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L, Nirmala Devi, K, Venkata Subbareddy.  2019.  Secure and Composite Routing Strategy through Clustering In WSN. 2019 2nd International Conference on Innovations in Electronics, Signal Processing and Communication (IESC). :119–123.

Due to openness of the deployed environment and transmission medium, Wireless Sensor Networks (WSNs) suffers from various types of security attacks including Denial of service, Sinkhole, Tampering etc. Securing WSN is achieved a greater research interest and this paper proposes a new secure routing strategy for WSNs based on trust model. In this model, initially the sensor nodes of the network are formulated as clusters. Further a trust evaluation mechanism was accomplished for every sensor node at Cluster Head level to build a secure route for data transmission from sensor node to base station. Here the trust evaluation is carried out only at cluster head and also the cluster head is chosen in such a way the node having rich resources availability. The trust evaluation is a composition of the social trust and data trust. Simulation experiments are conducted over the proposed approach and the performance is measured through the performance metrics such as network lifetime, and Malicious Detection Rate. The obtained performance metrics shows the outstanding performance of proposed approach even in the increased malicious behavior of network.

L. Huiying, X. Caiyun, K. Jun, D. Ying.  2015.  "A Novel Secure Arithmetic Image Coding Algorithm Based on Two-Dimensional Generalized Logistic Mapping". 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC). :671-674.

A novel secure arithmetic image coding algorithm based on Two-dimensional Generalized Logistic Mapping is proposed. Firstly, according to the digital image size m×n, two 2D chaotic sequences are generated by logistic chaotic mapping. Then, the original image data is scrambled by sorting the chaotic sequence. Secondly, the chaotic sequence is optimized to generate key stream which is used to mask the image data. Finally, to generate the final output, the coding interval order is controlled by the chaotic sequence during the arithmetic coding process. Experiment results show the proposed secure algorithm has good robustness and can be applied in the arithmetic coder for multimedia such as video and audio with little loss of coding efficiency.

L. Rivière, J. Bringer, T. H. Le, H. Chabanne.  2015.  "A novel simulation approach for fault injection resistance evaluation on smart cards". 2015 IEEE Eighth International Conference on Software Testing, Verification and Validation Workshops (ICSTW). :1-8.

Physical perturbations are performed against embedded systems that can contain valuable data. Such devices and in particular smart cards are targeted because potential attackers hold them. The embedded system security must hold against intentional hardware failures that can result in software errors. In a malicious purpose, an attacker could exploit such errors to find out secret data or disrupt a transaction. Simulation techniques help to point out fault injection vulnerabilities and come at an early stage in the development process. This paper proposes a generic fault injection simulation tool that has the particularity to embed the injection mechanism into the smart card source code. By its embedded nature, the Embedded Fault Simulator (EFS) allows us to perform fault injection simulations and side-channel analyses simultaneously. It makes it possible to achieve combined attacks, multiple fault attacks and to perform backward analyses. We appraise our approach on real, modern and complex smart card systems under data and control flow fault models. We illustrate the EFS capacities by performing a practical combined attack on an Advanced Encryption Standard (AES) implementation.

L. Thiele, M. Kurras, S. Jaeckel, S. Fähse, W. Zirwas.  2015.  "Interference-floor shaping for liquid coverage zones in coordinated 5G networks". 2015 49th Asilomar Conference on Signals, Systems and Computers. :1102-1106.

Joint transmission coordinated multi-point (CoMP) is a combination of constructive and destructive superposition of several to potentially many signal components, with the goal to maximize the desired receive-signal and at the same time to minimize mutual interference. Especially the destructive superposition requires accurate alignment of phases and amplitudes. Therefore, a 5G clean slate approach needs to incorporate the following enablers to overcome the challenging limitation for JT CoMP: accurate channel estimation of all relevant channel components, channel prediction for time-aligned precoder design, proper setup of cooperation areas corresponding to user grouping and to limit feedback overhead especially in FDD as well as treatment of out-of-cluster interference (interference floor shaping).

Laaboudi, Younes, Olivereau, Alexis, Oualha, Nouha.  2019.  An Intrusion Detection and Response Scheme for CP-ABE-Encrypted IoT Networks. 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS). :1–5.

This paper introduces a new method of applying both an Intrusion Detection System (IDS) and an Intrusion Response System (IRS) to communications protected using Ciphertext-Policy Attribute-based Encryption (CP-ABE) in the context of the Internet of Things. This method leverages features specific to CP-ABE in order to improve the detection capabilities of the IDS and the response ability of the network. It also enables improved privacy towards the users through group encryption rather than one-to-one shared key encryption as the policies used in the CP-ABE can easily include the IDS as an authorized reader. More importantly, it enables different levels of detection and response to intrusions, which can be crucial when using anomaly-based detection engines.

Lacerda, A., Rodrigues, J., Macedo, J., Albuquerque, E..  2017.  Deployment and analysis of honeypots sensors as a paradigm to improve security on systems. 2017 Internet Technologies and Applications (ITA). :64–68.
This article is about study of honeypots. In this work, we use some honeypot sensors deployment and analysis to identify, currently, what are the main attacks and security breaches explored by attackers to compromise systems. For example, a common server or service exposed to the Internet can receive a million of hits per day, but sometimes would not be easy to identify the difference between legitimate access and an attacker trying to scan, and then, interrupt the service. Finally, the objective of this research is to investigate the efficiency of the honeypots sensors to identify possible safety gaps and new ways of attacks. This research aims to propose some guidelines to avoid or minimize the damage caused by these attacks in real systems.
Lachner, Clemens, Rausch, Thomas, Dustdar, Schahram.  2019.  Context-Aware Enforcement of Privacy Policies in Edge Computing. 2019 IEEE International Congress on Big Data (BigDataCongress). :1—6.
Privacy is a fundamental concern that confronts systems dealing with sensitive data. The lack of robust solutions for defining and enforcing privacy measures continues to hinder the general acceptance and adoption of these systems. Edge computing has been recognized as a key enabler for privacy enhanced applications, and has opened new opportunities. In this paper, we propose a novel privacy model based on context-aware edge computing. Our model leverages the context of data to make decisions about how these data need to be processed and managed to achieve privacy. Based on a scenario from the eHealth domain, we show how our generalized model can be used to implement and enact complex domain-specific privacy policies. We illustrate our approach by constructing real world use cases involving a mobile Electronic Health Record that interacts with, and in different environments.
Lacroix, Alexsandre B., Langlois, J.M. Pierre, Boyer, François-Raymond, Gosselin, Antoine, Bois, Guy.  2016.  Node Configuration for the Aho-Corasick Algorithm in Intrusion Detection Systems. Proceedings of the 2016 Symposium on Architectures for Networking and Communications Systems. :121–122.

In this paper, we analyze the performance and cost trade-off from selecting two representations of nodes when implementing the Aho-Corasick algorithm. This algorithm can be used for pattern matching in network-based intrusion detection systems such as Snort. Our analysis uses the Snort 2.9.7 rules set, which contains almost 26k patterns. Our methodology consists of code profiling and analysis, followed by the selection of a parameter to maximize a metric that combines clock cycles count and memory usage. The parameter determines which of two types of nodes is selected for each trie node. We show that it is possible to select the parameter to optimize the metric, which results in an improvement by up to 12× compared with the single node-type case.

Lacroix, Jesse, El-Khatib, Khalil, Akalu, Rajen.  2016.  Vehicular Digital Forensics: What Does My Vehicle Know About Me? Proceedings of the 6th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications. :59–66.

A major component of modern vehicles is the infotainment system, which interfaces with its drivers and passengers. Other mobile devices, such as handheld phones and laptops, can relay information to the embedded infotainment system through Bluetooth and vehicle WiFi. The ability to extract information from these systems would help forensic analysts determine the general contents that is stored in an infotainment system. Based off the data that is extracted, this would help determine what stored information is relevant to law enforcement agencies and what information is non-essential when it comes to solving criminal activities relating to the vehicle itself. This would overall solidify the Intelligent Transport System and Vehicular Ad Hoc Network infrastructure in combating crime through the use of vehicle forensics. Additionally, determining the content of these systems will allow forensic analysts to know if they can determine anything about the end-user directly and/or indirectly.

Lafram, Ichrak, Berbiche, Naoual, El Alami, Jamila.  2019.  Artificial Neural Networks Optimized with Unsupervised Clustering for IDS Classification. 2019 1st International Conference on Smart Systems and Data Science (ICSSD). :1–7.

Information systems are becoming more and more complex and closely linked. These systems are encountering an enormous amount of nefarious traffic while ensuring real - time connectivity. Therefore, a defense method needs to be in place. One of the commonly used tools for network security is intrusion detection systems (IDS). An IDS tries to identify fraudulent activity using predetermined signatures or pre-established user misbehavior while monitoring incoming traffic. Intrusion detection systems based on signature and behavior cannot detect new attacks and fall when small behavior deviations occur. Many researchers have proposed various approaches to intrusion detection using machine learning techniques as a new and promising tool to remedy this problem. In this paper, the authors present a combination of two machine learning methods, unsupervised clustering followed by a supervised classification framework as a Fast, highly scalable and precise packets classification system. This model's performance is assessed on the new proposed dataset by the Canadian Institute for Cyber security and the University of New Brunswick (CICIDS2017). The overall process was fast, showing high accuracy classification results.

Laguna, Ignacio, Schulz, Martin, Richards, David F., Calhoun, Jon, Olson, Luke.  2016.  IPAS: Intelligent Protection Against Silent Output Corruption in Scientific Applications. Proceedings of the 2016 International Symposium on Code Generation and Optimization. :227–238.

This paper presents IPAS, an instruction duplication technique that protects scientific applications from silent data corruption (SDC) in their output. The motivation for IPAS is that, due to natural error masking, only a subset of SDC errors actually affects the output of scientific codes—we call these errors silent output corruption (SOC) errors. Thus applications require duplication only on code that, when affected by a fault, yields SOC. We use machine learning to learn code instructions that must be protected to avoid SOC, and, using a compiler, we protect only those vulnerable instructions by duplication, thus significantly reducing the overhead that is introduced by instruction duplication. In our experiments with five workloads, IPAS reduces the percentage of SOC by up to 90% with a slowdown that ranges between 1.04x and 1.35x, which corresponds to as much as 47% less slowdown than state-of-the-art instruction duplication techniques.

Lagunas, E., Rugini, L..  2017.  Performance of compressive sensing based energy detection. 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). :1–5.

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.

Lahbib, A., Toumi, K., Elleuch, S., Laouiti, A., Martin, S..  2017.  Link Reliable and Trust Aware RPL Routing Protocol for Internet of Things. 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA). :1–5.

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.

Lahrouni, Youssef, Pereira, Caroly, Bensaber, Boucif Amar, Biskri, Ismaïl.  2017.  Using Mathematical Methods Against Denial of Service (DoS) Attacks in VANET. Proceedings of the 15th ACM International Symposium on Mobility Management and Wireless Access. :17–22.

VANET network is a new technology on which future intelligent transport systems are based; its purpose is to develop the vehicular environment and make it more comfortable. In addition, it provides more safety for drivers and cars on the road. Therefore, we have to make this technology as secured as possible against many threats. As VANET is a subclass of MANET, it has inherited many security problems but with a different architecture and DOS attacks are one of them. In this paper, we have focused on DOS attacks that prevent users to receive the right information at the right moment. We have analyzed DOS attacks behavior and effects on the network using different mathematical models in order to find an efficient solution.

Lai, Chengzhe, Du, Yangyang, Men, Jiawei, Zheng, Dong.  2019.  A Trust-based Real-time Map Updating Scheme. 2019 IEEE/CIC International Conference on Communications in China (ICCC). :334—339.

The real-time map updating enables vehicles to obtain accurate and timely traffic information. Especially for driverless cars, real-time map updating can provide high-precision map service to assist the navigation, which requires vehicles to actively upload the latest road conditions. However, due to the untrusted network environment, it is difficult for the real-time map updating server to evaluate the authenticity of the road information from the vehicles. In order to prevent malicious vehicles from deliberately spreading false information and protect the privacy of vehicles from tracking attacks, this paper proposes a trust-based real-time map updating scheme. In this scheme, the public key is used as the identifier of the vehicle for anonymous communication with conditional anonymity. In addition, the blockchain is applied to provide the existence proof for the public key certificate of the vehicle. At the same time, to avoid the spread of false messages, a trust evaluation algorithm is designed. The fog node can validate the received massages from vehicles using Bayesian Inference Model. Based on the verification results, the road condition information is sent to the real-time map updating server so that the server can update the map in time and prevent the secondary traffic accident. In order to calculate the trust value offset for the vehicle, the fog node generates a rating for each message source vehicle, and finally adds the relevant data to the blockchain. According to the result of security analysis, this scheme can guarantee the anonymity and prevent the Sybil attack. Simulation results show that the proposed scheme is effective and accurate in terms of real-time map updating and trust values calculating.

Lai, Chengzhe, Ding, Yuhan.  2019.  A Secure Blockchain-Based Group Mobility Management Scheme in VANETs. 2019 IEEE/CIC International Conference on Communications in China (ICCC). :340–345.

Vehicular Ad-hoc Network (VANET) can provide vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications for efficient and safe transportation. The vehicles features high mobility, thus undergoing frequent handovers when they are moving, which introduces the significant overload on the network entities. To address the problem, the distributed mobility management (DMM) protocol for next generation mobile network has been proposed, which can be well combined with VANETs. Although the existing DMM solutions can guarantee the smooth handovers of vehicles, the security has not been fully considered in the mobility management. Moreover, the most of existing schemes cannot support group communication scenario. In this paper, we propose an efficient and secure group mobility management scheme based on the blockchain. Specifically, to reduce the handover latency and signaling cost during authentication, aggregate message authentication code (AMAC) and one-time password (OTP) are adopted. The security analysis and the performance evaluation results show that the proposed scheme can not only enhance the security functionalities but also support fast handover authentication.

Lai, Chia-Min, Lu, Chia-Yu, Lee, Hahn-Ming.  2018.  Implementation of Adversarial Scenario to Malware Analytic. Proceedings of the 2Nd International Conference on Machine Learning and Soft Computing. :127–132.

As the worldwide internet has non-stop developments, it comes with enormous amount automatically generated malware. Those malware had become huge threaten to computer users. A comprehensive malware family classifier can help security researchers to quickly identify characteristics of malware which help malware analysts to investigate in more efficient way. However, despite the assistance of the artificial intelligent (AI) classifiers, it has been shown that the AI-based classifiers are vulnerable to so-called adversarial attacks. In this paper, we demonstrate how the adversarial settings can be applied to the classifier of malware families classification. Our experimental results achieved high successful rate through the adversarial attack. We also find the important features which are ignored by malware analysts but useful in the future analysis.

Lai, J., Duan, B., Su, Y., Li, L., Yin, Q..  2017.  An active security defense strategy for wind farm based on automated decision. 2017 IEEE Power Energy Society General Meeting. :1–5.

With the development of smart grid, information and energy integrate deeply. For remote monitoring and cluster management, SCADA system of wind farm should be connected to Internet. However, communication security and operation risk put forward a challenge to data network of the wind farm. To address this problem, an active security defense strategy combined whitelist and security situation assessment is proposed. Firstly, the whitelist is designed by analyzing the legitimate packet of Modbus on communication of SCADA servers and PLCs. Then Knowledge Automation is applied to establish the Decision Requirements Diagram (DRD) for wind farm security. The D-S evidence theory is adopted to assess operation situation of wind farm and it together with whitelist offer the security decision for wind turbine. This strategy helps to eliminate the wind farm owners' security concerns of data networking, and improves the integrity of the cyber security defense for wind farm.

Lai, Shangqi, Patranabis, Sikhar, Sakzad, Amin, Liu, Joseph K., Mukhopadhyay, Debdeep, Steinfeld, Ron, Sun, Shi-Feng, Liu, Dongxi, Zuo, Cong.  2018.  Result Pattern Hiding Searchable Encryption for Conjunctive Queries. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :745–762.

The recently proposed Oblivious Cross-Tags (OXT) protocol (CRYPTO 2013) has broken new ground in designing efficient searchable symmetric encryption (SSE) protocol with support for conjunctive keyword search in a single-writer single-reader framework. While the OXT protocol offers high performance by adopting a number of specialised data-structures, it also trades-off security by leaking 'partial' database information to the server. Recent attacks have exploited similar partial information leakage to breach database confidentiality. Consequently, it is an open problem to design SSE protocols that plug such leakages while retaining similar efficiency. In this paper, we propose a new SSE protocol, called Hidden Cross-Tags (HXT), that removes 'Keyword Pair Result Pattern' (KPRP) leakage for conjunctive keyword search. We avoid this leakage by adopting two additional cryptographic primitives - Hidden Vector Encryption (HVE) and probabilistic (Bloom filter) indexing into the HXT protocol. We propose a 'lightweight' HVE scheme that only uses efficient symmetric-key building blocks, and entirely avoids elliptic curve-based operations. At the same time, it affords selective simulation-security against an unbounded number of secret-key queries. Adopting this efficient HVE scheme, the overall practical storage and computational overheads of HXT over OXT are relatively small (no more than 10% for two keywords query, and 21% for six keywords query), while providing a higher level of security.

Lakhdhar, Yosra, Rekhis, Slim, Boudriga, Noureddine.  2017.  Proactive Damage Assessment of Cyber Attacks Using Mobile Observer Agents. Proceedings of the 15th International Conference on Advances in Mobile Computing & Multimedia. :29–38.

One of the most critical challenges facing cyber defense nowadays is the complexity of recent released cyber-attacks, which are capable of disrupting critical industries and jeopardizing national economy. In this context, moving beyond common security approaches to make it possible to neutralize and react to security attacks at their early stages, becomes a requisite. We develop in this paper a formal model for the proactive assessment of security damages. We define a network of observer agents capable of observing incomplete information about attacks and affected cyber systems, and generating security observations useful for the identification of ongoing attack scenarios and their evolution in the future. A set of analytics are developed for the generation and management of scenario contexts as a set of measures useful for the proactive assessment of damages in the future, and the launching of countermeasures. A case study is provided to exemplify the proposal.

Lakhdhar, Yosra, Rekhis, Slim, Boudriga, Noureddine.  2016.  An Approach To A Graph-Based Active Cyber Defense Model. Proceedings of the 14th International Conference on Advances in Mobile Computing and Multi Media. :261–268.

Securing cyber system is a major concern as security attacks become more and more sophisticated. We develop in this paper a novel graph-based Active Cyber Defense (ACD) model to proactively respond to cyber attacks. The proposed model is based on the use of a semantically rich graph to describe cyber systems, types of used interconnection between them, and security related data useful to develop active defense strategies. The developed model takes into consideration the probabilistic nature of cyber attacks, and their degree of complexity. In this context, analytics are provided to proactively test the impact of vulnerabilities/threats increase on the system, analyze the consequent behavior of cyber systems and security solution, and decide about the security state of the whole cyber system. Our model integrates in the same framework decisions made by cyber defenders based on their expertise and knowledge, and decisions that are automatically generated using security analytic rules.

Lakhita, Yadav, S., Bohra, B., Pooja.  2015.  A review on recent phishing attacks in Internet. 2015 International Conference on Green Computing and Internet of Things (ICGCIoT). :1312–1315.

The development of internet comes with the other domain that is cyber-crime. The record and intelligently can be exposed to a user of illegal activity so that it has become important to make the technology reliable. Phishing techniques include domain of email messages. Phishing emails have hosted such a phishing website, where a click on the URL or the malware code as executing some actions to perform is socially engineered messages. Lexically analyzing the URLs can enhance the performance and help to differentiate between the original email and the phishing URL. As assessed in this study, in addition to textual analysis of phishing URL, email classification is successful and results in a highly precise anti phishing.

Lakhno, Valeriy, Kasatkin, Dmytro, Blozva, Andriy.  2019.  Modeling Cyber Security of Information Systems Smart City Based on the Theory of Games and Markov Processes. 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S T). :497–501.
The article considers some aspects of modeling information security circuits for information and communication systems used in Smart City. As a basic research paradigm, the postulates of game theory and mathematical dependencies based on Markov processes were used. Thus, it is possible to sufficiently substantively describe the procedure for selecting rational variants of cyber security systems used to protect information technologies in Smart City. At the same time, using the model proposed by us, we can calculate the probability of cyber threats for the Smart City systems, as well as the cybernetic risks of diverse threats. Further, on the basis of the described indicators, rational contour options are chosen to protect the information systems used in Smart City.
Lakshminarayana, Subhash, Karachiwala, Jabir Shabbir, Chang, Sang-Yoon, Revadigar, Girish, Kumar, Sristi Lakshmi Sravana, Yau, David K.Y., Hu, Yih-Chun.  2018.  Signal Jamming Attacks Against Communication-Based Train Control: Attack Impact and Countermeasure. Proceedings of the 11th ACM Conference on Security & Privacy in Wireless and Mobile Networks. :160-171.
We study the impact of signal jamming attacks against the communication based train control (CBTC) systems and develop the countermeasures to limit the attacks' impact. CBTC supports the train operation automation and moving-block signaling, which improves the transport efficiency. We consider an attacker jamming the wireless communication between the trains or the train to wayside access point, which can disable CBTC and the corresponding benefits. In contrast to prior work studying jamming only at the physical or link layer, we study the real impact of such attacks on end users, namely train journey time and passenger congestion. Our analysis employs a detailed model of leaky medium-based communication system (leaky waveguide or leaky feeder/coaxial cable) popularly used in CBTC systems. To counteract the jamming attacks, we develop a mitigation approach based on frequency hopping spread spectrum taking into account domain-specific structure of the leaky-medium CBTC systems. Specifically, compared with existing implementations of FHSS, we apply FHSS not only between the transmitter-receiver pair but also at the track-side repeaters. To demonstrate the feasibility of implementing this technology in CBTC systems, we develop a FHSS repeater prototype using software-defined radios on both leaky-medium and open-air (free-wave) channels. We perform extensive simulations driven by realistic running profiles of trains and real-world passenger data to provide insights into the jamming attack's impact and the effectiveness of the proposed countermeasure.
Lakshminarayana, Subhash, Teng, Teo Zhan, Yau, David K. Y., Tan, Rui.  2017.  Optimal Attack Against Cyber-Physical Control Systems with Reactive Attack Mitigation. Proceedings of the Eighth International Conference on Future Energy Systems. :179–190.
This paper studies the performance and resilience of a cyber-physical control system (CPCS) with attack detection and reactive attack mitigation. It addresses the problem of deriving an optimal sequence of false data injection attacks that maximizes the state estimation error of the system. The results provide basic understanding about the limit of the attack impact. The design of the optimal attack is based on a Markov decision process (MDP) formulation, which is solved efficiently using the value iteration method. Using the proposed framework, we quantify the effect of false positives and mis-detections on the system performance, which can help the joint design of the attack detection and mitigation. To demonstrate the use of the proposed framework in a real-world CPCS, we consider the voltage control system of power grids, and run extensive simulations using PowerWorld, a high-fidelity power system simulator, to validate our analysis. The results show that by carefully designing the attack sequence using our proposed approach, the attacker can cause a large deviation of the bus voltages from the desired set-point. Further, the results verify the optimality of the derived attack sequence and show that, to cause maximum impact, the attacker must carefully craft his attack to strike a balance between the attack magnitude and stealthiness, due to the simultaneous presence of attack detection and mitigation.