Biblio

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2020-04-03
Zhou, Hai, Rezaei, Amin, Shen, Yuanqi.  2019.  Resolving the Trilemma in Logic Encryption. 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). :1—8.

Logic encryption, a method to lock a circuit from unauthorized use unless the correct key is provided, is the most important technique in hardware IP protection. However, with the discovery of the SAT attack, all traditional logic encryption algorithms are broken. New algorithms after the SAT attack are all vulnerable to structural analysis unless a provable obfuscation is applied to the locked circuit. But there is no provable logic obfuscation available, in spite of some vague resorting to logic resynthesis. In this paper, we formulate and discuss a trilemma in logic encryption among locking robustness, structural security, and encryption efficiency, showing that pre-SAT approaches achieve only structural security and encryption efficiency, and post-SAT approaches achieve only locking robustness and encryption efficiency. There is also a dilemma between query complexity and error number in locking. We first develop a theory and solution to the dilemma in locking between query complexity and error number. Then, we provide a provable obfuscation solution to the dilemma between structural security and locking robustness. We finally present and discuss some results towards the resolution of the trilemma in logic encryption.

2020-10-06
Zaman, Tarannum Shaila, Han, Xue, Yu, Tingting.  2019.  SCMiner: Localizing System-Level Concurrency Faults from Large System Call Traces. 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). :515—526.

Localizing concurrency faults that occur in production is hard because, (1) detailed field data, such as user input, file content and interleaving schedule, may not be available to developers to reproduce the failure; (2) it is often impractical to assume the availability of multiple failing executions to localize the faults using existing techniques; (3) it is challenging to search for buggy locations in an application given limited runtime data; and, (4) concurrency failures at the system level often involve multiple processes or event handlers (e.g., software signals), which can not be handled by existing tools for diagnosing intra-process(thread-level) failures. To address these problems, we present SCMiner, a practical online bug diagnosis tool to help developers understand how a system-level concurrency fault happens based on the logs collected by the default system audit tools. SCMiner achieves online bug diagnosis to obviate the need for offline bug reproduction. SCMiner does not require code instrumentation on the production system or rely on the assumption of the availability of multiple failing executions. Specifically, after the system call traces are collected, SCMiner uses data mining and statistical anomaly detection techniques to identify the failure-inducing system call sequences. It then maps each abnormal sequence to specific application functions. We have conducted an empirical study on 19 real-world benchmarks. The results show that SCMiner is both effective and efficient at localizing system-level concurrency faults.

2020-04-17
Kiss, Ákos, Hodován, Renáta.  2019.  Security-Related Commits in Open Source Web Browser Projects. 2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW). :57—60.

The security of web browsers is of paramount importance, these days perhaps more than ever. Unfortunately, acquiring real data for security-related research is not an easy task, as access to sensitive information is rarely granted to researchers who are not members of a trusted security team. In this paper, we describe a method to mine security-related commits from open source software repositories, even if the reports of already fixed security issues have access restrictions, and we show the applicability of the method on two popular web browser projects. We also made the mined dataset available, listing more than 13,000 security-related commits, with which we hope to facilitate research on security-targeted bug prediction.

2020-08-28
Karaküçük, Ahmet, Dirik, A. Emir.  2019.  Source Device Attribution of Thermal Images Captured with Handheld IR Cameras. 2019 11th International Conference on Electrical and Electronics Engineering (ELECO). :547—551.

Source camera attribution of digital images has been a hot research topic in digital forensics literature. However, the thermal cameras and the radiometric data they generate stood as a nascent topic, as such devices are expensive and tailored for specific use-cases - not adapted by the masses. This has changed dramatically, with the low-cost, pluggable thermal-camera add-ons to smartphones and similar low-cost pocket-size thermal cameras introduced to consumers recently, which enabled the use of thermal imaging devices for the masses. In this paper, we are going to investigate the use of an established source device attribution method on radiometric data produced with a consumer-level, low-cost handheld thermal camera. The results we represent in this paper are promising and show that it is quite possible to attribute thermal images with their source camera.

2020-05-22
Chen, Jing, Tong, Wencan, Li, Xiaojian, Jiang, Yiyi, Zhu, Liyu.  2019.  A Survey of Time-varying Structural Modeling to Accountable Cloud Services. 2019 IEEE International Conference on Computation, Communication and Engineering (ICCCE). :9—12.

Cloud service has the computing characteristics of self-organizing strain on demand, which is prone to failure or loss of responsibility in its extensive application. In the prediction or accountability of this, the modeling of cloud service structure becomes an insurmountable priority. This paper reviews the modeling of cloud service network architecture. It mainly includes: Firstly, the research status of cloud service structure modeling is analyzed and reviewed. Secondly, the classification of time-varying structure of cloud services and the classification of time-varying structure modeling methods are summarized as a whole. Thirdly, it points out the existing problems. Finally, for cloud service accountability, research approach of time-varying structure modeling is proposed.

2020-01-21
Mazurczyk, Wojciech, Powójski, Krystian, Caviglione, Luca.  2019.  IPv6 Covert Channels in the Wild. Proceedings of the Third Central European Cybersecurity Conference. :1–6.

The increasing diffusion of malware endowed with steganographic techniques requires to carefully identify and evaluate a new set of threats. The creation of a covert channel to hide a communication within network traffic is one of the most relevant, as it can be used to exfiltrate information or orchestrate attacks. Even if network steganography is becoming a well-studied topic, only few works focus on IPv6 and consider real network scenarios. Therefore, this paper investigates IPv6 covert channels deployed in the wild. Also, it presents a performance evaluation of six different data hiding techniques for IPv6 including their ability to bypass some intrusion detection systems. Lastly, ideas to detect IPv6 covert channels are presented.

2020-01-27
Yao, Yuanshun, Li, Huiying, Zheng, Haitao, Zhao, Ben Y..  2019.  Latent Backdoor Attacks on Deep Neural Networks. Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. :2041–2055.

Recent work proposed the concept of backdoor attacks on deep neural networks (DNNs), where misclassification rules are hidden inside normal models, only to be triggered by very specific inputs. However, these "traditional" backdoors assume a context where users train their own models from scratch, which rarely occurs in practice. Instead, users typically customize "Teacher" models already pretrained by providers like Google, through a process called transfer learning. This customization process introduces significant changes to models and disrupts hidden backdoors, greatly reducing the actual impact of backdoors in practice. In this paper, we describe latent backdoors, a more powerful and stealthy variant of backdoor attacks that functions under transfer learning. Latent backdoors are incomplete backdoors embedded into a "Teacher" model, and automatically inherited by multiple "Student" models through transfer learning. If any Student models include the label targeted by the backdoor, then its customization process completes the backdoor and makes it active. We show that latent backdoors can be quite effective in a variety of application contexts, and validate its practicality through real-world attacks against traffic sign recognition, iris identification of volunteers, and facial recognition of public figures (politicians). Finally, we evaluate 4 potential defenses, and find that only one is effective in disrupting latent backdoors, but might incur a cost in classification accuracy as tradeoff.

Almeida, José Bacelar, Barbosa, Manuel, Barthe, Gilles, Campagna, Matthew, Cohen, Ernie, Grégoire, Benjamin, Pereira, Vitor, Portela, Bernardo, Strub, Pierre-Yves, Tasiran, Serdar.  2019.  A Machine-Checked Proof of Security for AWS Key Management Service. Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. :63–78.

We present a machine-checked proof of security for the domain management protocol of Amazon Web Services' KMS (Key Management Service) a critical security service used throughout AWS and by AWS customers. Domain management is at the core of AWS KMS; it governs the top-level keys that anchor the security of encryption services at AWS. We show that the protocol securely implements an ideal distributed encryption mechanism under standard cryptographic assumptions. The proof is machine-checked in the EasyCrypt proof assistant and is the largest EasyCrypt development to date.

Jarecki, Stanislaw, Krawczyk, Hugo, Resch, Jason.  2019.  Updatable Oblivious Key Management for Storage Systems. Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. :379–393.

We introduce Oblivious Key Management Systems (KMS) as a much more secure alternative to traditional wrapping-based KMS that form the backbone of key management in large-scale data storage deployments. The new system, that builds on Oblivious Pseudorandom Functions (OPRF), hides keys and object identifiers from the KMS, offers unconditional security for key transport, provides key verifiability, reduces storage, and more. Further, we show how to provide all these features in a distributed threshold implementation that enhances protection against server compromise. We extend this system with updatable encryption capability that supports key updates (known as key rotation) so that upon the periodic change of OPRF keys by the KMS server, a very efficient update procedure allows a client of the KMS service to non-interactively update all its encrypted data to be decryptable only by the new key. This enhances security with forward and post-compromise security, namely, security against future and past compromises, respectively, of the client's OPRF keys held by the KMS. Additionally, and in contrast to traditional KMS, our solution supports public key encryption and dispenses with any interaction with the KMS for data encryption (only decryption by the client requires such communication). Our solutions build on recent work on updatable encryption but with significant enhancements applicable to the remote KMS setting. In addition to the critical security improvements, our designs are highly efficient and ready for use in practice. We report on experimental implementation and performance.

2020-04-17
Jang, Yunseok, Zhao, Tianchen, Hong, Seunghoon, Lee, Honglak.  2019.  Adversarial Defense via Learning to Generate Diverse Attacks. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). :2740—2749.

With the remarkable success of deep learning, Deep Neural Networks (DNNs) have been applied as dominant tools to various machine learning domains. Despite this success, however, it has been found that DNNs are surprisingly vulnerable to malicious attacks; adding a small, perceptually indistinguishable perturbations to the data can easily degrade classification performance. Adversarial training is an effective defense strategy to train a robust classifier. In this work, we propose to utilize the generator to learn how to create adversarial examples. Unlike the existing approaches that create a one-shot perturbation by a deterministic generator, we propose a recursive and stochastic generator that produces much stronger and diverse perturbations that comprehensively reveal the vulnerability of the target classifier. Our experiment results on MNIST and CIFAR-10 datasets show that the classifier adversarially trained with our method yields more robust performance over various white-box and black-box attacks.

2020-03-23
Manucom, Emraida Marie M., Gerardo, Bobby D., Medina, Ruji P..  2019.  Analysis of Key Randomness in Improved One-Time Pad Cryptography. 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID). :11–16.
In cryptography, one-time pad (OTP) is claimed to be the perfect secrecy algorithm in several works if all of its features are applied correctly. Its secrecy depends mostly on random keys, which must be truly random and unpredictable. Random number generators are used in key generation. In Psuedo Random Number Generator (PRNG), the possibility of producing numbers that are predictable and repeated exists. In this study, a proposed method using True Random Number Generator (TRNG) and Fisher-Yates shuffling algorithm are implemented to generate random keys for OTP. Frequency (monobit) test, frequency test within a block, and runs tests are performed and showed that the proposed method produces more random keys. Sufficient confusion and diffusion properties are obtained using Pearson correlation analysis.
2020-04-06
Chen, Chia-Mei, Wang, Shi-Hao, Wen, Dan-Wei, Lai, Gu-Hsin, Sun, Ming-Kung.  2019.  Applying Convolutional Neural Network for Malware Detection. 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST). :1—5.

Failure to detect malware at its very inception leaves room for it to post significant threat and cost to cyber security for not only individuals, organizations but also the society and nation. However, the rapid growth in volume and diversity of malware renders conventional detection techniques that utilize feature extraction and comparison insufficient, making it very difficult for well-trained network administrators to identify malware, not to mention regular users of internet. Challenges in malware detection is exacerbated since complexity in the type and structure also increase dramatically in these years to include source code, binary file, shell script, Perl script, instructions, settings and others. Such increased complexity offers a premium on misjudgment. In order to increase malware detection efficiency and accuracy under large volume and multiple types of malware, this research adopts Convolutional Neural Networks (CNN), one of the most successful deep learning techniques. The experiment shows an accuracy rate of over 90% in identifying malicious and benign codes. The experiment also presents that CNN is effective with detecting source code and binary code, it can further identify malware that is embedded into benign code, leaving malware no place to hide. This research proposes a feasible solution for network administrators to efficiently identify malware at the very inception in the severe network environment nowadays, so that information technology personnel can take protective actions in a timely manner and make preparations for potential follow-up cyber-attacks.

2020-03-02
Zhang, Xuefei, Liu, Junjie, Li, Yijing, Cui, Qimei, Tao, Xiaofeng, Liu, Ren Ping.  2019.  Blockchain Based Secure Package Delivery via Ridesharing. 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP). :1–6.

Delivery service via ridesharing is a promising service to share travel costs and improve vehicle occupancy. Existing ridesharing systems require participating vehicles to periodically report individual private information (e.g., identity and location) to a central controller, which is a potential central point of failure, resulting in possible data leakage or tampering in case of controller break down or under attack. In this paper, we propose a Blockchain secured ridesharing delivery system, where the immutability and distributed architecture of the Blockchain can effectively prevent data tampering. However, such tamper-resistance property comes at the cost of a long confirmation delay caused by the consensus process. A Hash-oriented Practical Byzantine Fault Tolerance (PBFT) based consensus algorithm is proposed to improve the Blockchain efficiency and reduce the transaction confirmation delay from 10 minutes to 15 seconds. The Hash-oriented PBFT effectively avoids the double-spending attack and Sybil attack. Security analysis and simulation results demonstrate that the proposed Blockchain secured ridesharing delivery system offers strong security guarantees and satisfies the quality of delivery service in terms of confirmation delay and transaction throughput.

2020-08-13
Protskaya, Yanina, Veltri, Luca.  2019.  Broker Bridging Mechanism for Providing Anonymity in MQTT. 2019 10th International Conference on Networks of the Future (NoF). :110—113.
With the growth of the number of smart devices the range of fields where they are used is growing too, and it is essential to protect the communication between them. In addition to data integrity and confidentiality, for which standard mechanisms exists, a security service that may also be required is anonymity, allowing entities to communicate with each other in such a way that no third party knows that they are the participants of a certain message exchange. In this paper we propose a mechanism for creating anonymous communications using MQTT protocol. The design of our solution is based on dynamic broker bridging mechanism and allows clients to subscribe and to publish to a topic remaining incognito.
2020-04-06
Guo, Haoran, Ai, Jun, Shi, Tao.  2019.  A Clone Code Detection Method Based on Software Complex Network. 2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). :120—121.

This paper introduces complex network into software clone detection and proposes a clone code detection method based on software complex network feature matching. This method has the following properties. It builds a software network model with many added features and codes written with different languages can be detected by a single method. It reduces the space of code comparison, and it searches similar subnetworks to detect clones without knowing any clone codes information. This method can be used in detecting open source code which has been reused in software for security analysis.

2020-03-18
Kalashnikov, A.O., Anikina, E.V..  2019.  Complex Network Cybersecurity Monitoring Method. 2019 Twelfth International Conference "Management of large-scale system development" (MLSD). :1–3.
This paper considers one of the methods of efficient allocation of limited resources in special-purpose devices (sensors) to monitor complex network unit cybersecurity.
2020-04-17
Joseph, Justin, Bhadauria, Saumya.  2019.  Cookie Based Protocol to Defend Malicious Browser Extensions. 2019 International Carnahan Conference on Security Technology (ICCST). :1—6.
All popular browsers support browser extensions. They are small software module for customizing web browsers. It provides extra features like user interface modifications, ad blocking, cookie management and so on. As features increase, security becomes more difficult. The impact of malicious browser extensions is also enormous. More than 1 million Chrome users got affected by extensions from Chrome store itself. [1] The risk further increases with offline extension installations. The privileges browser extensions have, pave the path for many kinds of attacks. Replay attack and session hijacking are two of these attacks we are dealing here. Here we propose a defence system based on dynamic encrypted cookies to defend these attacks. We use cookies as token for continuous authentication, which protects entire communication. Static cookies are prone for session hijacking, and therefore we use dynamic cookies which are sealed with encryption. It also protects from replay attack by changing itself, making previous message obsolete. This essentially solves both of the problems.
2020-04-24
Gao, Boyo, Shi, Libao, Ni, Yixin.  2019.  A dynamic defense-attack game scheme with incomplete information for vulnerability analysis in a cyber-physical power infrastructure. 8th Renewable Power Generation Conference (RPG 2019). :1—8.
The modern power system is experiencing rapid development towards a smarter cyber-physical power grid. How to comprehensively and effectively identify the vulnerable components under various cyber attacks has attracted widespread interest and attention around the world. In this paper, a game-theoretical scheme is developed to analyze the vulnerabilities of transmission lines and cyber elements under locally coordinated cyber-physical attacks in a cyber-physical power infrastructure. A two-step scenario including resources allocation made by system defender in advance and subsequent coordinated cyber-physical attacks are designed elaborately. The designed scenario is modeled as a game of incomplete information, which is then converted into a bi-level mathematical programming problem. In the lower level model, the attacker aims at maximizing system losses by attacking some transmission lines. While in the higher level model, the defender allocates defensive resources, trying to maximally reduce the losses considering the possible attacks. The payoffs of the game are calculated by leveraging a strategy of searching accident chains caused by cascading failure analyzed in this paper. A particle swarm optimization algorithm is applied to solve the proposed nonlinear bi-level programming model, and the case studies on a 34-bus system are conducted to verify the effectiveness of the proposed scheme.
2020-08-03
Shu-fen, NIU, Bo-bin, WANG, You-chen, WANG, Jin-feng, WANG, Jing-min, CHEN.  2019.  Efficient and Secure Proxy re-signature Message Authentication Scheme in Vehicular Ad Hoc Network. 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). :1652–1656.

In order to solve privacy protection problem in the Internet of Vehicles environment, a message authentication scheme based on proxy re-signature is proposed using elliptic curves, which realizes privacy protection by transforming the vehicle's signature of the message into the roadside unit's signature of the same message through the trusted center. And through the trusted center traceability, to achieve the condition of privacy protection, and the use of batch verification technology, greatly improve the efficiency of authentication. It is proved that the scheme satisfies unforgeability in ECDLP hard problem in the random oracle model. The efficiency analysis shows that the scheme meets the security and efficiency requirements of the Internet of Vehicles and has certain practical significance.

2020-03-23
Zheng, Yaowen, Song, Zhanwei, Sun, Yuyan, Cheng, Kai, Zhu, Hongsong, Sun, Limin.  2019.  An Efficient Greybox Fuzzing Scheme for Linux-based IoT Programs Through Binary Static Analysis. 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC). :1–8.

With the rapid growth of Linux-based IoT devices such as network cameras and routers, the security becomes a concern and many attacks utilize vulnerabilities to compromise the devices. It is crucial for researchers to find vulnerabilities in IoT systems before attackers. Fuzzing is an effective vulnerability discovery technique for traditional desktop programs, but could not be directly applied to Linux-based IoT programs due to the special execution environment requirement. In our paper, we propose an efficient greybox fuzzing scheme for Linux-based IoT programs which consist of two phases: binary static analysis and IoT program greybox fuzzing. The binary static analysis is to help generate useful inputs for efficient fuzzing. The IoT program greybox fuzzing is to reinforce the IoT firmware kernel greybox fuzzer to support IoT programs. We implement a prototype system and the evaluation results indicate that our system could automatically find vulnerabilities in real-world Linux-based IoT programs efficiently.

2020-06-19
Liu, Keng-Cheng, Hsu, Chen-Chien, Wang, Wei-Yen, Chiang, Hsin-Han.  2019.  Facial Expression Recognition Using Merged Convolution Neural Network. 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE). :296—298.

In this paper, a merged convolution neural network (MCNN) is proposed to improve the accuracy and robustness of real-time facial expression recognition (FER). Although there are many ways to improve the performance of facial expression recognition, a revamp of the training framework and image preprocessing renders better results in applications. When the camera is capturing images at high speed, however, changes in image characteristics may occur at certain moments due to the influence of light and other factors. Such changes can result in incorrect recognition of human facial expression. To solve this problem, we propose a statistical method for recognition results obtained from previous images, instead of using the current recognition output. Experimental results show that the proposed method can satisfactorily recognize seven basic facial expressions in real time.

2020-04-10
Newaz, AKM Iqtidar, Sikder, Amit Kumar, Rahman, Mohammad Ashiqur, Uluagac, A. Selcuk.  2019.  HealthGuard: A Machine Learning-Based Security Framework for Smart Healthcare Systems. 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS). :389—396.
The integration of Internet-of-Things and pervasive computing in medical devices have made the modern healthcare system “smart.” Today, the function of the healthcare system is not limited to treat the patients only. With the help of implantable medical devices and wearables, Smart Healthcare System (SHS) can continuously monitor different vital signs of a patient and automatically detect and prevent critical medical conditions. However, these increasing functionalities of SHS raise several security concerns and attackers can exploit the SHS in numerous ways: they can impede normal function of the SHS, inject false data to change vital signs, and tamper a medical device to change the outcome of a medical emergency. In this paper, we propose HealthGuard, a novel machine learning-based security framework to detect malicious activities in a SHS. HealthGuard observes the vital signs of different connected devices of a SHS and correlates the vitals to understand the changes in body functions of the patient to distinguish benign and malicious activities. HealthGuard utilizes four different machine learning-based detection techniques (Artificial Neural Network, Decision Tree, Random Forest, k-Nearest Neighbor) to detect malicious activities in a SHS. We trained HealthGuard with data collected for eight different smart medical devices for twelve benign events including seven normal user activities and five disease-affected events. Furthermore, we evaluated the performance of HealthGuard against three different malicious threats. Our extensive evaluation shows that HealthGuard is an effective security framework for SHS with an accuracy of 91 % and an F1 score of 90 %.
2020-06-15
Kipchuk, Feodosiy, Sokolov, Volodymyr, Buriachok, Volodymyr, Kuzmenko, Lidia.  2019.  Investigation of Availability of Wireless Access Points based on Embedded Systems. 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S T). :1–5.
The paper presents the results of load testing of embedded hardware platforms for Internet of Things solutions. Analyzed the available hardware. The operating systems from different manufacturers were consolidated into a single classification, and for the two most popular, load testing was performed by an external and internal wireless network adapter. Developed its own software solution based on the Python programming language. The number of wireless subscribers ranged from 7 to 14. Experimental results will be useful in deploying wireless infrastructure for small commercial and scientific wireless networks.
2020-01-28
Krishna, Gutha Jaya, Ravi, Vadlamani.  2019.  Keystroke Based User Authentication Using Modified Differential Evolution. TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). :739–744.

User Authentication is a difficult problem yet to be addressed accurately. Little or no work is reported in literature dealing with clustering-based anomaly detection techniques for user authentication for keystroke data. Therefore, in this paper, Modified Differential Evolution (MDE) based subspace anomaly detection technique is proposed for user authentication in the context of behavioral biometrics using keystroke dynamics features. Thus, user authentication is posed as an anomaly detection problem. Anomalies in CMU's keystroke dynamics dataset are identified using subspace-based and distance-based techniques. It is observed that, among the proposed techniques, MDE based subspace anomaly detection technique yielded the highest Area Under ROC Curve (AUC) for user authentication problem. We also performed a Wilcoxon Signed Rank statistical test to corroborate our results statistically.

2020-03-23
Alaoui, Sadek Belamfedel, El Houssaine, Tissir, Noreddine, Chaibi.  2019.  Modelling, analysis and design of active queue management to mitigate the effect of denial of service attack in wired/wireless network. 2019 International Conference on Wireless Networks and Mobile Communications (WINCOM). :1–7.
Mitigating the effect of Distributed Denial of Service (DDoS) attacks in wired/wireless networks is a problem of extreme importance. The present paper investigates this problem and proposes a secure AQM to encounter the effects of DDoS attacks on queue's router. The employed method relies on modelling the TCP/AQM system subjected to different DoS attack rate where the resulting closed-loop system is expressed as new Markovian Jump Linear System (MJLS). Sufficient delay-dependent conditions which guarantee the syntheses of a stabilizing control for the closed-loop system with a guaranteed cost J* are derived. Finally, a numerical example is displayed.