Biblio

Intrusion detection systems (IDSs) are an essential cog of the network security suite that can defend the network from malicious intrusions and anomalous traffic. Many machine learning (ML)-based IDSs have been proposed in the literature for the detection of malicious network traffic. However, recent works have shown that ML models are vulnerable to adversarial perturbations through which an adversary can cause IDSs to malfunction by introducing a small impracticable perturbation in the network traffic. In this paper, we propose an adversarial ML attack using generative adversarial networks (GANs) that can successfully evade an ML-based IDS. We also show that GANs can be used to inoculate the IDS and make it more robust to adversarial perturbations.

Wireless network sensors are outsized number of pocket sized sensors deployed in the area under surveillance. The sensor network is very sensitive to unattended and remote Environment with a wide variety of applications in the agriculture, health, industry there a lot of challenges being faced with respect to the energy, mobility, security. The paper presents with regard to the context based surrounding information which has location privacy to the source node against an adversary who sees the network at a whole so a correlation strategy is proposed for providing the privacy.

Wireless Sensor Networks (WSNs) are used in sensitive applications such as in asset monitoring applications. Due to the sensitivity of information in these applications, it is important to ensure that flow of data between sensor nodes is secure and does not expose any information about the source node or the monitored assets. This paper proposes a scheme to preserve the source location privacy based on random routing techniques. To achieve high privacy, the proposed scheme randomly sends packet to sink node through tactically positioned agent nodes. The position of agent nodes is designed to guarantee that successive packets are routed through highly random and perplexing routing paths as compared to other routing schemes. Simulation results demonstrate that proposed scheme provides longer safety period and higher privacy against both, patient and cautious adversaries.

Quantum information exchange computer emulator is presented, which takes into consideration imperfections of real quantum channel such as noise and attenuation resulting in the necessity to increase number of photons in the impulse. The Qt Creator C++ program package provides evaluation of the ability to detect unauthorized access as well as an amount of information intercepted by intruder.

6LoWPAN is a widely used protocol for communication over IPV6 Low-power Wireless Personal Area Networks. Unfortunately, the 6LoWPAN packet fragmentation mechanism possesses vulnerabilities that adversaries can exploit to perform network attacks. Lack of fragment authentication, payload integrity verification, and sender IP address validation lead to fabrication, duplication, and impersonation attacks. Moreover, adversaries can abuse the poor reassembly buffer management technique of the 6LoWPAN layer to perform buffer exhaustion and selective forwarding attacks. In this paper, we propose SecuPAN - a security scheme for mitigating fragmentation-based network attacks in 6LoWPAN networks and devices. We propose a Message Authentication Code based per-fragment integrity and authenticity verification scheme to defend against fabrication and duplication attacks. We also present a mechanism for computing datagram-tag and IPv6 address cryptographically to mitigate impersonation attacks. Additionally, our reputation-based buffer management scheme protects 6LoWPAN devices from buffer reservation attacks. We provide an extensive security analysis of SecuPAN to demonstrate that SecuPAN is secure against strong adversarial scenarios. We also implemented a prototype of SecuPAN on Contiki enabled IoT devices and provided a performance analysis of our proposed scheme.

The false data injection attack (FDIA) is a form of cyber-attack capable of affecting the secure and economic operation of the smart grid. With DC model-based state estimation, this paper analyzes ways of constructing a successful attacking vector to fulfill specific targets, i.e., pre-specified state variable target and pre-specified meter target according to the adversary's willingness. The grid operator's historical reading experiences on meters are considered as a constraint for the adversary to avoid being detected. Also from the viewpoint of the adversary, we propose to take full advantage of the dual concept of the coefficients in the topology matrix to handle with the problem that the adversary has no access to some meters. Effectiveness of the proposed method is validated by numerical experiments on the IEEE-14 benchmark system.

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.

The Internet of Things (IoT) devices perform security-critical operations and deal with sensitive information in the IoT-based systems. Therefore, the increased deployment of smart devices will make them targets for cyber attacks. Adversaries can perform malicious actions, leak private information, and track devices' and their owners' location by gaining unauthorized access to IoT devices and networks. However, conventional security protocols are not primarily designed for resource constrained devices and therefore cannot be applied directly to IoT systems. In this paper, we propose Boot-IoT - a privacy-preserving, lightweight, and scalable security scheme for limited resource devices. Boot-IoT prevents a malicious device from joining an IoT network. Boot-IoT enables a device to compute a unique identity for authentication each time the device enters a network. Moreover, during device to device communication, Boot-IoT provides a lightweight mutual authentication scheme that ensures privacy-preserving identity usages. We present a detailed analysis of the security strength of BootIoT. We implemented a prototype of Boot-IoT on IoT devices powered by Contiki OS and provided an extensive comparative analysis of Boot-IoT with contemporary authentication methods. Our results show that Boot-IoT is resource efficient and provides better scalability compared to current solutions.

We consider the problem of communicating information over a network secretly and reliably in the presence of a hidden adversary who can eavesdrop and inject malicious errors. We provide polynomial-time distributed network codes that are information-theoretically rate-optimal for this scenario, improving on the rates achievable in prior work by Ngai Our main contribution shows that as long as the sum of the number of links the adversary can jam (denoted by ZO) and the number of links he can eavesdrop on (denoted by ZI) is less than the network capacity (denoted by C) (i.e., ), our codes can communicate (with vanishingly small error probability) a single bit correctly and without leaking any information to the adversary. We then use this scheme as a module to design codes that allow communication at the source rate of C- ZO when there are no security requirements, and codes that allow communication at the source rate of C- ZO- ZI while keeping the communicated message provably secret from the adversary. Interior nodes are oblivious to the presence of adversaries and perform random linear network coding; only the source and destination need to be tweaked. We also prove that the rate-region obtained is information-theoretically optimal. In proving our results, we correct an error in prior work by a subset of the authors in this paper.

We consider the problem of communicating information over a network secretly and reliably in the presence of a hidden adversary who can eavesdrop and inject malicious errors. We provide polynomial-time distributed network codes that are information-theoretically rate-optimal for this scenario, improving on the rates achievable in prior work by Ngai Our main contribution shows that as long as the sum of the number of links the adversary can jam (denoted by ZO) and the number of links he can eavesdrop on (denoted by ZI) is less than the network capacity (denoted by C) (i.e., ), our codes can communicate (with vanishingly small error probability) a single bit correctly and without leaking any information to the adversary. We then use this scheme as a module to design codes that allow communication at the source rate of C- ZO when there are no security requirements, and codes that allow communication at the source rate of C- ZO- ZI while keeping the communicated message provably secret from the adversary. Interior nodes are oblivious to the presence of adversaries and perform random linear network coding; only the source and destination need to be tweaked. We also prove that the rate-region obtained is information-theoretically optimal. In proving our results, we correct an error in prior work by a subset of the authors in this paper.

A key question that arises in rigorous analysis of cyberphysical systems under attack involves establishing whether or not the attacked system deviates significantly from the ideal allowed behavior. This is the problem of deciding whether or not the ideal system is an abstraction of the attacked system. A quantitative variation of this question can capture how much the attacked system deviates from the ideal. Thus, algorithms for deciding abstraction relations can help measure the effect of attacks on cyberphysical systems and to develop attack detection strategies. In this paper, we present a decision procedure for proving that one nonlinear dynamical system is a quantitative abstraction of another. Directly computing the reach sets of these nonlinear systems are undecidable in general and reach set over-approximations do not give a direct way for proving abstraction. Our procedure uses (possibly inaccurate) numerical simulations and a model annotation to compute tight approximations of the observable behaviors of the system and then uses these approximations to decide on abstraction. We show that the procedure is sound and that it is guaranteed to terminate under reasonable robustness assumptions.