Biblio

Modern vehicles rely on hundreds of on-board electronic control units (ECUs) communicating over in-vehicle networks. As external interfaces to the car control networks (such as the on-board diagnostic (OBD) port, auxiliary media ports, etc.) become common, and vehicle-to-vehicle / vehicle-to-infrastructure technology is in the near future, the attack surface for vehicles grows, exposing control networks to potentially life-critical attacks. This paper addresses the need for securing the controller area network (CAN) bus by detecting anomalous traffic patterns via unusual refresh rates of certain commands. While previous works have identified signal frequency as an important feature for CAN bus intrusion detection, this paper provides the first such algorithm with experiments using three attacks in five (total) scenarios. Our data-driven anomaly detection algorithm requires only five seconds of training time (on normal data) and achieves true positive / false discovery rates of 0.9998/0.00298, respectively (micro-averaged across the five experimental tests).

Machine learning has been widely used and achieved considerable results in various research areas. On the other hand, machine learning becomes a big threat when malicious attackers make use it for the wrong purpose. As such a threat, self-evolving botnets have been considered in the past. The self-evolving botnets autonomously predict vulnerabilities by implementing machine learning with computing resources of zombie computers. Furthermore, they evolve based on the vulnerability, and thus have high infectivity. In this paper, we consider several models of Markov chains to counter the spreading of the self-evolving botnets. Through simulation experiments, this paper shows the behaviors of these models.

In this paper, we propose a theoretical framework to investigate the eavesdropping behavior in underwater acoustic sensor networks. In particular, we quantify the eavesdropping activities by the eavesdropping probability. Our derived results show that the eavesdropping probability heavily depends on acoustic signal frequency, underwater acoustic channel characteristics (such as spreading factor and wind speed) and different hydrophones (such as isotropic hydrophones and array hydrophones). Simulation results have further validate the effectiveness and the accuracy of our proposed model.

Users' QoE (Quality of Experience) in Multi-sensorial, Immersive, Collaborative Environments (MICE) applications is mostly measured by psychometric studies. These studies provide a subjective insight into the performance of such applications. In this paper, we hypothesize that spatial coherence or the lack of it of the embedded virtual objects among users has a correlation to the QoE in MICE. We use Position Discrepancy (PD) to model this lack of spatial coherence in MICE. Based on that, we propose a Hierarchical Position Discrepancy Model (HPDM) that computes PD at multiple levels to derive the application/system-level PD as a measure of performance.; AB@Experimental results on an example task in MICE show that HPDM can objectively quantify the application performance and has a correlation to the psychometric study-based QoE measurements. We envisage HPDM can provide more insight on the MICE application without the need for extensive user study.

This paper offers a new approach to modelling the effect of cyber-attacks on reliability of software used in industrial control applications. The model is based on the view that successful cyber-attacks introduce failure regions, which are not present in non-compromised software. The model is then extended to cover a fault tolerant architecture, such as the 1-out-of-2 software, popular for building industrial protection systems. The model is used to study the effectiveness of software maintenance policies such as patching and "cleansing" ("proactive recovery") under different adversary models ranging from independent attacks to sophisticated synchronized attacks on the channels. We demonstrate that the effect of attacks on reliability of diverse software significantly depends on the adversary model. Under synchronized attacks system reliability may be more than an order of magnitude worse than under independent attacks on the channels. These findings, although not surprising, highlight the importance of using an adequate adversary model in the assessment of how effective various cyber-security controls are.

During the life span of large software projects, developers often apply the same code changes to different code locations in slight variations. Since the application of these changes to all locations is time-consuming and error-prone, tools exist that learn change patterns from input examples, search for possible pattern applications, and generate corresponding recommendations. In many cases, the generated recommendations are syntactically or semantically wrong due to code movements in the input examples. Thus, they are of low accuracy and developers cannot directly copy them into their projects without adjustments. We present the Accurate REcommendation System (ARES) that achieves a higher accuracy than other tools because its algorithms take care of code movements when creating patterns and recommendations. On average, the recommendations by ARES have an accuracy of 96% with respect to code changes that developers have manually performed in commits of source code archives. At the same time ARES achieves precision and recall values that are on par with other tools.

Modern web browsers have accrued an incredibly broad set of features since being invented for hypermedia dissemination in 1990. Many of these features benefit users by enabling new types of web applications. However, some features also bring risk to users' privacy and security, whether through implementation error, unexpected composition, or unintended use. Currently there is no general methodology for weighing these costs and benefits. Restricting access to only the features which are necessary for delivering desired functionality on a given website would allow users to enforce the principle of lease privilege on use of the myriad APIs present in the modern web browser. However, security benefits gained by increasing restrictions must be balanced against the risk of breaking existing websites. This work addresses this problem with a methodology for weighing the costs and benefits of giving websites default access to each browser feature. We model the benefit as the number of websites that require the feature for some user-visible benefit, and the cost as the number of CVEs, lines of code, and academic attacks related to the functionality. We then apply this methodology to 74 Web API standards implemented in modern browsers. We find that allowing websites default access to large parts of the Web API poses significant security and privacy risks, with little corresponding benefit. We also introduce a configurable browser extension that allows users to selectively restrict access to low-benefit, high-risk features on a per site basis. We evaluated our extension with two hardened browser configurations, and found that blocking 15 of the 74 standards avoids 52.0% of code paths related to previous CVEs, and 50.0% of implementation code identified by our metric, without affecting the functionality of 94.7% of measured websites.

Network motifs are often called the building blocks of networks. Analysis of motifs is found to be an indispensable tool for understanding local network structure, in contrast to measures based on node degree distribution and its functions that primarily address a global network topology. As a result, networks that are similar in terms of global topological properties may differ noticeably at a local level. In the context of power grids, this phenomenon of the impact of local structure has been recently documented in fragility analysis and power system classification. At the same time, most studies of power system networks still tend to focus on global topo-logical measures of power grids, often failing to unveil hidden mechanisms behind vulnerability of real power systems and their dynamic response to malfunctions. In this paper a pilot study of motif-based analysis of power grid robustness under various types of intentional attacks is presented, with the goal of shedding light on local dynamics and vulnerability of power systems.

In order to overcome the excessive false detection of marginal noise and the object holes of the existing algorithm in outdoor panoramic surveillance, a moving object detection algorithm based on pixel background sample sets in panoramic scanning mode is proposed. In the light of the space distribution characteristics, neighborhood pixels have similar values. Therefore, a background sample set for each pixel is created by random sampling in the first scanning cycle which effectively avoids the false detection of marginal noise and reduces the time cost of background model establishment. The adjacent frame difference detection algorithm in the traditional camera motion mode is prone to object holes. To solve this problem, detection based on background sample sets is presented to obtain complete moving object region. The results indicate that the proposed moving object detection algorithm works more efficiently on reducing marginal noise interference, and obtains complete moving object information compared with the frame difference detection algorithm based on registration results in traditional camera motion mode, thereby meeting the needs of real-time detection as well as improving its accuracy.

Supervisory Control and Data Acquisition(SCADA) communications are often subjected to various sophisticated cyber-attacks mostly because of their static system characteristics, enabling an attacker for easier profiling of the target system(s) and thereby impacting the Critical Infrastructures(CI). In this Paper, a novel approach to mitigate such static vulnerabilities is proposed by implementing a Moving Target Defense (MTD) strategy in a power grid SCADA environment, leveraging the existing communication network with an end-to-end IP-Hopping technique among trusted peers. The main contribution involves the design and implementation of MTD Architecture on Iowa State's PowerCyber testbed for targeted cyber-attacks, without compromising the availability of a SCADA system and studying the delay and throughput characteristics for different hopping rates in a realistic environment. Finally, we study two cases and provide mitigations for potential weaknesses of the proposed mechanism. Also, we propose to incorporate port mutation to further increase attack complexity as part of future work.

Conventional intrusion detection systems for smart grid communications rely heavily on static based attack detection techniques. In essence, signatures created from historical data are compared to incoming network traffic to identify abnormalities. In the case of attacks where no historical data exists, static based approaches become ineffective thus relinquishing system resilience and stability. Moving target defense (MTD) has shown to be effective in discouraging attackers by introducing system entropy to increase exploit costs. Increase in exploit cost leads to a decrease in profitability for an attacker. In this paper, a Moving Target Defense Intrusion Detection System (MTDIDS) is proposed for smart grid IPv6 based advanced metering infrastructure. The advantage of MTDIDS is the ability to detect anomalies across moving targets by means of planar keys thereupon increasing detection rate. Evaluation of MTDIDS was carried out in a smart grid advanced metering infrastructure simulated in MATLAB.

End-hopping is an effective component of Moving Target Defense (MTD) by randomly hopping network configuration of host, which is a game changing technique against cyber-attack and can interrupt cyber kill chain in the early stage. In this paper, a novel end-hopping model, Multi End-hopping (MEH), is proposed to exploit the full potentials of MTD techniques by hosts cooperating with others to share possible configurable space (PCS). And an optimization method based on cooperative game is presented to make hosts form optimal alliances against reconnaissance, scanning and blind probing DoS attack. Those model and method confuse adversaries by establishing alliances of hosts to enlarge their PCS, which thwarts various malicious scanning and mitigates probing DoS attack intensity. Through simulations, we validate the correctness of MEH model and the effectiveness of optimization method. Experiment results show that the proposed model and method increase system stable operational probability while introduces a low overhead in optimization.

Volume of digital data is increasing at a faster rate and the security of the data is at risk while being transit on a network as well as at rest. The execution time of full disk encryption in large servers is significant because of the computational complexity associated with disk encryption. Hence it is necessary to reduce the execution time of full disk encryption from the application point of view. In this work a full disk encryption algorithm namely EME2 AES (Encrypt Mix Encrypt V2 Advanced Encryption Standard) is analyzed. The execution speed of this algorithm is reduced by means of multicore compatible parallel implementation which makes use of available cores. Parallel implementation is executed on a multicore machine with 8 cores and speed up on the multicore implementation is measured. Results show that the multicore implementation of EME2 AES using OpenMP is up to 2.85 times faster than sequential execution for the chosen infrastructure and data range.

Internet of Things (IoT) is an emerging trend that is changing the way devices connect and communicate. Integration of cloud computing with IoT i.e. Cloud of Things (CoT) provide scalability, virtualized control and access to the services provided by IoT. Security issues are a major obstacle in widespread deployment and application of CoT. Among these issues, authentication and identification of user is crucial. In this study paper, survey of various authentication schemes is carried out. The aim of this paper is to study a multifactor authentication system which uses secret splitting in detail. The system uses exclusive-or operations, encryption algorithms and Diffie-Hellman key exchange algorithm to share key over the network. Security analysis shows the resistance of the system against different types of attacks.

Embedded and mobile devices forming part of the Internet-of-Things (IoT) need new authentication technologies and techniques. This requirement is due to the increase in effort and time attackers will use to compromise a device, often remote, based on the possibility of a significant monetary return. This paper proposes exploiting a device's accelerometers in-built functionality to implement multi-factor authentication. An experimental embedded system designed to emulate a typical mobile device is used to implement the ideas and investigated as proof-of-concept.

Electro-hydraulic servo actuation system is a mechanical, electrical and hydraulic mixing complex system. If it can't be repaired for a long time, it is necessary to consider the possibility of occurrence of multiple faults. Considering this possibility, this paper presents an extended Kalman filter (EKF) based method for multiple faults diagnosis. Through analysing the failure modes and mechanism of the electro-hydraulic servo actuation system and modelling selected typical failure modes, the relationship between the key parameters of the system and the faults is obtained. The extended Kalman filter which is a commonly used algorithm for estimating parameters is used to on-line fault diagnosis. Then use the extended Kalman filter to diagnose potential faults. The simulation results show that the multi-fault diagnosis method based on extended Kalman filter is effective for multi-fault diagnosis of electro-hydraulic servo actuation system.

We outline an anomaly detection method for industrial control systems (ICS) that combines the analysis of network package contents that are transacted between ICS nodes and their time-series structure. Specifically, we take advantage of the predictable and regular nature of communication patterns that exist between so-called field devices in ICS networks. By observing a system for a period of time without the presence of anomalies we develop a base-line signature database for general packages. A Bloom filter is used to store the signature database which is then used for package content level anomaly detection. Furthermore, we approach time-series anomaly detection by proposing a stacked Long Short Term Memory (LSTM) network-based softmax classifier which learns to predict the most likely package signatures that are likely to occur given previously seen package traffic. Finally, by the inspection of a real dataset created from a gas pipeline SCADA system, we show that an anomaly detection scheme combining both approaches can achieve higher performance compared to various current state-of-the-art techniques.

The utilization of the online services especially the access to Internet Banking services has grown rapidly from last five years. The Internet Banking services provide the customers with the secure and reliable environment to deal with. But with the technology advancement, it is mandatory for the banks to put into practice the ideal technologies or the best security strategies and procedures to authorize or validate the originality of the customers. This must be done to ensure that the data or the information being transmitted during any kind of transaction is safe and no kind of leakage or modification of the information is possible for the intruder. This paper presents a digital watermark method for the QR Code (Quick Response Code) In this, a visible watermark is embedded in the QR Code image using the watermark technology (DCT) and describes the functioning feature of a secure authorization system by means of QR codes & the digital watermark for Internet Banking.

5G, the fifth generation of mobile communication networks, is considered as one of the main IoT enablers. Connecting billions of things, 5G/IoT will be dealing with trillions of GBytes of data. Securing such large amounts of data is a very challenging task. Collected data varies from simple temperature measurements to more critical transaction data. Thus, applying uniform security measures is a waste of resources (processing, memory, and network bandwidth). Alternatively, a multi-level security model needs to be applied according to the varying requirements. In this paper, we present a multi-level security scheme (BLP) applied originally in the information security domain. We review its application in the network domain, and propose a modified version of BLP for the 5G/IoT case. The proposed model is proven to be secure and compliant with the model rules.

Use of internet increases day by day so securing network and data is a big issue. So, it is very important to maintain security to ensure safe and trusted communication of information between different organizations. Because of these IDS is a very useful component of computer and network security. IDS system is used by many organizations or industries to detect the weakness in their security, documenting previous attacks and threats and preventing all of this from violating security policies. Because of these advantages, this system is important in system security. In this paper, we find a multilevel solution for different approaches (attacks) based on intrusion detection system. In this paper, we identify different attacks and find the solutions for different type of attacks such as DDOS, SQL injection and Brute force attack. In this case, we use client-server architecture. To implement this we maintain profile of user and base on this we find normal user or attacker when system find that attack is present then it directly block the attack.

A method for the multiple faults diagnosis in linear analog circuits is presented in this paper. The proposed approach is based upon the concept named by the indirect compensation theorem. This theorem is reducing the procedure of fault diagnosis in the analog circuit to the symbolic analysis process. An extension of the indirect compensation theorem for the linear subcircuit is proposed. The indirect compensation provides equivalent replacement of the n-ports subcircuit by n norators and n fixators of voltages and currents. The proposed multiple faults diagnosis techniques can be used for evaluation of any kind of terminal characteristics of the two-port network. For calculation of the circuit determinant expressions, the Generalized Parameter Extraction Method is implemented. The main advantage of the analysis method is that it is cancellation free. It requires neither matrix nor ordinary graph description of the circuit. The process of symbolic circuit analysis is automated by the freeware computer program Cirsym which can be used online. The experimental results are presented to show the efficiency and reliability of the proposed technique.

Multi-robot transfer learning allows a robot to use data generated by a second, similar robot to improve its own behavior. The potential advantages are reducing the time of training and the unavoidable risks that exist during the training phase. Transfer learning algorithms aim to find an optimal transfer map between different robots. In this paper, we investigate, through a theoretical study of single-input single-output (SISO) systems, the properties of such optimal transfer maps. We first show that the optimal transfer learning map is, in general, a dynamic system. The main contribution of the paper is to provide an algorithm for determining the properties of this optimal dynamic map including its order and regressors (i.e., the variables it depends on). The proposed algorithm does not require detailed knowledge of the robots' dynamics, but relies on basic system properties easily obtainable through simple experimental tests. We validate the proposed algorithm experimentally through an example of transfer learning between two different quadrotor platforms. Experimental results show that an optimal dynamic map, with correct properties obtained from our proposed algorithm, achieves 60-70% reduction of transfer learning error compared to the cases when the data is directly transferred or transferred using an optimal static map.

We study the problem of allocating limited security countermeasures to protect network data from cyber-attacks, for scenarios modeled by Bayesian attack graphs. We consider multi-stage interactions between a network administrator and cybercriminals, formulated as a security game. This formulation is capable of representing security environments with significant dynamics and uncertainty, and very large strategy spaces. For the game model, we propose parameterized heuristic strategies for both players. Our heuristics exploit the topological structure of the attack graphs and employ different sampling methodologies to overcome the computational complexity in determining players' actions. Given the complexity of the game, we employ a simulation-based methodology, and perform empirical game analysis over an enumerated set of these heuristic strategies. Finally, we conduct experiments based on a variety of game settings to demonstrate the advantages of our heuristics in obtaining effective defense strategies which are robust to the uncertainty of the security environment.

As a proposed Internet architecture, Named Data Networking must provide effective security support: data authenticity, confidentiality, and availability. This poster focuses on supporting data confidentiality via encryption. The main challenge is to provide an easy-to-use key management mechanism that ensures only authorized parties are given the access to protected data. We describe the design of name-based access control (NAC) which provides automated key management by developing systematic naming conventions for both data and cryptographic keys. We also discuss an enhanced version of NAC that leverages attribute-based encryption mechanisms (NAC-ABE) to improve the flexibility of data access control and reduce communication, storage, and processing overheads.

Configuring and maintaining an enterprise network is a challenging and error-prone process. Administrators must often consider security policies from a variety of sources simultaneously, including regulatory requirements, industry standards, and to mitigate attack vectors. Erroneous implementation of a policy, however, can result in costly data breaches and intrusions. Relying on humans to discover and troubleshoot violations is slow and prone to error, considering the speed at which new attack vectors propagate and the increasing network dynamics, partly an effect of SDN. To ensure the network is always in a state consistent with the desired policies, administrators need frameworks to automatically diagnose and repair violations in real-time. To address this problem, we present NEAt, a system analogous to a smartphone's autocorrect feature that enables on-the-fly repair to policy-violating updates. NEAt modifies the forwarding behavior of updates to automatically repair violations of properties such as reachability, service chaining, and segmentation. NEAt sits between an SDN controller and the forwarding devices, and intercepts updates proposed by SDN applications. If an update violates the policy defined by an administrator, such as reachability or segmentation, NEAt transforms the update into one that complies with the policy. Unlike domain-specific languages or synthesis platforms, NEAt allows enterprise networks to leverage the advanced functionality of SDN applications while simultaneously achieving strong, automated enforcement of general policies.