Biblio

Integer errors in C/C++ are caused by arithmetic operations yielding results which are unrepresentable in certain type. They can lead to serious safety and security issues. Due to the complicated semantics of C/C++ integers, integer errors are widely harbored in real-world programs and it is error-prone to repair them even for experts. An automatic tool is desired to 1) automatically generate fixes which assist developers to correct the buggy code, and 2) provide sufficient hints to help developers review the generated fixes and better understand integer types in C/C++. In this paper, we present a tool IntPTI that implements the desired functionalities for C programs. IntPTI infers appropriate types for variables and expressions to eliminate representation issues, and then utilizes the derived types with fix patterns codified from the successful human-written patches. IntPTI provides a user-friendly web interface which allows users to review and manage the fixes. We evaluate IntPTI on 7 real-world projects and the results show its competitive repair accuracy and its scalability on large code bases. The demo video for IntPTI is available at: https://youtu.be/9Tgd4A\_FgZM.

Internet of Things (IoT) is characterized by heterogeneous devices that interact with each other on a collaborative basis to fulfill a common goal. In this scenario, some of the deployed devices are expected to be constrained in terms of memory usage, power consumption and processing resources. To address the specific properties and constraints of such networks, a complete stack of standardized protocols has been developed, among them the Routing Protocol for Low-Power and lossy networks (RPL). However, this protocol is exposed to a large variety of attacks from the inside of the network itself. To fill this gap, this paper focuses on the design and the integration of a novel Link reliable and Trust aware model into the RPL protocol. Our approach aims to ensure Trust among entities and to provide QoS guarantees during the construction and the maintenance of the network routing topology. Our model targets both node and link Trust and follows a multidimensional approach to enable an accurate Trust value computation for IoT entities. To prove the efficiency of our proposal, this last has been implemented and tested successfully within an IoT environment. Therefore, a set of experiments has been made to show the high accuracy level of our system.

The online portion of modern life is growing at an astonishing rate, with the consequence that more of the user's critical information is stored online. This poses an immediate threat to privacy and security of the user's data. This work will cover the increasing dangers and security risks of adware, adware injection, and malware injection. These programs increase in direct proportion to the number of users on the Internet. Each of these programs presents an imminent threat to a user's privacy and sensitive information, anytime they utilize the Internet. We will discuss how current ad blockers are not the actual solution to these threats, but rather a premise to our work. Current ad blocking tools can be discovered by the web servers which often requires suppression of the ad blocking tool. Suppressing the tool creates vulnerabilities in a user's system, but even when the tool is active their system is still susceptible to peril. It is possible, even when an ad blocking tool is functioning, for it to allow adware content through. Our solution to the contemporary threats is our tool, MalFire.

Vulnerability analysis is important procedure for a cyber security evaluation process. There are two types of vulnerability analysis, which is an interview for the facility manager and a vulnerability scanning with a software tool. It is difficult to use the vulnerability scanning tool on an operating nuclear plant control system because of the possibility of giving adverse effects to the system. The purpose of this paper is to suggest a method of cyber security vulnerability test using the DPPS and PMAS test-bed. Based on functions of the test-bed, possible threats and vulnerabilities in terms of cyber security were analyzed. Attack trees and test scenarios could be established with the consideration of attack vectors. It is expected that this method can be helpful to implement adequate security controls and verify whether the security controls make adverse impact to the inherent functions of the systems.

Securing Internet of Things is a challenge because of its multiple points of vulnerability. In particular, Distributed Denial of Service (DDoS) attacks on IoT devices pose a major security challenge to be addressed. In this paper, we propose a DNS query-based DDoS attack mitigation system using Software-Defined Networking (SDN) to block the network traffic for DDoS attacks. With some features provided by SDN, we can analyze traffic patterns and filter suspicious network flows out. To show the feasibility of the proposed system, we particularly implemented a prototype with Dirichlet process mixture model to distinguish benign traffic from malicious traffic and conducted experiments with the dataset collected from real network traces. We demonstrate the effectiveness of the proposed method by both simulations and experiment data obtained from the real network traffic traces.

In the article, it is proposed to improve the formulas for computing metrics of RIP, OSPF and EIGRP routing protocols by introducing an information security risk indicator of the route. This approach will allow to choose the best route in terms of Quality of Service (QoS) indicators and information security (confidentiality, integrity and availability of transmitted information).

In the article, it is proposed to improve the formulas for computing metrics of RIP, OSPF and EIGRP routing protocols by introducing an information security risk indicator of the route. This approach will allow to choose the best route in terms of Quality of Service (QoS) indicators and information security (confidentiality, integrity and availability of transmitted information).

Software Defined Networking (SDN) has proved to be a promising approach for creating next generation software based network ecosystems. It has provided us with a centralized network provision, a holistic management plane and a well-defined level of abstraction. But, at the same time brings forth new security and management challenges. Research in the field of SDN is primarily focused on reconfiguration, forwarding and network management issues. However in recent times the interest has moved to tackling security and maintenance issues. This work is based on providing a means to mitigate security challenges in an SDN environment from a DDoS attack based point of view. This paper introduces a Multi-Agent based intrusion prevention and mitigation architecture for SDN. Thus allowing networks to govern their behavior and take appropriate measures when the network is under attack. The architecture is evaluated against filter based intrusion prevention architectures to measure efficiency and resilience against DDoS attacks and false policy based attacks.

Named Data Networking (NDN) is a future Internet architecture, NDN forwarding strategy is a hot research topic in MANET. At present, there are two categories of forwarding strategies in NDN. One is the blind forwarding(BF), the other is the aware forwarding(AF). Data packet return by the way that one came forwarding strategy(DRF) as one of the BF strategy may fail for the interruptions of the path that are caused by the mobility of nodes. Consumer need to wait until the interest packet times out to request the data packet again. To solve the insufficient of DRF, in this paper a Forwarding Strategy, called FN based on Neighbor-aware is proposed for NDN MANET. The node maintains the neighbor information and the request information of neighbor nodes. In the phase of data packet response, in order to improve request satisfaction rate, node specifies the next hop node; Meanwhile, in order to reduce packet loss rate, node assists the last hop node to forward packet to the specific node. The simulation results show that compared with DRF and greedy forwarding(GF) strategy, FN can improve request satisfaction rate when node density is high.

Software-defined networking (SDN) is enabling radically easier deployment of new routing infrastructures in enterprise and operator networks. However, it is not clear how to best exploit this flexibility, when also considering the migration costs. In this paper, we use tools from network economics to study a recent proposal of using information-centric networking (ICN) principles on an SDN infrastructure for improving the delivery of Internet Protocol (IP) services. The key value proposition of this IP-over-ICN approach is to use the native and lightweight multicast service delivery enabled by the ICN technology to reduce network load by removing redundant data. Our analysis shows that for services where IP multicast delivery is technically feasible, IP-over-ICN deployments are economically sensible if only few users will access the given service simultaneously. However, for services where native IP multicast is not a technically feasible option, such as for dynamically generated or personalized content, IP-over-ICN significantly outperforms IP.

We present a neural network technique for the analysis and extrapolation of time-series data called Neural Decomposition (ND). Units with a sinusoidal activation function are used to perform a Fourier-like decomposition of training samples into a sum of sinusoids, augmented by units with nonperiodic activation functions to capture linear trends and other nonperiodic components. We show how careful weight initialization can be combined with regularization to form a simple model that generalizes well. Our method generalizes effectively on the Mackey-Glass series, a dataset of unemployment rates as reported by the U.S. Department of Labor Statistics, a time-series of monthly international airline passengers, and an unevenly sampled time-series of oxygen isotope measurements from a cave in north India. We find that ND outperforms popular time-series forecasting techniques including LSTM, echo state networks, (S)ARIMA, and SVR with a radial basis function.

Our project, NFC Unlock, implements a secure multifactor authentication system for computers using Near Field Communication technology. The application is written in C\# with pGina. It implements an NFC authentication which replaces the standard Windows credentials to allow the use of an NFC tag and a passcode to authenticate the user. Unlike the most prevalent multifactor authentication methods, NFC authentication does not require a user wait for an SMS code to type into the computer. A user enters a passcode and scans the NFC tag to log in. In order to prevent the data from being hacked, the system encrypts the NFC tag ID and the passcode with Advanced Encryption Standard. Users can easily register an NFC tag and link it to their computer account. The program also has several extra features including text alerts, record keeping of all login and login attempts, and a user-friendly configuration menu. Initial tests show that the NFC-based multifactor authentication system has the advantage of improved security with a simplified login process.

This paper introduces a hardware Trojan detection method using Chip ID which is generated by Relative Time-Delays (RTD) of sensor chains and the effectiveness of RTD is verified by post-layout simulations. The rank of time-delays of the sensor chains would be changed in Trojan-inserted chip. RTD is an accurate approach targeting to all kinds of Trojans, since it is based on the RELATIVE relationship between the time-delays rather than the absolute values, which are hard to be measured and will change with the fabricate process. RTD needs no golden chip, because the RELATIVE values would not change in most situations. Thus the genuine ID can be generated by simulator. The sensor chains can be inserted into a layout utilizing unused spaces, so RTD is a low-cost solution. A Trojan with 4x minimum NMOS is placed in different places of the chip. The behavior of the chip is obtained by using transient based post-layout simulation. All the Trojans are detected AND located, thus the effectiveness of RTD is verified.

Named Data Networking (NDN) is a new network architecture design that led to the evolution of a network architecture based on data-centric. Questions have been raised about how to compare its performance with the old architecture such as IP network which is generally based on Internet Protocol version 4 (IPv4). Differs with the old one, source and destination addresses in the delivery of data are not required on the NDN network because the addresses function is replaced by a data name (Name) which serves to identify the data uniquely. In a computer network, a network routing is an essential factor to support data communication. The network routing on IP network relies only on Routing Information Base (RIB) derived from the IP table on the router. So that, if there is a problem on the network such as there is one node exposed to a dangerous attack, the IP router should wait until the IP table is updated, and then the routing channel is changed. The issue of how to change the routing path without updating IP table has received considerable critical attention. The NDN network has an advantage such as its capability to execute an adaptive forwarding mechanism, which FIB (Forwarding Information Base) of the NDN router keeps information for routing and forwarding planes. Therefore, if there is a problem on the network, the NDN router can detect the problem more quickly than the IP router. The contribution of this study is important to explain the benefit of the forwarding mechanism of the NDN network compared to the IP network forwarding mechanism when there is a node which is suffered a hijack attack.

This paper investigates closed-form expressions to evaluate the performance of the Compressive Sensing (CS) based Energy Detector (ED). The conventional way to approximate the probability density function of the ED test statistic invokes the central limit theorem and considers the decision variable as Gaussian. This approach, however, provides good approximation only if the number of samples is large enough. This is not usually the case in CS framework, where the goal is to keep the sample size low. Moreover, working with a reduced number of measurements is of practical interest for general spectrum sensing in cognitive radio applications, where the sensing time should be sufficiently short since any time spent for sensing cannot be used for data transmission on the detected idle channels. In this paper, we make use of low-complexity approximations based on algebraic transformations of the one-dimensional Gaussian Q-function. More precisely, this paper provides new closed-form expressions for accurate evaluation of the CS-based ED performance as a function of the compressive ratio and the Signal-to-Noise Ratio (SNR). Simulation results demonstrate the increased accuracy of the proposed equations compared to existing works.

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.

The cyclostationary characteristics of signals has some important applications in such as blind channel equalization, blind adaptive beamforming, and system identification. However, the cyclostationary characteristics also can be a weak link in physical layer security. With high-order cyclostationary theory, some system information can be obtained easily. In this paper, we proposed a new algorithm based on constellation phase rotation and amplitude randomization, during which the cyclostationary feature of signals can be suppressed.

Delay and security are both highly concerned in the Internet of Things (IoT). In this paper, we set up a secure analytical framework for IoT networks to characterize the network delay performance and secrecy performance. Firstly, stochastic geometry and queueing theory are adopted to model the location of IoT devices and the temporal arrival of packets. Based on this model, a low-complexity secure on-off scheme is proposed to improve the network performance. Then, the delay performance and secrecy performance are evaluated in terms of packet delay and packet secrecy outage probability. It is demonstrated that the intensity of IoT devices arouse a tradeoff between the delay and security and the secure on-off scheme can improve the network delay performance and secrecy performance. Moreover, secrecy transmission rate is adopted to reflect the delay-security tradeoff. The analytical and simulation results show the effects of intensity of IoT devices and secure on-off scheme on the network delay performance and secrecy performance.

In cognitive radio network, the primary user (PU) network and the secondary user (SU) network interfered with each other because of sharing the spectral resource. Also interference among multi-downlinks in SU network decreased the sum rate in SU network and the eavesdropper in PU network decreased the secrecy rate in PU network. Focusing on above problem, this paper raised two channel selection and beamforming methods based on singular value decomposition (SVD) and uplink-downlink duality respectively, and then analyzed the performance of them in physical layer security.

Deep learning techniques have demonstrated the ability to perform a variety of object recognition tasks using visible imager data; however, deep learning has not been implemented as a means to autonomously detect and assess targets of interest in a physical security system. We demonstrate the use of transfer learning on a convolutional neural network (CNN) to significantly reduce training time while keeping detection accuracy of physical security relevant targets high. Unlike many detection algorithms employed by video analytics within physical security systems, this method does not rely on temporal data to construct a background scene; targets of interest can halt motion indefinitely and still be detected by the implemented CNN. A key advantage of using deep learning is the ability for a network to improve over time. Periodic retraining can lead to better detection and higher confidence rates. We investigate training data size versus CNN test accuracy using physical security video data. Due to the large number of visible imagers, significant volume of data collected daily, and currently deployed human in the loop ground truth data, physical security systems present a unique environment that is well suited for analysis via CNNs. This could lead to the creation of algorithmic element that reduces human burden and decreases human analyzed nuisance alarms.

One of the challenges of malware defense is that the attacker has the advantage over the defender. In many cases, an attack is successful and causes damage before the defender can even begin to prepare a defense. The ability to anticipate attacks and prepare defenses before they occur would be a significant scientific and technological development with practical applications in cybersecurity. In this paper, we present a method to augment machine learning-based malware detection systems by predicting signatures of future malware variants and injecting these variants into the defensive system as a vaccine. Our method uses deep learning to learn patterns of malware evolution from family histories. These evolution patterns are then used to predict future family developments. Our experiments show that a detection system augmented with these future malware signatures is able to detect future malware variants that could not be detected by the detection system alone. In particular, it detected 11 new malware variants without increasing false positives, while providing up to 5 months of lead time between prediction and attack.

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.

It is a challenging problem to preserve the friendly-correlations between individuals when publishing social-network data. To alleviate this problem, uncertain graph has been presented recently. The main idea of uncertain graph is converting an original graph into an uncertain form, where the correlations between individuals is an associated probability. However, the existing methods of uncertain graph lack rigorous guarantees of privacy and rely on the assumption of adversary's knowledge. In this paper we first introduced a general model for constructing uncertain graphs. Then, we proposed an algorithm under the model which is based on differential privacy and made an analysis of algorithm's privacy. Our algorithm provides rigorous guarantees of privacy and against the background knowledge attack. Finally, the algorithm we proposed satisfied differential privacy and showed feasibility in the experiments. And then, we compare our algorithm with (k, ε)-obfuscation algorithm in terms of data utility, the importance of nodes for network in our algorithm is similar to (k, ε)-obfuscation algorithm.