Biblio

Contemporary enterprise systems focus primarily on performance and development/maintenance costs. Dealing with cyber-threats and system compromise is relegated to good coding (i.e., defensive programming) and secure environment (e.g., patched OS, firewalls, etc.). This approach, while a necessary start, is not sufficient. Such security relies on no missteps, and compromise only need a single flaw; consequently, we must design for compromise and mitigate its impact. One approach is to utilize fine-grained modularization and isolation. In such a system, decomposition ensures that compromise of a single module presents limited and known risk to data/resource theft and denial. We propose mechanisms for automating such modular composition and consider its system performance impact.

Despite the additional protection it affords, two-factor authentication (2FA) adoption reportedly remains low. To better understand 2FA adoption and its barriers, we observed the deployment of a 2FA system at Carnegie Mellon University (CMU). We explore user behaviors and opinions around adoption, surrounding a mandatory adoption deadline. Our results show that (a) 2FA adopters found it annoying, but fairly easy to use, and believed it made their accounts more secure; (b) experience with CMU Duo often led to positive perceptions, sometimes translating into 2FA adoption for other accounts; and, (c) the differences between users required to adopt 2FA and those who adopted voluntarily are smaller than expected. We also explore the relationship between different usage patterns and perceived usability, and identify user misconceptions, insecure practices, and design issues. We conclude with recommendations for large-scale 2FA deployments to maximize adoption, focusing on implementation design, use of adoption mandates, and strategic messaging.

Approaches for programming language design used commonly in the research community today center around theoretical and performance-oriented evaluation. Recently, researchers have been considering more approaches to language design, including the use of quantitative and qualitative user studies that examine how different designs might affect programmers. In this paper, we argue for an interdisciplinary approach that incorporates many different methods in the creation and evaluation of programming languages. We argue that the addition of user-oriented design techniques can be helpful at many different stages in the programming language design process.

The evolution of modern enterprise networking has progressed to the point where two conditions make deception an important and urgent control to introduce to a target environment now: Inevitability of attacks and Context-awareness of attacks.

Conversational IT services are expected to reduce user wait times and improve overall customer satisfaction. Cloud-based solutions are readily available for enterprise subject matter experts (SMEs) to train user-question classifiers and build conversational services with little effort. However, methodologies that the SMEs can use to improve the response accuracy and conversation quality are merely stated and evaluated. In complex service scenarios such as software support, the scope of topics is typically large and the training samples are often limited. Thus, training the classifier based on labeled samples of plain user utterances is not effective in most cases. In this paper, we identify several methods for improving classification quality and evaluate them in concrete training set scenarios. Particularly, a process-based methodology is described that builds and refines on top of service domain knowledge in order to develop a scalable solution for training accurate conversation services. Enterprises and service providers are continuously seeking new ways to improve customer experience on working with IT systems, where user wait times and service resolution quality are critical business metrics. One of the latest trends is the use of conversational IT services. Customers can interact with a conversational service to express their questions in natural language and the system can automatically return relevant answers or execute back-end processes for automated actions. Various text classification techniques have been developed and applied to understand the user questions and trigger the correct responses. For instance, in the context of IT software support, customers can use conversational systems to get answers about software product errors, licenses, or upgrade processes. While the potential benefits of building conversational services are huge, it is often difficult to effectively train classification models that cover well the scope of realistically complex services. In this paper, we propose a training methodology that addresses the limitations in both the scope of topics and the scarcity of the training set. We further evaluate the proposed methodology in a real service support scenario and share the lessons learned.

Software Defined Network (SDN) architecture is a new and novel way of network management mechanism. In SDN, switches do not process the incoming packets like conventional network computing environment. They match for the incoming packets in the forwarding tables and if there is none it will be sent to the controller for processing which is the operating system of the SDN. A Distributed Denial of Service (DDoS) attack is a biggest threat to cyber security in SDN network. The attack will occur at the network layer or the application layer of the compromised systems that are connected to the network. In this paper a machine learning based intelligent method is proposed which can detect the incoming packets as infected or not. The different machine learning algorithms adopted for accomplishing the task are Naive Bayes, K-Nearest neighbor (KNN) and Support vector machine (SVM) to detect the anomalous behavior of the data traffic. These three algorithms are compared according to their performances and KNN is found to be the suitable one over other two. The performance measure is taken here is the detection rate of infected packets.

In Mobile Ad-hoc Network (MANET), we cannot predict the clear picture of the topology of a node because of its varying nature. Without notice participation and departure of nodes results in lack of trust relationship between nodes. In such circumstances, there is no guarantee that path between two nodes would be secure or free of malicious nodes. The presence of single malicious node could lead repeatedly compromised node. After providing security to route and data packets still, there is a need for the implementation of defense mechanism that is intrusion detection system(IDS) against compromised nodes. In this paper, we have implemented IDS, which defend against some routing attacks like the black hole and gray hole successfully. After measuring performance we get marginally increased Packet delivery ratio and Throughput.

In the context of the rapid technological progress, the cyber-threats become a serious challenge that requires immediate and continuous action. As cybercrime poses a permanent and increasing threat, governments, corporate and individual users of the cyber-space are constantly struggling to ensure an acceptable level of security over their assets. Maliciousness on the cyber-space spans identity theft, fraud, and system intrusions. This is due to the benefits of cyberspace-low entry barriers, user anonymity, and spatial and temporal separation between users, make it a fertile field for deception and fraud. Numerous, supervised and unsupervised, techniques have been proposed and used to identify fraudulent transactions and activities that deviate from regular patterns of behaviour. For instance, neural networks and genetic algorithms were used to detect credit card fraud in a dataset covering 13 months and 50 million credit card transactions. Unsupervised methods, such as clustering analysis, have been used to identify financial fraud or to filter fake online product reviews and ratings on e-commerce websites. Blockchain technology has demonstrated its feasibility and relevance in e-commerce. Its use is now being extended to new areas, related to electronic government. The technology appears to be the most appropriate in areas that require storage and processing of large amounts of protected data. The question is what can blockchain technology do and not do to fight malicious online activity?

Cyber-Physical Systems (CPS) have been increasingly subject to cyber-attacks including code injection attacks. Zero day attacks further exasperate the threat landscape by requiring a shift to defense in depth approaches. With the tightly coupled nature of cyber components with the physical domain, these attacks have the potential to cause significant damage if safety-critical applications such as automobiles are compromised. Moving target defense techniques such as instruction set randomization (ISR) have been commonly proposed to address these types of attacks. However, under current implementations an attack can result in system crashing which is unacceptable in CPS. As such, CPS necessitate proper control reconfiguration mechanisms to prevent a loss of availability in system operation. This paper addresses the problem of maintaining system and security properties of a CPS under attack by integrating ISR, detection, and recovery capabilities that ensure safe, reliable, and predictable system operation. Specifically, we consider the problem of detecting code injection attacks and reconfiguring the controller in real-time. The developed framework is demonstrated with an autonomous vehicle case study.

This paper presents a computational platform for dynamic security assessment (DSA) of large electricity grids, developed as part of the iTesla project. It leverages High Performance Computing (HPC) to analyze large power systems, with many scenarios and possible contingencies, thus paving the way for pan-European operational stability analysis. The results of the DSA are summarized by decision trees of 11 stability indicators. The platform's workflow and parallel implementation architecture is described in detail, including the way commercial tools are integrated into a plug-in architecture. A case study of the French grid is presented, with over 8000 scenarios and 1980 contingencies. Performance data of the case study (using 10,000 parallel cores) is analyzed, including task timings and data flows. Finally, the generated decision trees are compared with test data to quantify the functional performance of the DSA platform.

Aiming at the disadvantage of poor convergence performance of PSO and artificial swarm algorithm, an improved hybrid algorithm is proposed to overcome the shortcomings of complex optimization problems. Through the test of four standard function by hybrid algorithm and compared the result with standard particle swarm optimization (PSO) algorithm and Artificial Bee Colony (ABC) algorithm, the convergence rate and convergence precision of the hybrid algorithm are both superior to those of the standard particle swarm algorithm and Artificial Bee Colony algorithm, presenting a better optimal performance.

Email is the most admired method of exchanging messages using the Internet. One of the intimidations to email users is to detect the spam they receive. This can be addressed using different detection and filtering techniques. Machine learning algorithms, especially Support Vector Machine (SVM), can play vital role in spam detection. We propose the use of weighted SVM for spam filtering using weight variables obtained by KFCM algorithm. The weight variables reflect the importance of different classes. The misclassification of emails is reduced by the growth of weight value. We evaluate the impact of spam detection using SVM, WSVM with KPCM and WSVM with KFCM.UCI Repository SMS Spam base dataset is used for our experimentation.

The problem of optimal attack path analysis is one of the hotspots in network security. Many methods are available to calculate an optimal attack path, such as Q-learning algorithm, heuristic algorithms, etc. But most of them have shortcomings. Some methods can lead to the problem of path loss, and some methods render the result un-comprehensive. This article proposes an improved Monte Carlo Graph Search algorithm (IMCGS) to calculate optimal attack paths in target network. IMCGS can avoid the problem of path loss and get comprehensive results quickly. IMCGS is divided into two steps: selection and backpropagation, which is used to calculate optimal attack paths. A weight vector containing priority, host connection number, CVSS value is proposed for every host in an attack path. This vector is used to calculate the evaluation value, the total CVSS value and the average CVSS value of a path in the target network. Result for a sample test network is presented to demonstrate the capabilities of the proposed algorithm to generate optimal attack paths in one single run. The results obtained by IMCGS show good performance and are compared with Ant Colony Optimization Algorithm (ACO) and k-zero attack graph.

It is a research hotspot that using blockchain technology to solve the security problems of the Internet of Things (IoT). Although many related ideas have been proposed, there are very few literatures with theoretical and data support. This paper focuses on the research of model construction and performance evaluation. First, an IoT security model is established based on blockchain and InterPlanetary File System (IPFS). In this model, many security risks of traditional IoT architectures can be avoided, and system performance is significantly improved in distributed large capacity storage, concurrency and query. Secondly, the performance of the proposed model is evaluated through the average latency and throughput, which are meaningful for further research and optimization of this direction. Analysis and test results demonstrate the effectiveness of the blockchain-based security model.

Power systems domain has generally been very conservative in terms of conducting digital forensic investigations, especially so since the advent of smart grids. This lack of research due to a multitude of challenges has resulted in absence of knowledge base and resources to facilitate such an investigation. Digitalization in the form of smart grids is upon us but in case of cyber-attacks, attribution to such attacks is challenging and difficult if not impossible. In this research, we have identified digital forensic artifacts resulting from a cyber-attack on Wide Area Monitoring, Protection and Control (WAMPAC) systems, which will help an investigator attribute an attack using the identified evidences. The research also shows the usage of sandboxing for digital forensics along with hardware-in-the-loop (HIL) setup. This is first of its kind effort to identify and acquire all the digital forensic evidences for WAMPAC systems which will ultimately help in building a body of knowledge and taxonomy for power system forensics.

To observe and control a networked system, especially in failure-prone circumstances, it is imperative that the underlying network structure be robust against node or link failures. A common approach for increasing network robustness is redundancy: deploying additional nodes and establishing new links between nodes, which could be prohibitively expensive. This paper addresses the problem of improving structural robustness of networks without adding extra links. The main idea is to ensure that a small subset of nodes, referred to as the trusted nodes, remains intact and functions correctly at all times. We extend two fundamental metrics of structural robustness with the notion of trusted nodes, network connectivity, and r-robustness, and then show that by controlling the number and location of trusted nodes, any desired connectivity and robustness can be achieved without adding extra links. We study the complexity of finding trusted nodes and construction of robust networks with trusted nodes. Finally, we present a resilient consensus algorithm with trusted nodes and show that, unlike existing algorithms, resilient consensus is possible in sparse networks containing few trusted nodes.

We address the problem of distributed state estimation of a linear dynamical process in an attack-prone environment. A network of sensors, some of which can be compromised by adversaries, aim to estimate the state of the process. In this context, we investigate the impact of making a small subset of the nodes immune to attacks, or “trusted”. Given a set of trusted nodes, we identify separate necessary and sufficient conditions for resilient distributed state estimation. We use such conditions to illustrate how even a small trusted set can achieve a desired degree of robustness (where the robustness metric is specific to the problem under consideration) that could otherwise only be achieved via additional measurement and communication-link augmentation. We then establish that, unfortunately, the problem of selecting trusted nodes is NP-hard. Finally, we develop an attack-resilient, provably-correct distributed state estimation algorithm that appropriately leverages the presence of the trusted nodes.

Prior work notes dispositional, learned, and situational aspects of trust in automation. However, no work has investigated the relative role of these factors in initial trust of an automated system. Moreover, trust in automation researchers often consider trust unidimensionally, whereas ability, integrity, and benevolence perceptions (i.e., trusting beliefs) may provide a more thorough understanding of trust dynamics. To investigate this, we recruited 163 participants on Amazon's Mechanical Turk (MTurk) and randomly assigned each to one of 4 videos describing a hypothetical drone system: one control, the others with additional system performance or process, or both types of information. Participants reported on trusting beliefs in the system, propensity to trust other people, risk-taking tendencies, and trust in the government law enforcement agency behind the system. We found that financial risk-taking tendencies influenced trusting beliefs. Also, those who received process information were likely to have higher integrity and ability beliefs than those not receiving process information, while those who received performance information were likely to have higher ability beliefs. Lastly, perceptions of structural assurance positively influenced all three trusting beliefs. Our findings suggest that a) users' risk-taking tendencies influence trustworthiness perceptions of systems, b) different types of information about a system have varied effects on the trustworthiness dimensions, and c) institutions play an important role in users' calibration of trust. Insights gained from this study can help design training materials and interfaces that improve user trust calibration in automated systems.

Although the vision of 5G is to accommodate billions IoT devices and applications, its success depends very much on its ability to provide enhanced and affordable security. This paper introduces an Identity Federation solution which reuses the SIM authentication for cellular IoT devices enabling single-sign-on. The proposed solution alleviates the IoT provider's burden of device identity management at the same time as the operational costs are reduced considerably. The proposed solution is realized by open source software for LTE, identity management and IoT.

With the rapid development of Android systems and the growing of Android market, Android system has become a focus of developers and users. MTK6795 is System-on-a-chip (SoC), which is specially designed by MediaTek for high-end smart phones. It integrates the application processor and the baseband processor in just one chip. In this paper, a new encryption method based on the baseband processor of MT6795 SoC is proposed and successfully applied on one Android-based smart phone to protect user data. In this method, the encryption algorithm and private user data are isolated into two processors, which improves the security of users' private data.

This paper describes an approach to detecting malicious code introduced by insiders, which can compromise the data integrity in a program. The approach identifies security spots in a program, which are either malicious code or benign code. Malicious code is detected by reviewing each security spot to determine whether it is malicious or benign. The integrity breach conditions (IBCs) for object-oriented programs are specified to identify security spots in the programs. The IBCs are specified by means of the concepts of coupling within an object or between objects. A prototype tool is developed to validate the approach with a case study.

Model Based Systems Engineering (MBSE) adds efficiency during all phases of the design lifecycle. MBSE tools enforce design policies and rules to capture the design elements, inter-element relationships, and their attributes in a consistent manner. The system elements, and attributes are captured and stored in a centralized MBSE database for future retrieval. Systems that depend on computer networks can be designed using MBSE to meet cybersecurity and resilience requirements. At each step of a structured systems engineering methodology, decisions need to be made regarding the selection of architecture and designs that mitigate cyber risk and enhance cyber resilience. Detailed risk and decision analysis methods involve complex models and computations which are often characterized as a Big Data analytic problem. In this paper, we argue in favor of using graph analytic methods with model based systems engineering to support risk and decision analyses when engineering cyber resilient systems.

Increasing number of Internet-scale applications, such as video streaming, incur huge amount of wide area traffic. Such traffic over the unreliable Internet without bandwidth guarantee suffers unpredictable network performance. This result, however, is unappealing to the application providers. Fortunately, Internet giants like Google and Microsoft are increasingly deploying their private wide area networks (WANs) to connect their global datacenters. Such high-speed private WANs are reliable, and can provide predictable network performance. In this paper, we propose a new type of service-inter-datacenter network as a service (iDaaS), where traditional application providers can reserve bandwidth from those Internet giants to guarantee their wide area traffic. Specifically, we design a bandwidth trading market among multiple iDaaS providers and application providers, and concentrate on the essential bandwidth pricing problem. The involved challenging issue is that the bandwidth price of each iDaaS provider is not only influenced by other iDaaS providers, but also affected by the application providers. To address this issue, we characterize the interaction between iDaaS providers and application providers using a Stackelberg game model, and analyze the existence and uniqueness of the equilibrium. We further present an efficient bandwidth pricing algorithm by blending the advantage of a geometrical Nash bargaining solution and the demand segmentation method. For comparison, we present two bandwidth reservation algorithms, where each iDaaS provider's bandwidth is reserved in a weighted fair manner and a max-min fair manner, respectively. Finally, we conduct comprehensive trace-driven experiments. The evaluation results show that our proposed algorithms not only ensure the revenue of iDaaS providers, but also provide bandwidth guarantee for application providers with lower bandwidth price per unit.

The globalization and outsourcing of the semiconductor industry has raised serious concerns about the trustworthiness of the hardware. Importing Third Party IP cores in the Integrated Chip design has opened gates for new form of attacks on hardware. Hardware Trojans embedded in Third Party IPs has necessitated the need for secure IC design process. Design-for-Trust techniques aimed at detection of Hardware Trojans come with overhead in terms of area, latency and power consumption. In this work, we present a Cuckoo Search algorithm based Design Space Exploration process for finding low cost hardware solutions during High Level Synthesis. The exploration is conducted with respect to datapath resource allocation for single and nested loops. The proposed algorithm is compared with existing Hardware Trojan detection mechanisms and experimental results show that the proposed algorithm is able to achieve 3x improvement in Cost when compared existing algorithms.

Diversity has been identified as one of the key dimensions of recommendation utility that should be considered besides the overall accuracy of the system. A common diversification approach is to rerank results produced by a baseline recommendation engine according to a diversification criterion. The intent-aware framework is one of the frameworks that has been proposed for recommendations diversification. It assumes existence of a set of aspects associated with items, which also represent user intentions, and the framework promotes diversity across the aspects to address user expectations more accurately. In this paper we consider item-based collaborative filtering and suggest that the traditional view of item similarity is lacking a user perspective. We argue that user preferences towards different aspects should be reflected in recommendations produced by the system. We incorporate the intent-aware framework into the item-based recommendation algorithm by injecting personalised intent-aware covariance into the item similarity measure, and explore the impact of such change on the performance of the algorithm. Our experiments show that the proposed method improves both accuracy and diversity of recommendations, offering better accuracy/diversity tradeoff than existing solutions.