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Smith, Andrew, Vorobeychik, Yevgeniy, Letchford, Joshua.  2014.  Multi-Defender Security Games on Networks. SIGMETRICS Perform. Eval. Rev.. 41:4–7.

Stackelberg security game models and associated computational tools have seen deployment in a number of high- consequence security settings, such as LAX canine patrols and Federal Air Marshal Service. This deployment across essentially independent agencies raises a natural question: what global impact does the resulting strategic interaction among the defenders, each using a similar model, have? We address this question in two ways. First, we demonstrate that the most common solution concept of Strong Stackelberg equilibrium (SSE) can result in significant under-investment in security entirely because SSE presupposes a single defender. Second, we propose a framework based on a different solution concept which incorporates a model of interdependencies among targets, and show that in this framework defenders tend to over-defend, even under significant positive externalities of increased defense.

Hadagali, C..  2017.  Multicore implementation of EME2 AES disk encryption algorithm using OpenMP. 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–6.

Volume of digital data is increasing at a faster rate and the security of the data is at risk while being transit on a network as well as at rest. The execution time of full disk encryption in large servers is significant because of the computational complexity associated with disk encryption. Hence it is necessary to reduce the execution time of full disk encryption from the application point of view. In this work a full disk encryption algorithm namely EME2 AES (Encrypt Mix Encrypt V2 Advanced Encryption Standard) is analyzed. The execution speed of this algorithm is reduced by means of multicore compatible parallel implementation which makes use of available cores. Parallel implementation is executed on a multicore machine with 8 cores and speed up on the multicore implementation is measured. Results show that the multicore implementation of EME2 AES using OpenMP is up to 2.85 times faster than sequential execution for the chosen infrastructure and data range.

Zhang, Junjie, Sun, Tianfu.  2019.  Multi-core Heterogeneous Video Processing System Design. 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID). :178–182.
In order to accelerate the image processing speed, in this paper, a multi-core heterogeneous computing technology based on the Xilinx Zynq platform is proposed. The proposed technique could accelerate the real-time video image processing system through hardware acceleration. In order to verify the proposed technique, an Otsu binarized hardware-accelerated IP is designed in FPGA and interacts with ARM through the AXI bus. Compared with the existing homogeneous architecture processor computing, the image processing speed of the proposed technique with multi-core heterogeneous acceleration processing is significantly accelerated.
Hoque, Enamul, Carenini, Giuseppe.  2016.  MultiConVis: A Visual Text Analytics System for Exploring a Collection of Online Conversations. Proceedings of the 21st International Conference on Intelligent User Interfaces. :96–107.

Online conversations, such as blogs, provide rich amount of information and opinions about popular queries. Given a query, traditional blog sites return a set of conversations often consisting of thousands of comments with complex thread structure. Since the interfaces of these blog sites do not provide any overview of the data, it becomes very difficult for the user to explore and analyze such a large amount of conversational data. In this paper, we present MultiConVis, a visual text analytics system designed to support the exploration of a collection of online conversations. Our system tightly integrates NLP techniques for topic modeling and sentiment analysis with information visualizations, by considering the unique characteristics of online conversations. The resulting interface supports the user exploration, starting from a possibly large set of conversations, then narrowing down to the subset of conversations, and eventually drilling-down to the set of comments of one conversation. Our evaluations through case studies with domain experts and a formal user study with regular blog readers illustrate the potential benefits of our approach, when compared to a traditional blog reading interface.

Yan, Jingwei, Zheng, Wenming, Cui, Zhen, Tang, Chuangao, Zhang, Tong, Zong, Yuan, Sun, Ning.  2016.  Multi-clue Fusion for Emotion Recognition in the Wild. Proceedings of the 18th ACM International Conference on Multimodal Interaction. :458–463.

In the past three years, Emotion Recognition in the Wild (EmotiW) Grand Challenge has drawn more and more attention due to its huge potential applications. In the fourth challenge, aimed at the task of video based emotion recognition, we propose a multi-clue emotion fusion (MCEF) framework by modeling human emotion from three mutually complementary sources, facial appearance texture, facial action, and audio. To extract high-level emotion features from sequential face images, we employ a CNN-RNN architecture, where face image from each frame is first fed into the fine-tuned VGG-Face network to extract face feature, and then the features of all frames are sequentially traversed in a bidirectional RNN so as to capture dynamic changes of facial textures. To attain more accurate facial actions, a facial landmark trajectory model is proposed to explicitly learn emotion variations of facial components. Further, audio signals are also modeled in a CNN framework by extracting low-level energy features from segmented audio clips and then stacking them as an image-like map. Finally, we fuse the results generated from three clues to boost the performance of emotion recognition. Our proposed MCEF achieves an overall accuracy of 56.66% with a large improvement of 16.19% with respect to the baseline.

Yang, Lei, Humayed, Abdulmalik, Li, Fengjun.  2016.  A Multi-cloud Based Privacy-preserving Data Publishing Scheme for the Internet of Things. Proceedings of the 32Nd Annual Conference on Computer Security Applications. :30–39.

With the increased popularity of ubiquitous computing and connectivity, the Internet of Things (IoT) also introduces new vulnerabilities and attack vectors. While secure data collection (i.e. the upward link) has been well studied in the literature, secure data dissemination (i.e. the downward link) remains an open problem. Attribute-based encryption (ABE) and outsourced-ABE has been used for secure message distribution in IoT, however, existing mechanisms suffer from extensive computation and/or privacy issues. In this paper, we explore the problem of privacy-preserving targeted broadcast in IoT. We propose two multi-cloud-based outsourced-ABE schemes, namely the parallel-cloud ABE and the chain-cloud ABE, which enable the receivers to partially outsource the computationally expensive decryption operations to the clouds, while preventing user attributes from being disclosed. In particular, the proposed solution protects three types of privacy (i.e., data, attribute and access policy privacy) by enforcing collaborations among multiple clouds. Our schemes also provide delegation verifiability that allows the receivers to verify whether the clouds have faithfully performed the outsourced operations. We extensively analyze the security guarantees of the proposed mechanisms and demonstrate the effectiveness and efficiency of our schemes with simulated resource-constrained IoT devices, which outsource operations to Amazon EC2 and Microsoft Azure.

Amjad, N., Afzal, H., Amjad, M. F., Khan, F. A..  2018.  A Multi-Classifier Framework for Open Source Malware Forensics. 2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). :106-111.

Traditional anti-virus technologies have failed to keep pace with proliferation of malware due to slow process of their signatures and heuristics updates. Similarly, there are limitations of time and resources in order to perform manual analysis on each malware. There is a need to learn from this vast quantity of data, containing cyber attack pattern, in an automated manner to proactively adapt to ever-evolving threats. Machine learning offers unique advantages to learn from past cyber attacks to handle future cyber threats. The purpose of this research is to propose a framework for multi-classification of malware into well-known categories by applying different machine learning models over corpus of malware analysis reports. These reports are generated through an open source malware sandbox in an automated manner. We applied extensive pre-modeling techniques for data cleaning, features exploration and features engineering to prepare training and test datasets. Best possible hyper-parameters are selected to build machine learning models. These prepared datasets are then used to train the machine learning classifiers and to compare their prediction accuracy. Finally, these results are validated through a comprehensive 10-fold cross-validation methodology. The best results are achieved through Gaussian Naive Bayes classifier with random accuracy of 96% and 10-Fold Cross Validation accuracy of 91.2%. The said framework can be deployed in an operational environment to learn from malware attacks for proactively adapting matching counter measures.

Abuzainab, N., Saad, W..  2018.  A Multiclass Mean-Field Game for Thwarting Misinformation Spread in the Internet of Battlefield Things. IEEE Transactions on Communications. 66:6643—6658.

In this paper, the problem of misinformation propagation is studied for an Internet of Battlefield Things (IoBT) system, in which an attacker seeks to inject false information in the IoBT nodes in order to compromise the IoBT operations. In the considered model, each IoBT node seeks to counter the misinformation attack by finding the optimal probability of accepting given information that minimizes its cost at each time instant. The cost is expressed in terms of the quality of information received as well as the infection cost. The problem is formulated as a mean-field game with multiclass agents, which is suitable to model a massive heterogeneous IoBT system. For this game, the mean-field equilibrium is characterized, and an algorithm based on the forward backward sweep method is proposed to find the mean-field equilibrium. Then, the finite-IoBT case is considered, and the conditions of convergence of the equilibria in the finite case to the mean-field equilibrium are presented. Numerical results show that the proposed scheme can achieve a 1.2-fold increase in the quality of information compared with a baseline scheme, in which the IoBT nodes are always transmitting. The results also show that the proposed scheme can reduce the proportion of infected nodes by 99% compared with the baseline.

Abuzainab, N., Saad, W..  2018.  A Multiclass Mean-Field Game for Thwarting Misinformation Spread in the Internet of Battlefield Things (IoBT). IEEE Transactions on Communications. :1–1.

In this paper, the problem of misinformation propagation is studied for an Internet of Battlefield Things (IoBT) system in which an attacker seeks to inject false information in the IoBT nodes in order to compromise the IoBT operations. In the considered model, each IoBT node seeks to counter the misinformation attack by finding the optimal probability of accepting a given information that minimizes its cost at each time instant. The cost is expressed in terms of the quality of information received as well as the infection cost. The problem is formulated as a mean-field game with multiclass agents which is suitable to model a massive heterogeneous IoBT system. For this game, the mean-field equilibrium is characterized, and an algorithm based on the forward backward sweep method is proposed to find the mean-field equilibrium. Then, the finite IoBT case is considered, and the conditions of convergence of the equilibria in the finite case to the mean-field equilibrium are presented. Numerical results show that the proposed scheme can achieve a 1.2-fold increase in the quality of information (QoI) compared to a baseline scheme in which the IoBT nodes are always transmitting. The results also show that the proposed scheme can reduce the proportion of infected nodes by 99% compared to the baseline.

Cho, Junho, Cho, Ho-Shin.  2016.  A Multi-channel MAC Protocol in Underwater Acoustic Sensor Networks. Proceedings of the 11th ACM International Conference on Underwater Networks & Systems. :25:1–25:2.
In this paper, a multi-channel medium access control (MAC) protocol is proposed to overcome the Large Interference Range Collision (LIRC) problem in underwater acoustic sensor networks (UWASNs), which has been known to occur when a handshaking based MAC protocol is jointly used with a power control. The proposed scheme divides the frequency band into two separate channels each used for control and data packets transmission. Considering the acoustic signal attenuation characteristics, higher frequency is used for data and lower frequency is used for control. And then, the data transmission power is controlled to escape the LIRC problem and simultaneously to save as much as possible. Furthermore with the separated channels, we can also reduce control-data packet collisions.
Sun, Xuguang, Zhou, Yi, Shu, Xiaofeng.  2018.  Multi-Channel Linear Prediction Speech Dereverberation Algorithm Based on QR-RLS Adaptive Filter. Proceedings of the 3rd International Conference on Multimedia Systems and Signal Processing. :109–113.

This paper proposes a multi-channel linear prediction (MCLP) speech dereverberation algorithm based on QR-decomposition recursive least squares (QR-RLS) adaptive filter, which can avoid the possible instability caused by the RLS algorithm, and achieve same speech dereverberation performance as the prototype MCLP dereverberation algorithm based on RLS. This can be confirmed by the theoretical derivation and experiments. Thus, the proposed algorithm can be a good alternative for practical speech applications.

Li, Jiaxun, Zhao, Haitao, Wang, Haijun, Zhou, Li, Wei, Jibo.  2016.  Multi-channel Access and Rendezvous in CRNs: Demo. Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing. :353–354.

Cognitive radio (CR) has emerged as a promising technology to increase the utilization of spectrum resource. A pivotal challenge in CR lies on secondary users' (SU) finding each other on the frequency band, i.e., the spectrum locating. In this demo, we implement two kinds of multi-channel rendezvous technology to solve the problem of spectrum locating: (i) the common control channel (CCC) based rendezvous scheme, which is simple and effective when a control channel is always available; and (ii) the channel-hopping (CH) based blind rendezvous, which could also obtain guaranteed rendezvous on all commonly available channels of pairwise SUs in a short time without a CCC. Furthermore, the cognitive nodes in the demonstration could adjust their communication channels autonomously according to the dynamic spectrum environment for continuous data transmission.

Harb, H., William, A., El-Mohsen, O. A., Mansour, H. A..  2017.  Multicast Security Model for Internet of Things Based on Context Awareness. 2017 13th International Computer Engineering Conference (ICENCO). :303–309.

Internet of Things (IoT) devices are resource constrained devices in terms of power, memory, bandwidth, and processing. On the other hand, multicast communication is considered more efficient in group oriented applications compared to unicast communication as transmission takes place using fewer resources. That is why many of IoT applications rely on multicast in their transmission. This multicast traffic need to be secured specially for critical applications involving actuators control. Securing multicast traffic by itself is cumbersome as it requires an efficient and scalable Group Key Management (GKM) protocol. In case of IoT, the situation is more difficult because of the dynamic nature of IoT scenarios. This paper introduces a solution based on using context aware security server accompanied with a group of key servers to efficiently distribute group encryption keys to IoT devices in order to secure the multicast sessions. The proposed solution is evaluated relative to the Logical Key Hierarchy (LKH) protocol. The comparison shows that the proposed scheme efficiently reduces the load on the key servers. Moreover, the key storage cost on both members and key servers is reduced.

Yang, Bowen, Chen, Xiang, Xie, Jinsen, Li, Sugang, Zhang, Yanyong, Yang, Jian.  2019.  Multicast Design for the MobilityFirst Future Internet Architecture. 2019 International Conference on Computing, Networking and Communications (ICNC). :88–93.
With the advent of fifth generation (5G) network and increasingly powerful mobile devices, people can conveniently obtain network resources wherever they are and whenever they want. However, the problem of mobility support in current network has not been adequately solved yet, especially in inter-domain mobile scenario, which leads to poor experience for mobile consumers. MobilityFirst is a clean slate future Internet architecture which adopts a clean separation between identity and network location. It provides new mechanisms to address the challenge of wireless access and mobility at scale. However, MobilityFirst lacks effective ways to deal with multicast service over mobile networks. In this paper, we design an efficient multicast mechanism based on MobilityFirst architecture and present the deployment in current network at scale. Furthermore, we propose a hierarchical multicast packet header with additional destinations to achieve low-cost dynamic multicast routing and provide solutions for both the multicast source and the multicast group members moving in intra- or inter-domain. Finally, we deploy a multicast prototype system to evaluate the performance of the proposed multicast mechanism.
Zhou, Liming, Shan, Yingzi.  2019.  Multi-branch Source Location Privacy Protection Scheme Based on Random Walk in WSNs. 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). :543–547.
In many applications, source nodes send the sensing information of the monitored objects and the sinks receive the transmitted data. Considering the limited resources of sensor nodes, location privacy preservation becomes an important issue. Although many schemes are proposed to preserve source or sink location security, few schemes can preserve the location security of source nodes and sinks. In order to solve this problem, we propose a novel of multi-branch source location privacy protection method based on random walk. This method hides the location of real source nodes by setting multiple proxy sources. And multiple neighbors are randomly selected by the real source node as receivers until a proxy source receives the packet. In addition, the proxy source is chosen randomly, which can prevent the attacker from obtaining the location-related data of the real source node. At the same time, the scheme sets up a branch interference area around the base station to interfere with the adversary by increasing routing branches. Simulation results describe that our scheme can efficiently protect source and sink location privacy, reduce the communication overhead, and prolong the network lifetime.
Song, Wei-Tao, Hu, Bin, Zhao, Xiu-Feng.  2018.  Multi-Bit Leveled Homomorphic Encryption of Message Matrices. Proceedings of the 2Nd International Conference on Cryptography, Security and Privacy. :45-50.

Fully homomorphic encryption (FHE) makes it easier for cloud computing to be consistent with privacy. But the efficiency of existing FHE schemes is still far from the actual needs. The main cause is that most of existing FHE schemes are single-bit encryption. Hiromasa, Abe and Okamoto (PKC 2015) reached the major milestone by constructing the first fully homomorphic encryption (FHE) scheme that encrypted message matrices (with single-bit matrices components) and supported homomorphic matrix addition and multiplication. In this paper, we propose a more efficient variant of Hiromasa, Abe and Okamoto with a lower factor noise-expansion factor for homomorphic multiplication from $\Theta$(poly(n)) to $\Theta$(1) and multi-bit matrices components.

Viegas, P., Borges, D., Montezuma, P., Dinis, R., Silva, M. M..  2019.  Multi-beam Physical Security Scheme: Security Assessment and Impact of Array Impairments on Security and Quality of Service. 2019 PhotonIcs Electromagnetics Research Symposium - Spring (PIERS-Spring). :2368—2375.

Massive multiple-input multiple-output (mMIMO) with perfect channel state information (CSI) can lead array power gain increments proportional to the number of antennas. Despite this fact constrains on power amplification still exist due to envelope variations of high order constellation signals. These constrains can be overpassed by a transmitter with several amplification branches, with each one associated to a component signal that results from the decomposition of a multilevel constellation as a sum of several quasi constant envelope signals that are sent independently. When combined with antenna arrays at the end of each amplification branch the security improves due to the energy separation achieved by beamforming. However, to avoid distortion on the signal resulting from the combination of all components at channel level all the beams of signal components should be directed in same direction. In such conditions it is crucial to assess the impact of misalignments between beams associated to each user, which is the purpose of this work. The set of results presented here show the good tolerance against misalignments of these transmission structures.

Psychogiou, D., Simpson, D. J..  2018.  Multi-Band Acoustic-Wave-Lumped-Element Resonator-Based Bandstop Filters with Continuously Tunable Stopband Bandwidths. 2018 IEEE/MTT-S International Microwave Symposium - IMS. :860–863.
A new class of multi-band acoustic-wave-Iumped-ele-ment-resonator (AWLR)-based bandstop filters (BSFs) is reported. It is based on\$N\$multi-resonant A WLRs-shaped by\$K\$AWLRs and 2K inverters-that are connected to an all-pass network and result in\$\textbackslashtextbackslashpmbK\textbackslashtextbackslash Nˆth\$order rejection bands. The proposed concept allows the realization of multiple rejection bands with the following characteristics: i) fractional bandwidths (FBWs) larger than the electromechanical coupling coefficient\$\textbackslashtextbackslashpmbk\_tˆ\textbackslashtextbackslash 2\$of its constituent acoustic-wave resonators, ii) continuously variable and inde-pendently-controlled FBWs, iii) intrinsically-switched stopbands, and iv) an all pass state. For proof-of-concept validation purposes a dual-band prototype was designed, built, and tested. It exhibits two stopbands centered at 418 and 433 MHz that can be continu-ously-tuned in FBW (up to 7.7:1 tuning range) and in number.
Hong, Bo, Chen, Jie, Zhang, Kai, Qian, Haifeng.  2019.  Multi-Authority Non-Monotonic KP-ABE With Cryptographic Reverse Firewall. IEEE Access. 7:159002–159012.
The revelations of Snowden show that hardware and software of devices may corrupt users' machine to compromise the security in various ways. To address this concern, Mironov and Stephen-Davidowitz introduce the Cryptographic Reverse Firewall (CRF) concept that is able to resist the ex-filtration of secret information for some compromised machine (Eurocrypt 2015). There are some applications of CRF deployed in many cryptosystems, but less studied and deployed in Attribute-Based Encryption (ABE) field, which attracts a wide range of attention and is employed in real-world scenarios (i.e., data sharing in cloud). In this work, we focus how to give a CRF security protection for a multi-authority ABE scheme and hence propose a multi-authority key-policy ABE scheme with CRF (acronym, MA-KP-ABE-CRF), which supports attribute distribution and non-monotonic access structure. To achieve this, beginning with revisiting a MA-KP-ABE with non-trivial combining non-monotonic formula, we then give the randomness of ciphertexts and secret keys with reverse firewall and give formal security analysis. Finally, we give a simulation on our MA-KP-ABE-CRF system based on Charm library whose the experimental results demonstrate practical efficiency.
Zhang, ZhiShuo, Zhang, Wei, Qin, Zhiguang.  2020.  Multi-Authority CP-ABE with Dynamical Revocation in Space-Air-Ground Integrated Network. 2020 International Conference on Space-Air-Ground Computing (SAGC). :76–81.
Space-air-ground integrated network (SAGIN) is emerged as a versatile computing and traffic architecture in recent years. Though SAGIN brings many significant benefits for modern communication and computing services, there are many unprecedented challenges in SAGIN. The one critical challenge in SAGIN is the data security. In SAGIN, because the data will be stored in cleartext on cloud, the sensitive data may suffer from the illegal access by the unauthorized users even the untrusted cloud servers (CSs). Ciphertext-policy attribute-based encryption (CP-ABE), which is a type of attribute-based encryption (ABE), has been regarded as a promising solution to the critical challenge of the data security on cloud. But there are two main blemishes in traditional CP-ABE. The first one is that there is only one attribute authority (AA) in CP-ABE. If the single AA crashs down, the whole system will be shut down. The second one is that the AA cannot effectively manage the life cycle of the users’ private keys. If a user on longer has one attribute, the AA cannot revoke the user’s private key of this attribute. This means the user can still decrypt some ciphertexts using this invalid attribute. In this paper, to solve the two flaws mentioned above, we propose a multi-authority CP-ABE (MA-CP-ABE) scheme with the dynamical key revocation (DKR). Our key revocation supports both user revocation and attribute revocation. And the our revocation is time friendly. What’s more, by using our dynamically tag-based revocation algorithm, AAs can dynamically and directly re-enable or revoke the invalid attributes to users. Finally, by evaluating and implementing our scheme, we can observe that our scheme is more comprehensive and practical for cloud applications in SAGIN.
Liu, Zechao, Jiang, Zoe L., Wang, Xuan, Wu, Yulin, Yiu, S.M..  2018.  Multi-Authority Ciphertext Policy Attribute-Based Encryption Scheme on Ideal Lattices. 2018 IEEE Intl Conf on Parallel Distributed Processing with Applications, Ubiquitous Computing Communications, Big Data Cloud Computing, Social Computing Networking, Sustainable Computing Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom). :1003—1008.
Ciphertext policy attribute-based encryption (CP-ABE) is a promising cryptographic technology that provides fine-grained access control as well as data confidentiality. It enables one sender to encrypt the data for more receivers, and to specify a policy on who can decrypt the ciphertext using his/her attributes alone. However, most existing ABE schemes are constructed on bilinear maps and they cannot resist quantum attacks. In this paper, we propose a multi-authority CP-ABE (MA-CPABE) scheme on ideal lattices which is still secure in post-quantum era. On one hand, multiple attribute authorities are required when user's attributes cannot be managed by a central authority. On the other hand, compared with generic lattice, the ideal lattice has extra algebraic structure and can be used to construct more efficient cryptographic applications. By adding some virtual attributes for each authority, our scheme can support flexible threshold access policy. Security analysis shows that the proposed scheme is secure against chosen plaintext attack (CPA) in the standard model under the ring learning with errors (R-LWE) assumption.
Huang, Kaiqing.  2019.  Multi-Authority Attribute-Based Encryption for Resource-Constrained Users in Edge Computing. 2019 International Conference on Information Technology and Computer Application (ITCA). :323–326.
Multi-authority attribute-based encryption (MA-ABE) is a promising technique to protect data privacy and achieve fine-grained access control in edge computing for Internet of Things (IoT). However, most of the existing MA-ABE schemes suffer from expensive computational cost in the encryption and decryption phases, which are not practical for resource constrained users in IoT. We propose a large-universe MA-CP-ABE scheme with online/offline encryption and outsourced decryption. In our scheme, most expensive encryption operations have been executed in the user's initialization phase by adding reusable ciphertext pool besides splitting the encryption algorithm to online encryption and offline encryption. Moreover, massive decryption operation are outsourced to the near edge server for reducing the computation overhead of decryption. The proposed scheme is proven statically secure under the q-DPBDHE2 assumption. The performance analysis results indicate that the proposed scheme is efficient and suitable for resource-constrained users in edge computing for IoT.
Yang, Y., McLaughlin, K., Sezer, S., Littler, T., Im, E.G., Pranggono, B., Wang, H.F..  2014.  Multiattribute SCADA-Specific Intrusion Detection System for Power Networks. Power Delivery, IEEE Transactions on. 29:1092-1102.

The increased interconnectivity and complexity of supervisory control and data acquisition (SCADA) systems in power system networks has exposed the systems to a multitude of potential vulnerabilities. In this paper, we present a novel approach for a next-generation SCADA-specific intrusion detection system (IDS). The proposed system analyzes multiple attributes in order to provide a comprehensive solution that is able to mitigate varied cyber-attack threats. The multiattribute IDS comprises a heterogeneous white list and behavior-based concept in order to make SCADA cybersystems more secure. This paper also proposes a multilayer cyber-security framework based on IDS for protecting SCADA cybersecurity in smart grids without compromising the availability of normal data. In addition, this paper presents a SCADA-specific cybersecurity testbed to investigate simulated attacks, which has been used in this paper to validate the proposed approach.

Gayathri, Bhimavarapu, Yammani, Chandrasekhar.  2019.  Multi-Attacking Strategy on Smart Grid with Incomplete Network Information. 2019 8th International Conference on Power Systems (ICPS). :1—5.

The chances of cyber-attacks have been increased because of incorporation of communication networks and information technology in power system. Main objective of the paper is to prove that attacker can launch the attack vector without the knowledge of complete network information and the injected false data can't be detected by power system operator. This paper also deals with analyzing the impact of multi-attacking strategy on the power system. This false data attacks incurs lot of damage to power system, as it misguides the power system operator. Here, we demonstrate the construction of attack vector and later we have demonstrated multiple attacking regions in IEEE 14 bus system. Impact of attack vector on the power system can be observed and it is proved that the attack cannot be detected by power system operator with the help of residue check method.

Yufei Gu, Yangchun Fu, Prakash, A., Zhiqiang Lin, Heng Yin.  2014.  Multi-Aspect, Robust, and Memory Exclusive Guest OS Fingerprinting. Cloud Computing, IEEE Transactions on. 2:380-394.

Precise fingerprinting of an operating system (OS) is critical to many security and forensics applications in the cloud, such as virtual machine (VM) introspection, penetration testing, guest OS administration, kernel dump analysis, and memory forensics. The existing OS fingerprinting techniques primarily inspect network packets or CPU states, and they all fall short in precision and usability. As the physical memory of a VM always exists in all these applications, in this article, we present OS-SOMMELIER+, a multi-aspect, memory exclusive approach for precise and robust guest OS fingerprinting in the cloud. It works as follows: given a physical memory dump of a guest OS, OS-SOMMELIER+ first uses a code hash based approach from kernel code aspect to determine the guest OS version. If code hash approach fails, OS-SOMMELIER+ then uses a kernel data signature based approach from kernel data aspect to determine the version. We have implemented a prototype system, and tested it with a number of Linux kernels. Our evaluation results show that the code hash approach is faster but can only fingerprint the known kernels, and data signature approach complements the code signature approach and can fingerprint even unknown kernels.