Biblio

Due to its low cost and availability, magnetic sensors nowadays are often incorporated into security systems to detect or localize threats. This paper, with the help of a correlated pre-published work, describes preliminary steps to ensure reliable results that could help in reducing inaccuracies/ errors in case of considering a security system that detects Magnetic IEDs employing AMR-based magnetic field sensors.

As the worldwide demand for cybersecurity-trained professionals continues to grow, the need to understand and define what cybersecurity education really means at the college or university level. Given the relative infancy of these efforts to define undergraduate cybersecurity programs, the panelists will present different perspectives on how such programs can be structured. They will then engage with the audience to explore additional viewpoints on cybersecurity, and work toward a shared understanding of undergraduate cybersecurity programs.

NASA's next-generation space communications network will involve dynamic and autonomous services analogous to services provided by current terrestrial wireless networks. This architecture concept, known as the Space Mobile Network (SMN), is enabled by several technologies now in development. A pillar of the SMN architecture is the establishment and utilization of a continuous bidirectional control plane space link channel and a new User Initiated Service (UIS) protocol to enable more dynamic and autonomous mission operations concepts, reduced user space communications planning burden, and more efficient and effective provider network resource utilization. This paper provides preliminary results from the application of model-driven architecture methodology to develop UIS. Such an approach is necessary to ensure systematic investigation of several open questions concerning the efficiency, robustness, interoperability, scalability and security of the control plane space link and UIS protocol.

Detecting software security vulnerabilities and distinguishing vulnerable from non-vulnerable code is anything but simple. Most of the time, vulnerabilities remain undisclosed until they are exposed, for instance, by an attack during the software operational phase. Software metrics are widely-used indicators of software quality, but the question is whether they can be used to distinguish vulnerable software units from the non-vulnerable ones during development. In this paper, we perform an exploratory study on software metrics, their interdependency, and their relation with security vulnerabilities. We aim at understanding: i) the correlation between software architectural characteristics, represented in the form of software metrics, and the number of vulnerabilities; and ii) which are the most informative and discriminative metrics that allow identifying vulnerable units of code. To achieve these goals, we use, respectively, correlation coefficients and heuristic search techniques. Our analysis is carried out on a dataset that includes software metrics and reported security vulnerabilities, exposed by security attacks, for all functions, classes, and files of five widely used projects. Results show: i) a strong correlation between several project-level metrics and the number of vulnerabilities, ii) the possibility of using a group of metrics, at both file and function levels, to distinguish vulnerable and non-vulnerable code with a high level of accuracy.

We present an adaptive multi-rate ray sampling algorithm targeting mobile ray-tracing GPUs. We efficiently combine two existing algorithms, adaptive supersampling and undersampling, into a single framework targeting ray-tracing GPUs and extend it to a new multi-rate sampling scheme by utilizing tile-based rendering and frame-to-frame coherency. The experimental results show that our implementation is a versatile solution for future ray-tracing GPUs as it provides up to 2.98 times better efficiency in terms of performance per Watt by reducing the number of rays to be fed into the dedicated hardware and minimizing the memory operations.

Minimizing the grid impacts of large-scale plug-in electric vehicle (PEV) charging tends to be associated with coordination strategies that seek to fill the overnight valley in electricity demand. However such strategies can result in high charging power, raising the possibility of local overloads within the distribution grid and of accelerated battery degradation. The paper establishes a framework for PEV charging coordination that facilitates the tradeoff between total generation cost and the local costs associated with overloading and battery degradation. A decentralized approach to solving the resulting large-scale optimization problem involves each PEV minimizing their charging cost with respect to a forecast price profile while taking into account local grid and battery effects. The charging strategies proposed by participating PEVs are used to update the price profile which is subsequently rebroadcast to the PEVs. The process then repeats. It is shown that under mild conditions this iterative process converges to the unique, efficient (socially optimal) coordination strategy.

This work proposes a generic focused crawling framework for discovering resources on any given topic that reside on the Surface or the Dark Web. The proposed crawler is able to seamlessly traverse the Surface Web and several darknets present in the Dark Web (i.e. Tor, I2P and Freenet) during a single crawl by automatically adapting its crawling behavior and its classifier-guided hyperlink selection strategy based on the network type. This hybrid focused crawler is demonstrated for the discovery of Web resources containing recipes for producing homemade explosives. The evaluation experiments indicate the effectiveness of the proposed ap-proach both for the Surface and the Dark Web.

Return-oriented programming (ROP) has been crucial for attackers to evade the security mechanisms of operating systems. It is currently used in malicious documents that exploit viewer applications and cause malware infection. For inspecting a large number of commonly handled documents, high-performance and flexible-detection methods are required. However, current solutions are either time-consuming or less precise. In this paper, we propose a novel method for statically detecting ROP chains in malicious documents. Our method generates a hidden Markov model (HMM) of ROP chains as well as one of benign documents by learning known malicious and benign documents and libraries used for ROP gadgets. Detection is performed by calculating the likelihood ratio between malicious and benign HMMs. In addition, we reduce the number of false positives by ROP chain integrity checking, which confirms whether ROP gadgets link properly if they are executed. Experimental results showed that our method can detect ROP-based malicious documents with no false negatives and few false positives at high throughput.

Today, outsourcing query processing tasks to remote cloud servers becomes a viable option; such outsourcing calls for encrypting data stored at the server so as to render it secure against eavesdropping adversaries and/or an honest-but-curious server itself. At the same time, to be efficiently managed, outsourced data should be indexed, and even adaptively so, as a side-effect of query processing. Computationally heavy encryption schemes render such outsourcing unattractive; an alternative, Order-Preserving Encryption Scheme (OPES), intentionally preserves and reveals the order in the data, hence is unattractive from the security viewpoint. In this paper, we propose and analyze a scheme for lightweight and indexable encryption, based on linear-algebra operations. Our scheme provides higher security than OPES and allows for range and point queries to be efficiently evaluated over encrypted numeric data, with decryption performed at the client side. We implement a prototype that performs incremental, query-triggered adaptive indexing over encrypted numeric data based on this scheme, without leaking order information in advance, and without prohibitive overhead, as our extensive experimental study demonstrates.

Intrusion detection systems need to be both accurate and fast. Speed is important especially when operating at the network level. Additionally, many intrusion detection systems rely on signature based detection approaches. However, machine learning can also be helpful for intrusion detection. One key challenge when using machine learning, aside from the detection accuracy, is using machine learning algorithms that are fast. In this paper, several processing architectures are considered for use in machine learning based intrusion detection systems. These architectures include standard CPUs, GPUs, and cognitive processors. Results of their processing speeds are compared and discussed.

Millions of users worldwide resort to mobile VPN clients to either circumvent censorship or to access geo-blocked content, and more generally for privacy and security purposes. In practice, however, users have little if any guarantees about the corresponding security and privacy settings, and perhaps no practical knowledge about the entities accessing their mobile traffic. In this paper we provide a first comprehensive analysis of 283 Android apps that use the Android VPN permission, which we extracted from a corpus of more than 1.4 million apps on the Google Play store. We perform a number of passive and active measurements designed to investigate a wide range of security and privacy features and to study the behavior of each VPN-based app. Our analysis includes investigation of possible malware presence, third-party library embedding, and traffic manipulation, as well as gauging user perception of the security and privacy of such apps. Our experiments reveal several instances of VPN apps that expose users to serious privacy and security vulnerabilities, such as use of insecure VPN tunneling protocols, as well as IPv6 and DNS traffic leakage. We also report on a number of apps actively performing TLS interception. Of particular concern are instances of apps that inject JavaScript programs for tracking, advertising, and for redirecting e-commerce traffic to external partners.

The smart grid is an electrical grid that has a duplex communication. This communication is between the utility and the consumer. Digital system, automation system, computers and control are the various systems of Smart Grid. It finds applications in a wide variety of systems. Some of its applications have been designed to reduce the risk of power system blackout. Dynamic vulnerability assessment is done to identify, quantify, and prioritize the vulnerabilities in a system. This paper presents a novel approach for classifying the data into one of the two classes called vulnerable or non-vulnerable by carrying out Dynamic Vulnerability Assessment (DVA) based on some data mining techniques such as Multichannel Singular Spectrum Analysis (MSSA), and Principal Component Analysis (PCA), and a machine learning tool such as Support Vector Machine Classifier (SVM-C) with learning algorithms that can analyze data. The developed methodology is tested in the IEEE 57 bus, where the cause of vulnerability is transient instability. The results show that data mining tools can effectively analyze the patterns of the electric signals, and SVM-C can use those patterns for analyzing the system data as vulnerable or non-vulnerable and determines System Vulnerability Status.

This article focuses on the design of safe and attack-resilient Cyber-Physical Systems (CPS) equipped with multiple sensors measuring the same physical variable. A malicious attacker may be able to disrupt system performance through compromising a subset of these sensors. Consequently, we develop a precise and resilient sensor fusion algorithm that combines the data received from all sensors by taking into account their specified precisions. In particular, we note that in the presence of a shared bus, in which messages are broadcast to all nodes in the network, the attacker’s impact depends on what sensors he has seen before sending the corrupted measurements. Therefore, we explore the effects of communication schedules on the performance of sensor fusion and provide theoretical and experimental results advocating for the use of the Ascending schedule, which orders sensor transmissions according to their precision starting from the most precise. In addition, to improve the accuracy of the sensor fusion algorithm, we consider the dynamics of the system in order to incorporate past measurements at the current time. Possible ways of mapping sensor measurement history are investigated in the article and are compared in terms of the confidence in the final output of the sensor fusion. We show that the precision of the algorithm using history is never worse than the no-history one, while the benefits may be significant. Furthermore, we utilize the complementary properties of the two methods and show that their combination results in a more precise and resilient algorithm. Finally, we validate our approach in simulation and experiments on a real unmanned ground robot.

Information Fusion (IF) systems have long exploited data provided by hard (physics-based) sensors with the aspiration of making sense of the environment they are monitoring. In recent times, the IF community has recognized the potential of utilizing data generated by people, also known as soft data. In this study, we demonstrate how course of action (CoA) generation, one of the key elements of Level 3 High-Level Information Fusion and a vital component for security and defense decision support systems, can be augmented using soft (human-derived) data for improved mission effectiveness. This conceptualization is validated through an elaborate experiment situated in the maritime world. To the best of the authors' knowledge, this is the first study to apply soft data to automatic CoA generation in the maritime domain.

Computing researchers have long focused on improving energy-efficiency under the implicit assumption that all energy is created equal. Yet, this assumption is actually incorrect: energy's cost and carbon footprint vary substantially over time. As a result, consuming energy inefficiently when it is cheap and clean may sometimes be preferable to consuming it efficiently when it is expensive and dirty. Green datacenters adapt their energy usage to optimize for such variations, as reflected in changing electricity prices or renewable energy output. Thus, we introduce energy-agility as a new metric to evaluate green datacenter applications. To illustrate fundamental tradeoffs in energy-agile design, we develop GreenSort, a distributed sorting system optimized for energy-agility. GreenSort is representative of the long-running, massively-parallel, data-intensive tasks that are common in datacenters and amenable to delays from power variations. Our results demonstrate the importance of energy-agile design when considering the benefits of using variable power. For example, we show that GreenSort requires 31% more time and energy to complete when power varies based on real-time electricity prices versus when it is constant. Thus, in this case, real-time prices should be at least 31% lower than fixed prices to warrant using them.

With the increasing incentive of enterprises to ingest as much data as they can in what is commonly referred to as "data lakes", and with the recent development of multiple technologies to support this "load-first" paradigm, the new environment presents serious data management challenges. Among them, the assessment of data quality and cleaning large volumes of heterogeneous data sources become essential tasks in unveiling the value of big data. The coveted use of unstructured and semi-structured data in large volumes makes current data cleaning tools (primarily designed for relational data) not directly adoptable. We present CLAMS, a system to discover and enforce expressive integrity constraints from large amounts of lake data with very limited schema information (e.g., represented as RDF triples). This demonstration shows how CLAMS is able to discover the constraints and the schemas they are defined on simultaneously. CLAMS also introduces a scale-out solution to efficiently detect errors in the raw data. CLAMS interacts with human experts to both validate the discovered constraints and to suggest data repairs. CLAMS has been deployed in a real large-scale enterprise data lake and was experimented with a real data set of 1.2 billion triples. It has been able to spot multiple obscure data inconsistencies and errors early in the data processing stack, providing huge value to the enterprise.

As embedded devices (under the guise of "smart-whatever") rapidly proliferate into many domains, they become attractive targets for malware. Protecting them from software and physical attacks becomes both important and challenging. Remote attestation is a basic tool for mitigating such attacks. It allows a trusted party (verifier) to remotely assess software integrity of a remote, untrusted, and possibly compromised, embedded device (prover). Prior remote attestation methods focus on software (malware) attacks in a one-verifier/one-prover setting. Physical attacks on provers are generally ruled out as being either unrealistic or impossible to mitigate. In this paper, we argue that physical attacks must be considered, particularly, in the context of many provers, e.g., a network, of devices. As- suming that physical attacks require capture and subsequent temporary disablement of the victim device(s), we propose DARPA, a light-weight protocol that takes advantage of absence detection to identify suspected devices. DARPA is resilient against a very strong adversary and imposes minimal additional hardware requirements. We justify and identify DARPA's design goals and evaluate its security and costs.

Trust in SSL-based communication on the Internet is provided by Certificate Authorities (CAs) in the form of signed certificates. Checking the validity of a certificate involves three steps: (i) checking its expiration date, (ii) verifying its signature, and (iii) making sure that it is not revoked. Currently, Certificate Revocation checks (i.e. step (iii) above) are done either via Certificate Revocation Lists (CRLs) or Online Certificate Status Protocol (OCSP) servers. Unfortunately, both current approaches tend to incur such a high overhead that several browsers (including almost all mobile ones) choose not to check certificate revocation status, thereby exposing their users to significant security risks. To address this issue, we propose DCSP: a new low-latency approach that provides up-to-date and accurate certificate revocation information. DCSP capitalizes on the existing scalable and high-performance infrastructure of DNS. DCSP minimizes end user latency while, at the same time, requiring only a small number of cryptographic signatures by the CAs. Our design and initial performance results show that DCSP has the potential to perform an order of magnitude faster than the current state-of-the-art alternatives.

Induction is a successful approach for verification of hardware and software systems. A common practice is to model a system using logical formulas, and then use a decision procedure to verify that some logical formula is an inductive safety invariant for the system. A key ingredient in this approach is coming up with the inductive invariant, which is known as invariant inference. This is a major difficulty, and it is often left for humans or addressed by sound but incomplete abstract interpretation. This paper is motivated by the problem of inductive invariants in shape analysis and in distributed protocols. This paper approaches the general problem of inferring first-order inductive invariants by restricting the language L of candidate invariants. Notice that the problem of invariant inference in a restricted language L differs from the safety problem, since a system may be safe and still not have any inductive invariant in L that proves safety. Clearly, if L is finite (and if testing an inductive invariant is decidable), then inferring invariants in L is decidable. This paper presents some interesting cases when inferring inductive invariants in L is decidable even when L is an infinite language of universal formulas. Decidability is obtained by restricting L and defining a suitable well-quasi-order on the state space. We also present some undecidability results that show that our restrictions are necessary. We further present a framework for systematically constructing infinite languages while keeping the invariant inference problem decidable. We illustrate our approach by showing the decidability of inferring invariants for programs manipulating linked-lists, and for distributed protocols.

Complex networks usually expose community structure with groups of nodes sharing many links with the other nodes in the same group and relatively few with the nodes of the rest. This feature captures valuable information about the organization and even the evolution of the network. Over the last decade, a great number of algorithms for community detection have been proposed to deal with the increasingly complex networks. However, the problem of doing this in a private manner is rarely considered. In this paper, we solve this problem under differential privacy, a prominent privacy concept for releasing private data. We analyze the major challenges behind the problem and propose several schemes to tackle them from two perspectives: input perturbation and algorithm perturbation. We choose Louvain method as the back-end community detection for input perturbation schemes and propose the method LouvainDP which runs Louvain algorithm on a noisy super-graph. For algorithm perturbation, we design ModDivisive using exponential mechanism with the modularity as the score. We have thoroughly evaluated our techniques on real graphs of different sizes and verified that ModDivisive steadily gives the best modularity and avg.F1Score on large graphs while LouvainDP outperforms the remaining input perturbation competitors in certain settings.