information has been encrypted before hiding it into an input image. If in any case the cipher text got revealed from the input image, the middle person other than receiver can't access the information as it is in encrypted form.