Biblio

The Software Assurance Metrics and Tool Evaluation (SAMATE) project at the National Institute of Standards and Technology (NIST) has created the Software Assurance Reference Dataset (SARD) to provide researchers and software security assurance tool developers with a set of known security flaws. As part of an empirical evaluation of a runtime monitoring framework, two test suites were executed and monitored, revealing deficiencies which led to a collaboration with the NIST SAMATE team to provide replacements. Test Suites 45 and 46 are analyzed, discussed, and updated to improve accuracy, consistency, preciseness, and automation. Empirical results show metrics such as recall, precision, and F-Measure are all impacted by invalid base assumptions regarding the test suites.

A two-factor authenticated key-agreement scheme for session initiation protocol emerged as a best remedy to overcome the ascribed limitations of the password-based authentication scheme. Recently, Lu et al. proposed an anonymous two-factor authenticated key-agreement scheme for SIP using elliptic curve cryptography. They claimed that their scheme is secure against attacks and achieves user anonymity. Conversely, this paper's keen analysis points out several severe security weaknesses of the Lu et al.'s scheme. In addition, this paper puts forward an enhanced anonymous two-factor mutual authenticated key-agreement scheme for session initiation protocol using elliptic curve cryptography. The security analysis and performance analysis sections demonstrates that the proposed scheme is more robust and efficient than Lu et al.'s scheme.

Recently, various certificate-less signature (CLS) schemes have been developed using bilinear pairing to provide authenticity of message. In 2015, Jia-Lun Tsai proposed a certificate-less pairing based short signature scheme using elliptic curve cryptography (ECC) and prove its security under random oracle. However, it is shown that the scheme is inappropriate for its practical use as there is no message-signature dependency present during signature generation and verification. Thus, the scheme is vulnerable. To overcome these attacks, this paper aims to present a variant of Jia-Lun Tsai's short signature scheme. Our scheme is secured under the hardness of collusion attack algorithm with k traitors (k–-CAA). The performance analysis demonstrates that proposed scheme is efficient than other related signature schemes.

Security patterns are generic solutions that can be applied since early stages of software life to overcome recurrent security weaknesses. Their generic nature and growing number make their choice difficult, even for experts in system design. To help them on the pattern choice, this paper proposes a semi-automatic methodology of classification and the classification itself, which exposes relationships among software weaknesses, security principles and security patterns. It expresses which patterns remove a given weakness with respect to the security principles that have to be addressed to fix the weakness. The methodology is based on seven steps, which anatomize patterns and weaknesses into set of more precise sub-properties that are associated through a hierarchical organization of security principles. These steps provide the detailed justifications of the resulting classification and allow its upgrade. Without loss of generality, this classification has been established for Web applications and covers 185 software weaknesses, 26 security patterns and 66 security principles. Research supported by the industrial chair on Digital Confidence (http://confiance-numerique.clermont-universite.fr/index-en.html).

Defending key network infrastructure, such as Internet backbone links or the communication channels of critical infrastructure, is paramount, yet challenging. The inherently complex nature and quantity of network data impedes detecting attacks in real world settings. In this paper, we utilize features of network flows, characterized by their entropy, together with an extended version of the original Replicator Neural Network (RNN) and deep learning techniques to learn models of normality. This combination allows us to apply anomaly-based intrusion detection on arbitrarily large amounts of data and, consequently, large networks. Our approach is unsupervised and requires no labeled data. It also accurately detects network-wide anomalies without presuming that the training data is completely free of attacks. The evaluation of our intrusion detection method, on top of real network data, indicates that it can accurately detect resource exhaustion attacks and network profiling techniques of varying intensities. The developed method is efficient because a normality model can be learned by training an RNN within a few seconds only.

Power system security is one of the key issues in the operation of smart grid system. Evaluation of power system security is a big challenge considering all the contingencies, due to huge computational efforts involved. Phasor measurement unit plays a vital role in real time power system monitoring and control. This paper presents static security assessment scheme for large scale inter connected power system with Phasor measurement unit using Artificial Neural Network. Voltage magnitude and phase angle are used as input variables of the ANN. The optimal location of PMU under base case and critical contingency cases are determined using Genetic algorithm. The performance of the proposed optimization model was tested with standard IEEE 30 bus system incorporating zero injection buses and successful results have been obtained.

Many malware families utilize domain generation algorithms (DGAs) to establish command and control (C&C) connections. While there are many methods to pseudorandomly generate domains, we focus in this paper on detecting (and generating) domains on a per-domain basis which provides a simple and flexible means to detect known DGA families. Recent machine learning approaches to DGA detection have been successful on fairly simplistic DGAs, many of which produce names of fixed length. However, models trained on limited datasets are somewhat blind to new DGA variants. In this paper, we leverage the concept of generative adversarial networks to construct a deep learning based DGA that is designed to intentionally bypass a deep learning based detector. In a series of adversarial rounds, the generator learns to generate domain names that are increasingly more difficult to detect. In turn, a detector model updates its parameters to compensate for the adversarially generated domains. We test the hypothesis of whether adversarially generated domains may be used to augment training sets in order to harden other machine learning models against yet-to-be-observed DGAs. We detail solutions to several challenges in training this character-based generative adversarial network. In particular, our deep learning architecture begins as a domain name auto-encoder (encoder + decoder) trained on domains in the Alexa one million. Then the encoder and decoder are reassembled competitively in a generative adversarial network (detector + generator), with novel neural architectures and training strategies to improve convergence.

The traditional text classification methods usually follow this process: first, a sentence can be considered as a bag of words (BOW), then transformed into sentence feature vector which can be classified by some methods, such as maximum entropy (ME), Naive Bayes (NB), support vector machines (SVM), and so on. However, when these methods are applied to text classification, we usually can not obtain an ideal result. The most important reason is that the semantic relations between words is very important for text categorization, however, the traditional method can not capture it. Sentiment classification, as a special case of text classification, is binary classification (positive or negative). Inspired by the sentiment analysis, we use a novel deep learning-based recurrent neural networks (RNNs)model for automatic security audit of short messages from prisons, which can classify short messages(secure and non-insecure). In this paper, the feature of short messages is extracted by word2vec which captures word order information, and each sentence is mapped to a feature vector. In particular, words with similar meaning are mapped to a similar position in the vector space, and then classified by RNNs. RNNs are now widely used and the network structure of RNNs determines that it can easily process the sequence data. We preprocess short messages, extract typical features from existing security and non-security short messages via word2vec, and classify short messages through RNNs which accept a fixed-sized vector as input and produce a fixed-sized vector as output. The experimental results show that the RNNs model achieves an average 92.7% accuracy which is higher than SVM.

Software defined networking promises network operators to dramatically simplify network management. It provides flexibility and innovation through network programmability. With SDN, network management moves from codifying functionality in terms of low-level device configuration to building software that facilitates network management and debugging[1]. SDN provides new techniques to solve long-standing problems in networking like routing by separating the complexity of state distribution from network specification. Despite all the hype surrounding SDNs, exploiting its full potential is demanding. Security is still the major issue and a striking challenge that reduces the growth of SDNs. Moreover the introduction of various architectural components and up cycling of novel entities of SDN poses new security issues and threats. SDN is considered as major target for digital threats and cyber-attacks[2] and have more devastating effects than simple networks. Initial SDN design doesn't considered security as its part; therefore, it must be raised on the agenda. This article discusses the security solutions proposed to secure SDNs. We categorize the security solutions in the article by presenting a thematic taxonomy based on SDN architectural layers/interfaces[3], security measures and goals, simulation framework. Moreover, the literature also points out the possible attacks[2] targeting different layers/interfaces of SDNs. For securing SDNs, the potential requirements and their key enablers are also identified and presented. Also, the articles sketch the design of secure and dependable SDNs. At last, we discuss open issues and challenges of SDN security that may be rated appropriate to be handled by professionals and researchers in the future.

Modern smart surveillance systems can not only record the monitored environment but also identify the targeted objects and detect anomaly activities. These advanced functions are often facilitated by deep neural networks, achieving very high accuracy and large data processing throughput. However, inappropriate design of the neural network may expose such smart systems to the risks of leaking the target being searched or even the adopted learning model itself to attackers. In this talk, we will present the security challenges in the design of smart surveillance systems. We will also discuss some possible solutions that leverage the unique properties of emerging nano-devices, including the incurred design and performance cost and optimization methods for minimizing these overheads.

Coming days are becoming a much challenging task for the power system researchers due to the anomalous increase in the load demand with the existing system. As a result there exists a discordant between the transmission and generation framework which is severely pressurizing the power utilities. In this paper a quick and efficient methodology has been proposed to identify the most sensitive or susceptible regions in any power system network. The technique used in this paper comprises of correlation of a multi-bus power system network to an equivalent two-bus network along with the application of Artificial neural network(ANN) Architecture with training algorithm for online monitoring of voltage security of the system under all multiple exigencies which makes it more flexible. A fast voltage stability indicator has been proposed known as Unified Voltage Stability Indicator (UVSI) which is used as a substratal apparatus for the assessment of the voltage collapse point in a IEEE 30-bus power system in combination with the Feed Forward Neural Network (FFNN) to establish the accuracy of the status of the system for different contingency configurations.

In view of the high demand for the security of visiting data in power system, a network data security analysis method based on DPI technology was put forward in this paper, to solve the problem of security gateway judge the legality of the network data. Considering the legitimacy of the data involves data protocol and data contents, this article will filters the data from protocol matching and content detection. Using deep packet inspection (DPI) technology to screen the protocol. Using protocol analysis to detect the contents of data. This paper implements the function that allowing secure data through the gateway and blocking threat data. The example proves that the method is more effective guarantee the safety of visiting data.

With the popularization and development of network knowledge, network intruders are increasing, and the attack mode has been updated. Intrusion detection technology is a kind of active defense technology, which can extract the key information from the network system, and quickly judge and protect the internal or external network intrusion. Intrusion detection is a kind of active security technology, which provides real-time protection for internal attacks, external attacks and misuse, and it plays an important role in ensuring network security. However, with the diversification of intrusion technology, the traditional intrusion detection system cannot meet the requirements of the current network security. Therefore, the implementation of intrusion detection needs diversifying. In this context, we apply neural network technology to the network intrusion detection system to solve the problem. In this paper, on the basis of intrusion detection method, we analyze the development history and the present situation of intrusion detection technology, and summarize the intrusion detection system overview and architecture. The neural network intrusion detection is divided into data acquisition, data analysis, pretreatment, intrusion behavior detection and testing.

This paper considers the physical layer security for the cluster-based cooperative wireless sensor networks (WSNs), where each node is equipped with a single antenna and sensor nodes cooperate at each cluster of the network to form a virtual multi-input multi-output (MIMO) communication architecture. We propose a joint cooperative beamforming and jamming scheme to enhance the security of the WSNs where a part of sensor nodes in Alice's cluster are deployed to transmit beamforming signals to Bob while a part of sensor nodes in Bob's cluster are utilized to jam Eve with artificial noise. The optimization of beamforming and jamming vectors to minimize total energy consumption satisfying the quality-of-service (QoS) constraints is a NP-hard problem. Fortunately, through reformulation, the problem is proved to be a quadratically constrained quadratic problem (QCQP) which can be solved by solving constraint integer programs (SCIP) algorithm. Finally, we give the simulation results of our proposed scheme.

Complex traffic networks include a number of controlled intersections, and, commonly, multiple districts or municipalities. The result is that the overall traffic control problem is extremely complex computationally. Moreover, given that different municipalities may have distinct, non-aligned, interests, traffic light controller design is inherently decentralized, a consideration that is almost entirely absent from related literature. Both complexity and decentralization have great bearing both on the quality of the traffic network overall, as well as on its security. We consider both of these issues in a dynamic traffic network. First, we propose an effective local search algorithm to efficiently design system-wide control logic for a collection of intersections. Second, we propose a game theoretic (Stackelberg game) model of traffic network security in which an attacker can deploy denial-of-service attacks on sensors, and develop a resilient control algorithm to mitigate such threats. Finally, we propose a game theoretic model of decentralization, and investigate this model both in the context of baseline traffic network design, as well as resilient design accounting for attacks. Our methods are implemented and evaluated using a simple traffic network scenario in SUMO.

Emergency message delivery in packet networks is promising in terms of resiliency to failures and service delivery to handicapped persons. In this paper, we propose an NDN(Named Data Networking)-based emergency message delivery mechanism by leveraging multicasting and ABE (Attribute-Based Encryption) functions.

Bulk electric systems include hundreds of synchronous generators. Faults in such systems can induce oscillations in the generators which if not detected and controlled can destabilize the system. Mode estimation is a popular method for oscillation detection. In this paper, we propose a resilient algorithm to estimate electro-mechanical oscillation modes in large scale power system in the presence of false data. In particular, we add a fault tolerance mechanism to a variant of alternating direction method of multipliers (ADMM) called S-ADMM. We evaluate our method on an IEEE 68-bus test system under different attack scenarios and show that in all the scenarios our algorithm converges well.

The prevalent integration of highly intermittent renewable distributed energy resources (DER) into microgrids necessitates the deployment of a microgrid controller. In the absence of the main electric grid setting the network voltage and frequency, the microgrid power and energy management becomes more challenging, accentuating the need for a centralized microgrid controller that, through communication links, ensures smooth operation of the autonomous system. This extensive reliance on information and communication technologies (ICT) creates potential access points and vulnerabilities that may be exploited by cyber-attackers. This paper first presents a typical microgrid configuration operating in islanded mode; the microgrid elements, primary and secondary control functions for power, energy and load management are defined. The information transferred from the central controller to coordinate and dispatch the DERs is provided along with the deployable communication technologies and protocols. The vulnerabilities arising in such microgrids along with the cyber-attacks exploiting them are described. The impact of these attacks on the microgrid controller functions was shown to be dependent on the characteristics, location and target of the cyber-attack, as well as the microgrid configuration and control. A real-time hardware-in-the loop (HIL) testing platform, which emulates a microgrid featuring renewable DERs, an energy storage system (ESS), a diesel generator and controllable loads was used as the case study in order to demonstrate the impact of various cyber-attacks.

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

Riding on the success of SDN for enterprise and data center networks, recently researchers have shown much interest in applying SDN for critical infrastructures. A key concern, however, is the vulnerability of the SDN controller as a single point of failure. In this paper, we develop a cyber-physical simulation platform that interconnects Mininet (an SDN emulator), hardware SDN switches, and PowerWorld (a high-fidelity, industry-strength power grid simulator). We report initial experiments on how a number of representative controller faults may impact the delay of smart grid communications. We further evaluate how this delay may affect the performance of the underlying physical system, namely automatic gain control (AGC) as a fundamental closed-loop control that regulates the grid frequency to a critical nominal value. Our results show that when the fault-induced delay reaches seconds (e.g., more than four seconds in some of our experiments), degradation of the AGC becomes evident. Particularly, the AGC is most vulnerable when it is in a transient following say step changes in loading, because the significant state fluctuations will exacerbate the effects of using a stale system state in the control.

Software defined networking (SDN) is an emerging technology for controlling flows through networks. Used in the context of industrial control systems, an objective is to design configurations that have built-in protection for hardware failures in the sense that the configuration has "baked-in" back-up routes. The objective is to leave the configuration static as long as possible, minimizing the need to have the controller push in new routing and filtering rules We have designed and implemented a tool that enables us to determine the complete connectivity map from an analysis of all switch configurations in the network. We can use this tool to explore the impact of a link failure, in particular to determine whether the failure induces loss of the ability to deliver a flow even after the built-in back-up routes are used. A measure of the original configuration's resilience to link failure is the mean number of link failures required to induce the first such loss of service. The computational cost of each link failure and subsequent analysis is large, so there is much to be gained by reducing the overall cost of obtaining a statistically valid estimate of resiliency. This paper shows that when analysis of a network state can identify all as-yet-unfailed links any one of whose failure would induce loss of a flow, then we can use the technique of importance sampling to estimate the mean number of links required to fail before some flow is lost, and analyze the potential for reducing the variance of the sample statistic. We provide both theoretical and empirical evidence for significant variance reduction.

Real world applications of Wireless Sensor Networks such as border control, healthcare monitoring and target tracking require secure communications. Thus, during WSN setup, one of the first requirements is to distribute the keys to the sensor nodes which can be later used for securing the messages exchanged between sensors. The key management schemes in WSN secure the communication between a pair or a group of nodes. However, the storage capacity of the sensor nodes is limited which makes storage requirement as an important parameter for the evaluation of key management schemes. This paper classifies the existing key management schemes proposed for WSNs into three categories: storage inefficient, storage efficient and highly storage efficient key management schemes.

This paper presents a contextual anomaly detection method and its use in the discovery of malicious voltage control actions in the low voltage distribution grid. The model-based anomaly detection uses an artificial neural network model to identify a distributed energy resource's behaviour under control. An intrusion detection system observes distributed energy resource's behaviour, control actions and the power system impact, and is tested together with an ongoing voltage control attack in a co-simulation set-up. The simulation results obtained with a real photovoltaic rooftop power plant data show that the contextual anomaly detection performs on average 55% better in the control detection and over 56% better in the malicious control detection over the point anomaly detection.

The successful operations of modern power grids are highly dependent on a reliable and ecient underlying communication network. Researchers and utilities have started to explore the opportunities and challenges of applying the emerging software-de ned networking (SDN) technology to enhance eciency and resilience of the Smart Grid. This trend calls for a simulation-based platform that provides sufcient exibility and controllability for evaluating network application designs, and facilitating the transitions from inhouse research ideas to real productions. In this paper, we present DSSnet, a hybrid testing platform that combines a power distribution system simulator with an SDN emulator to support high delity analysis of communication network applications and their impacts on the power systems. Our contributions lay in the design of a virtual time system with the tight controllability on the execution of the emulation system, i.e., pausing and resuming any speci ed container processes in the perception of their own virtual clocks, with little overhead scaling to 500 emulated hosts with an average of 70 ms overhead; and also lay in the ecient synchronization of the two sub-systems based on the virtual time. We evaluate the system performance of DSSnet, and also demonstrate the usability through a case study by evaluating a load shifting algorithm.