Biblio

Digital forensic investigators today are faced with numerous problems when recovering footprints of criminal activity that involve the use of computer systems. Investigators need the ability to recover evidence in a forensically sound manner, even when criminals actively work to alter the integrity, veracity, and provenance of data, applications and software that are used to support illicit activities. In many ways, operating systems (OS) can be strengthened from a technological viewpoint to support verifiable, accurate, and consistent recovery of system data when needed for forensic collection efforts. In this paper, we extend the ideas for forensic-friendly OS design by proposing the use of a practical form of computing on encrypted data (CED) and computing with encrypted functions (CEF) which builds upon prior work on component encryption (in circuits) and white-box cryptography (in software). We conduct experiments on sample programs to provide analysis of the approach based on security and efficiency, illustrating how component encryption can strengthen key OS functions and improve tamper-resistance to anti-forensic activities. We analyze the tradeoff space for use of the algorithm in a holistic approach that provides additional security and comparable properties to fully homomorphic encryption (FHE).

Hierarchical based formation is one of the approaches widely used to minimize the energy consumption in which node with higher residual energy routes the data gathered. Several hierarchical works were proposed in the literature with two and three layered architectures. In the work presented in this paper, we propose an enhanced architecture for three layered hierarchical clustering based approach, which is referred to as enhanced three-layer hierarchical clustering approach (EHCA). The EHCA is based on an enhanced feature of the grid node in terms of its mobility. Further, in our proposed EHCA, we introduce distributed clustering technique for lower level head selection and incorporate security mechanism to detect the presence of any malicious node. We show by simulation results that our proposed EHCA reduces the energy consumption significantly and thus improves the lifetime of the network. Also, we highlight the appropriateness of the proposed EHCA for battlefield surveillance applications.

iOS devices are steadily obtaining popularity of the majority of users because of its some unique advantages in recent years. They can do many things that have been done on a desktop computer or laptop. With the increase in the use of mobile devices by individuals, organizations and government, there are many problems with information security especially some sensitive data related to users. As we all known, encryption algorithm play a significant role in data security. In order to prevent data being intercepted and being leaked during communication, in this paper, we adopted DES encryption algorithm that is fast, simple and suitable for large amounts of data of encryption to encrypt the data of iOS client and adopted the ECC encryption algorithms that was used to overcome the shortcoming of exchanging keys in a securing way before communications. In addition, we should also consider the application isolation and security mechanism of iOS that these features also protect the data securing to some extent. Namely, we propose an encryption algorithm combined the strengths of DES and ECC and make full use of the advantages of hybrid algorithm. Then, we tested and evaluated the performances of the suggested cryptography mechanism within the mobile platform of iOS. The results show that the algorithm has fairly efficiency in practical applications and strong anti-attack ability and it also improves the security and efficiency in data transmission.

ERP helps enterprises to integrate internal information and to improve operating performance and reaction capability. However, it is not enough to depend on ERP if enterprises want to develop quickly. The enterprise also needs several external supporting sub-systems such as personnel management system, equipment management system, etc. These sub-systems maybe outsourcing customized or developed by internal IT staff. They may be distributed in many branches or headquarter to collect the first line of data and then to deliver data to ERP for data integration. Most enterprises use human or timing batch process via internet to deliver data to ERP, but the two methods are not ideal from the view point of efficiency and security. This paper proposes a fast and safe way with both trigger and data replication techniques to deliver in time the distributed data to ERP for data integration.

Critical information systems strongly rely on event logging techniques to collect data, such as housekeeping/error events, execution traces and dumps of variables, into unstructured text logs. Event logs are the primary source to gain actionable intelligence from production systems. In spite of the recognized importance, system/application logs remain quite underutilized in security analytics when compared to conventional and structured data sources, such as audit traces, network flows and intrusion detection logs. This paper proposes a method to measure the occurrence of interesting activity (i.e., entries that should be followed up by analysts) within textual and heterogeneous runtime log streams. We use an entropy-based approach, which makes no assumptions on the structure of underlying log entries. Measurements have been done in a real-world Air Traffic Control information system through a data analytics framework. Experiments suggest that our entropy-based method represents a valuable complement to security analytics solutions.

Malware writers often develop malware with automated measures, so the number of malware has increased dramatically. Automated measures tend to repeatedly use significant modules, which form the basis for identifying malware variants and discriminating malware families. Thus, we propose a novel visualization analysis method for researching malware similarity. This method converts malicious Windows Portable Executable (PE) files into local entropy images for observing internal features of malware, and then normalizes local entropy images into entropy pixel images for malware classification. We take advantage of the Jaccard index to measure similarities between entropy pixel images and the k-Nearest Neighbor (kNN) classification algorithm to assign entropy pixel images to different malware families. Preliminary experimental results show that our visualization method can discriminate malware families effectively.

The display image on the visual display unit (VDU) can be retrieved from the radiated and conducted emission at some distance with no trace. In this paper, the maximum eavesdropping distance for the unintentional radiation and conduction electromagnetic (EM) signals which contain information has been estimated in theory by considering some realistic parameters. Firstly, the maximum eavesdropping distance for the unintentional EM radiation is estimated based on the reception capacity of a log-periodic antenna which connects to a receiver, the experiment data, the attenuation in free-space and the additional attenuation in the propagation path. And then, based on a multi-conductor transmission model and some experiment results, the maximum eavesdropping distance for the conducted emission is theoretically derived. The estimating results demonstrated that the ITE equipment may also exist threat of the information leakage even if it has met the current EMC requirements.

We show that a class of statistical properties of distributions, which includes such practically relevant properties as entropy, the number of distinct elements, and distance metrics between pairs of distributions, can be estimated given a sublinear sized sample. Specifically, given a sample consisting of independent draws from any distribution over at most k distinct elements, these properties can be estimated accurately using a sample of size O(k log k). For these estimation tasks, this performance is optimal, to constant factors. Complementing these theoretical results, we also demonstrate that our estimators perform exceptionally well, in practice, for a variety of estimation tasks, on a variety of natural distributions, for a wide range of parameters. The key step in our approach is to first use the sample to characterize the ``unseen'' portion of the distribution—effectively reconstructing this portion of the distribution as accurately as if one had a logarithmic factor larger sample. This goes beyond such tools as the Good-Turing frequency estimation scheme, which estimates the total probability mass of the unobserved portion of the distribution: We seek to estimate the shape of the unobserved portion of the distribution. This work can be seen as introducing a robust, general, and theoretically principled framework that, for many practical applications, essentially amplifies the sample size by a logarithmic factor; we expect that it may be fruitfully used as a component within larger machine learning and statistical analysis systems.

In recent years, behavioral biometrics have become a popular approach to support continuous authentication systems. Most generally, a continuous authentication system can make two types of errors: false rejects and false accepts. Based on this, the most commonly reported metrics to evaluate systems are the False Reject Rate (FRR) and False Accept Rate (FAR). However, most papers only report the mean of these measures with little attention paid to their distribution. This is problematic as systematic errors allow attackers to perpetually escape detection while random errors are less severe. Using 16 biometric datasets we show that these systematic errors are very common in the wild. We show that some biometrics (such as eye movements) are particularly prone to systematic errors, while others (such as touchscreen inputs) show more even error distributions. Our results also show that the inclusion of some distinctive features lowers average error rates but significantly increases the prevalence of systematic errors. As such, blind optimization of the mean EER (through feature engineering or selection) can sometimes lead to lower security. Following this result we propose the Gini Coefficient (GC) as an additional metric to accurately capture different error distributions. We demonstrate the usefulness of this measure both to compare different systems and to guide researchers during feature selection. In addition to the selection of features and classifiers, some non- functional machine learning methodologies also affect error rates. The most notable examples of this are the selection of training data and the attacker model used to develop the negative class. 13 out of the 25 papers we analyzed either include imposter data in the negative class or randomly sample training data from the entire dataset, with a further 6 not giving any information on the methodology used. Using real-world data we show that both of these decisions lead to significant underestimation of error rates by 63% and 81%, respectively. This is an alarming result, as it suggests that researchers are either unaware of the magnitude of these effects or might even be purposefully attempting to over-optimize their EER without actually improving the system.

Software security is an important aspect of ensuring software quality. Early detection of vulnerable code during development is essential for the developers to make cost and time effective software testing. The traditional software metrics are used for early detection of software vulnerability, but they are not directly related to code constructs and do not specify any particular granularity level. The goal of this study is to help developers evaluate software security using class-level traceable patterns called micro patterns to reduce security risks. The concept of micro patterns is similar to design patterns, but they can be automatically recognized and mined from source code. If micro patterns can better predict vulnerable classes compared to traditional software metrics, they can be used in developing a vulnerability prediction model. This study explores the performance of class-level patterns in vulnerability prediction and compares them with traditional class-level software metrics. We studied security vulnerabilities as reported for one major release of Apache Tomcat, Apache Camel and three stand-alone Java web applications. We used machine learning techniques for predicting vulnerabilities using micro patterns and class-level metrics as features. We found that micro patterns have higher recall in detecting vulnerable classes than the software metrics.

It is difficult to assess the security of modern enterprise networks because they are usually dynamic with configuration changes (such as changes in topology, firewall rules, etc). Graphical security models (e.g., Attack Graphs and Attack Trees) and security metrics (e.g., attack cost, shortest attack path) are widely used to systematically analyse the security posture of network systems. However, there are problems using them to assess the security of dynamic networks. First, the existing graphical security models are unable to capture dynamic changes occurring in the networks over time. Second, the existing security metrics are not designed for dynamic networks such that their effectiveness to the dynamic changes in the network is still unknown. In this paper, we conduct a comprehensive analysis via simulations to evaluate the effectiveness of security metrics using a Temporal Hierarchical Attack Representation Model. Further, we investigate the varying effects of security metrics when changes are observed in the dynamic networks. Our experimental analysis shows that different security metrics have varying security posture changes with respect to changes in the network.

It is difficult to assess the security of modern enterprise networks because they are usually dynamic with configuration changes (such as changes in topology, firewall rules, etc). Graphical security models (e.g., Attack Graphs and Attack Trees) and security metrics (e.g., attack cost, shortest attack path) are widely used to systematically analyse the security posture of network systems. However, there are problems using them to assess the security of dynamic networks. First, the existing graphical security models are unable to capture dynamic changes occurring in the networks over time. Second, the existing security metrics are not designed for dynamic networks such that their effectiveness to the dynamic changes in the network is still unknown. In this paper, we conduct a comprehensive analysis via simulations to evaluate the effectiveness of security metrics using a Temporal Hierarchical Attack Representation Model. Further, we investigate the varying effects of security metrics when changes are observed in the dynamic networks. Our experimental analysis shows that different security metrics have varying security posture changes with respect to changes in the network.

It is difficult to assess the security of modern enterprise networks because they are usually dynamic with configuration changes (such as changes in topology, firewall rules, etc). Graphical security models (e.g., Attack Graphs and Attack Trees) and security metrics (e.g., attack cost, shortest attack path) are widely used to systematically analyse the security posture of network systems. However, there are problems using them to assess the security of dynamic networks. First, the existing graphical security models are unable to capture dynamic changes occurring in the networks over time. Second, the existing security metrics are not designed for dynamic networks such that their effectiveness to the dynamic changes in the network is still unknown. In this paper, we conduct a comprehensive analysis via simulations to evaluate the effectiveness of security metrics using a Temporal Hierarchical Attack Representation Model. Further, we investigate the varying effects of security metrics when changes are observed in the dynamic networks. Our experimental analysis shows that different security metrics have varying security posture changes with respect to changes in the network.

Here we explore the applicability of traditional sliding window based convolutional neural network (CNN) detection pipeline and region based object detection techniques such as Faster Region-based CNN (R-CNN) and Region-based Fully Convolutional Networks (R-FCN) on the problem of object detection in X-ray security imagery. Within this context, with limited dataset availability, we employ a transfer learning paradigm for network training tackling both single and multiple object detection problems over a number of R-CNN/R-FCN variants. The use of first-stage region proposal within the Faster RCNN and R-FCN provide superior results than traditional sliding window driven CNN (SWCNN) approach. With the use of Faster RCNN with VGG16, pretrained on the ImageNet dataset, we achieve 88.3 mAP for a six object class X-ray detection problem. The use of R-FCN with ResNet-101, yields 96.3 mAP for the two class firearm detection problem requiring 0.1 second computation per image. Overall we illustrate the comparative performance of these techniques as object localization strategies within cluttered X-ray security imagery.

We propose a method for comparative analysis of evaluation of the cryptographic strength of the asymmetric encryption algorithms RSA and the existing GOST R 34.10-2001. Describes the fundamental design ratios, this method is based on computing capacity used for decoding and the forecast for the development of computer technology.

We present a system for identifying interesting social media posts on Twitter and delivering them to users' mobile devices in real time as push notifications. In our problem formulation, users are interested in broad topics such as politics, sports, and entertainment: our system processes tweets in real time to identify relevant, novel, and salient content. There are three interesting aspects to our work: First, instead of attempting to tame the cacophony of unfiltered tweets, we exploit a smaller, but still sizeable, collection of curated tweet streams corresponding to the Twitter accounts of different media outlets. Second, we apply distant supervision to extract topic labels from curated streams that have a specific focus, which can then be leveraged to build high-quality topic classifiers essentially "for free". Finally, our system delivers content via Twitter direct messages, supporting in situ interactions modeled after conversations with intelligent agents. These ideas are demonstrated in an end-to-end working prototype.

The Internet of Things (IoT) comes together with the connection between sensors and devices. These smart devices have been upgraded from a standalone device which can only handle a specific task at one time to an interactive device that can handle multiple tasks in time. However, this technology has been exposed to many vulnerabilities especially on the malicious attacks of the devices. With the IoT constraints and low-security mechanisms applied, the malicious attacks could exploit the sensor vulnerability to provide wrong data where it can lead to wrong interpretation and actuation to the users. Due to this problems, this short paper presents an event-based access control framework that considers integrity, privacy and the authenticity in the IoT devices.

We propose to use a genetic algorithm to evolve novel reconfigurable hardware to implement elliptic curve cryptographic combinational logic circuits. Elliptic curve cryptography offers high security-level with a short key length making it one of the most popular public-key cryptosystems. Furthermore, there are no known sub-exponential algorithms for solving the elliptic curve discrete logarithm problem. These advantages render elliptic curve cryptography attractive for incorporating in many future cryptographic applications and protocols. However, elliptic curve cryptography has proven to be vulnerable to non-invasive side-channel analysis attacks such as timing, power, visible light, electromagnetic, and acoustic analysis attacks. In this paper, we use a genetic algorithm to address this vulnerability by evolving combinational logic circuits that correctly implement elliptic curve cryptographic hardware that is also resistant to simple timing and power analysis attacks. Using a fitness function composed of multiple objectives - maximizing correctness, minimizing propagation delays and minimizing circuit size, we can generate correct combinational logic circuits resistant to non-invasive, side channel attacks. To the best of our knowledge, this is the first work to evolve a cryptography circuit using a genetic algorithm. We implement evolved circuits in hardware on a Xilinx Kintex-7 FPGA. Results reveal that the evolutionary algorithm can successfully generate correct, and side-channel resistant combinational circuits with negligible propagation delay.

According to the 2016 Internet Security Threat Report by Symantec, there are around 431 million variants of malware known. This effort focuses on malware used for spying on user's activities, remotely controlling devices, and identity and credential theft within a Windows based operating system. As Windows operating systems create and maintain a log of all events that are encountered, various malware are tested on virtual machines to determine what events they trigger in the Windows logs. The observations are compiled into Operating System specific lookup tables that can then be used to find the tested malware on other computers with the same Operating System.

Today's mobile applications increasingly rely on communication with a remote backend service to perform many critical functions, including handling user-specific information. This implies that some form of authentication should be used to associate a user with their actions and data. Since schemes involving tedious account creation procedures can represent "friction" for users, many applications are moving toward alternative solutions, some of which, while increasing usability, sacrifice security. This paper focuses on a new trend of authentication schemes based on what we call "device-public" information, which consists of properties and data that any application running on a device can obtain. While these schemes are convenient to users, since they require little to no interaction, they are vulnerable by design, since all the needed information to authenticate a user is available to any app installed on the device. An attacker with a malicious app on a user's device could easily hijack the user's account, steal private information, send (and receive) messages on behalf of the user, or steal valuable virtual goods. To demonstrate how easily these vulnerabilities can be weaponized, we developed a generic exploitation technique that first mines all relevant data from a victim's phone, and then transfers and injects them into an attacker's phone to fool apps into granting access to the victim's account. Moreover, we developed a dynamic analysis detection system to automatically highlight problematic apps. Using our tool, we analyzed 1,000 popular applications and found that 41 of them, including the popular messaging apps WhatsApp and Viber, were vulnerable. Finally, our work proposes solutions to this issue, based on modifications to the Android API.

Industrial control systems are cyber-physical systems that are used to operate critical infrastructures such as smart grids, traffic systems, industrial facilities, and water distribution networks. The digitalization of these systems increases their efficiency and decreases their cost of operation, but also makes them more vulnerable to cyber-attacks. In order to protect industrial control systems from cyber-attacks, the installation of multiple layers of security measures is necessary. In this paper, we study how to allocate a large number of security measures under a limited budget, such as to minimize the total risk of cyber-attacks. The security measure allocation problem formulated in this way is a combinatorial optimization problem subject to a knapsack (budget) constraint. The formulated problem is NP-hard, therefore we propose a method to exploit submodularity of the objective function so that polynomial time algorithms can be applied to obtain solutions with guaranteed approximation bounds. The problem formulation requires a preprocessing step in which attack scenarios are selected, and impacts and likelihoods of these scenarios are estimated. We discuss how the proposed method can be applied in practice.

Information technology graduates reach industry and innovate for the future after completing demanding degrees. Upper division college courses require long hours of work on class projects and exams. Some students have hopes of completing their degrees, but are deterred due to many different issues. Instructors can monitor students' progress based on their assignments, projects, and exams. Judging students' understanding and potential for success becomes more difficult when handling large classes. In this paper we utilize IBM Text Analytics Web Tooling on large amounts of unstructured text data collected from past assignments, exams, and discussions to help professors make assessments faster for large classes. In particular, we focus on an Information Security course offered at San Jose State University and use its classroom-generated data to determine if the extracted information provides strong insights for professors to help struggling students. We examine these issues through exploratory analysis.

Knowledge work such as summarizing related research in preparation for writing, typically requires the extraction of useful information from scientific literature. Nowadays the primary source of information for researchers comes from electronic documents available on the Web, accessible through general and academic search engines such as Google Scholar or IEEE Xplore. Yet, the vast amount of resources makes retrieving only the most relevant results a difficult task. As a consequence, researchers are often confronted with loads of low-quality or irrelevant content. To address this issue we introduce a novel system, which combines a rich, interactive Web-based user interface and different visualization approaches. This system enables researchers to identify key phrases matching current information needs and spot potentially relevant literature within hierarchical document collections. The chosen context was the collection and summarization of related work in preparation for scientific writing, thus the system supports features such as bibliography and citation management, document metadata extraction and a text editor. This paper introduces the design rationale and components of the PaperViz. Moreover, we report the insights gathered in a formative design study addressing usability.

Meta-programs are programs that generate other programs, but in weakly type-safe systems, type-checking a meta-program only establishes its own type safety, and generated programs need additional type-checking after generation. Strong type safety of a meta-program implies type safety of any generated object program, a property with important engineering benefits. Current strongly type-safe systems suffer from expressivity limitations and cannot support many meta-programs found in practice, for example automatic generation of lenses. To overcome this, we move away from the idea of staged meta-programming. Instead, we use an off-the-shelf dependently-typed language as the meta-language and a relatively standard, intrinsically well-typed representation of the object language. We scale this approach to practical meta-programming, by choosing a high-level, explicitly typed intermediate representation as the object language, rather than a surface programming language. We implement our approach as a library for the Glasgow Haskell Compiler (GHC) and evaluate it on several meta-programs, including a deriveLenses meta-program taken from a real-world Haskell lens library. Our evaluation demonstrates expressivity beyond the state of the art and applicability to real settings, at little cost in terms of code size.

Hacker forums and other social platforms may contain vital information about cyber security threats. But using manual analysis to extract relevant threat information from these sources is a time consuming and error-prone process that requires a significant allocation of resources. In this paper, we explore the potential of Machine Learning methods to rapidly sift through hacker forums for relevant threat intelligence. Utilizing text data from a real hacker forum, we compared the text classification performance of Convolutional Neural Network methods against more traditional Machine Learning approaches. We found that traditional machine learning methods, such as Support Vector Machines, can yield high levels of performance that are on par with Convolutional Neural Network algorithms.