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Liu, Jian, Wang, Chen, Chen, Yingying, Saxena, Nitesh.  2017.  VibWrite: Towards Finger-input Authentication on Ubiquitous Surfaces via Physical Vibration. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. :73–87.

The goal of this work is to enable user authentication via finger inputs on ubiquitous surfaces leveraging low-cost physical vibration. We propose VibWrite that extends finger-input authentication beyond touch screens to any solid surface for smart access systems (e.g., access to apartments, vehicles or smart appliances). It integrates passcode, behavioral and physiological characteristics, and surface dependency together to provide a low-cost, tangible and enhanced security solution. VibWrite builds upon a touch sensing technique with vibration signals that can operate on surfaces constructed from a broad range of materials. It is significantly different from traditional password-based approaches, which only authenticate the password itself rather than the legitimate user, and the behavioral biometrics-based solutions, which usually involve specific or expensive hardware (e.g., touch screen or fingerprint reader), incurring privacy concerns and suffering from smudge attacks. VibWrite is based on new algorithms to discriminate fine-grained finger inputs and supports three independent passcode secrets including PIN number, lock pattern, and simple gestures by extracting unique features in the frequency domain to capture both behavioral and physiological characteristics such as contacting area, touching force, and etc. VibWrite is implemented using a single pair of low-cost vibration motor and receiver that can be easily attached to any surface (e.g., a door panel, a desk or an appliance). Our extensive experiments demonstrate that VibWrite can authenticate users with high accuracy (e.g., over 95% within two trials), low false positive rate (e.g., less 3%) and is robust to various types of attacks.

Suwannasa, A., Broadbent, M., Mauthe, A..  2020.  Vicinity-based Replica Finding in Named Data Networking. 2020 International Conference on Information Networking (ICOIN). :146–151.
In Named Data Networking (NDN) architectures, a content object is located according to the content's identifier and can be retrieved from all nodes that hold a replica of the content. The default forwarding strategy of NDN is to forward an Interest packet along the default path from the requester to the server to find a content object according to its name prefix. However, the best path may not be the default path, since content might also be located nearby. Hence, the default strategy could result in a sub-optimal delivery efficiency. To address this issue we introduce a vicinity-based replica finding scheme. This is based on the observation that content objects might be requested several times. Therefore, replicas can be often cached within a particular neighbourhood and thus it might be efficient to specifically look for them in order to improve the content delivery performance. Within this paper, we evaluate the optimal size of the vicinity within which content should be located (i.e. the distance between the requester and its neighbours that are considered within the content search). We also compare the proposed scheme with the default NDN forwarding strategy with respect to replica finding efficiency and network overhead. Using the proposed scheme, we demonstrate that the replica finding mechanism reduces the delivery time effectively with acceptable overhead costs.
Frumento, Enrico, Freschi, Federica, Andreoletti, Davide, Consoli, Angelo.  2017.  Victim Communication Stack (VCS): A Flexible Model to Select the Human Attack Vector. Proceedings of the 12th International Conference on Availability, Reliability and Security. :50:1–50:6.
Information security has rapidly grown to meet the requirements of today services. A solid discipline has been developed as far as technical security is concerned. However, the human layer plays an increasingly decisive role in the managing of Information Technology (IT) systems. The research field that studies the vulnerabilities of the human layer is referred to as Social Engineering, and has not received the same attention of its technical counterpart. We try to partially fill this gap by studying the selection of the Human Attack Vector (HAV), i.e., the path or the means that the attacker uses to compromise the human layer. To this aim, we propose a multilayer model, named Victim Communication Stack (VCS), that provides the key elements to facilitate the choice of the HAV. This work has been carried out under the DOGANA European project.
AL-ATHAMNEH, M., KURUGOLLU, F., CROOKES, D., FARID, M..  2016.  Video Authentication Based on Statistical Local Information. Proceedings of the 9th International Conference on Utility and Cloud Computing. :388–391.

With the outgrowth of video editing tools, video information trustworthiness becomes a hypersensitive field. Today many devices have the capability of capturing digital videos such as CCTV, digital cameras and mobile phones and these videos may transmitted over the Internet or any other non secure channel. As digital video can be used to as supporting evidence, it has to be protected against manipulation or tampering. As most video authentication techniques are based on watermarking and digital signatures, these techniques are effectively used in copyright purposes but difficult to implement in other cases such as video surveillance or in videos captured by consumer's cameras. In this paper we propose an intelligent technique for video authentication which uses the video local information which makes it useful for real world applications. The proposed algorithm relies on the video's statistical local information which was applied on a dataset of videos captured by a range of consumer video cameras. The results show that the proposed algorithm has potential to be a reliable intelligent technique in digital video authentication without the need to use for SVM classifier which makes it faster and less computationally expensive in comparing with other intelligent techniques.

Suo, Yucong, Zhang, Chen, Xi, Xiaoyun, Wang, Xinyi, Zou, Zhiqiang.  2019.  Video Data Hierarchical Retrieval via Deep Hash Method. 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN). :709—714.

Video retrieval technology faces a series of challenges with the tremendous growth in the number of videos. In order to improve the retrieval performance in efficiency and accuracy, a novel deep hash method for video data hierarchical retrieval is proposed in this paper. The approach first uses cluster-based method to extract key frames, which reduces the workload of subsequent work. On the basis of this, high-level semantical features are extracted from VGG16, a widely used deep convolutional neural network (deep CNN) model. Then we utilize a hierarchical retrieval strategy to improve the retrieval performance, roughly can be categorized as coarse search and fine search. In coarse search, we modify simHash to learn hash codes for faster speed, and in fine search, we use the Euclidean distance to achieve higher accuracy. Finally, we compare our approach with other two methods through practical experiments on two videos, and the results demonstrate that our approach has better retrieval effect.

Velmurugan, K.Jayasakthi, Hemavathi, S..  2019.  Video Steganography by Neural Networks Using Hash Function. 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). 1:55–58.

Video Steganography is an extension of image steganography where any kind of file in any extension is hidden into a digital video. The video content is dynamic in nature and this makes the detection of hidden data difficult than other steganographic techniques. The main motive of using video steganography is that the videos can store large amount of data in it. This paper focuses on security using the combination of hybrid neural networks and hash function for determining the best bits in the cover video to embed the secret data. For the embedding process, the cover video and the data to be hidden is uploaded. Then the hash algorithm and neural networks are applied to form the stego video. For the extraction process, the reverse process is applied and the secret data is obtained. All experiments are done using MatLab2016a software.

Rout, S., Mohapatra, R. K..  2020.  Video Steganography using Curvelet Transform and Elliptic Curve Cryptography. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–7.
Video steganography mainly deals with secret data transmission in a carrier video file without being visually noticeable by intruders. Video steganography is preferred over image steganography because a video carries more space in comparison to an image. The main concept of information hiding consists of a cover media, which is a greyscale or a color video, a secret data, which is an image or text, and a stego key. Here a secure video steganography method has been proposed which uses Curvelet Transform for secret data embedding, Elliptic Curve Cryptography for stego key encryption and a threshold algorithm for the determination of the amount of secret data to be encoded per frame. A video is a collection of various frames. The frames are selected randomly from the cover video and the frame number of the respective frames has been indexed in the stego key to find the secret data embedding location. Here, the selection of frames in a sequential manner has been avoided to improve security. For enhanced security, the stego key is also encrypted using Elliptic Curve Integrated Encryption Scheme (ECIES). Fast Discrete Curvelet Transform (FDCT) has been applied to the frames of the cover video and the curvelet coefficients have been modified to obscure the secret data to produce the stego video.
Roy, Subhajit, Mukherjee, Srilekha, Sanyal, Goutam.  2018.  Video Steganography Using Karhunen-LoÈVe Transform. Proceedings of the 2Nd International Conference on Digital Signal Processing. :142–146.

Steganography is the art and science of message hiding i.e. passing confidential message through the unsecure channel in such a way that the existence of secret message is unknown. Image and video are the very popular choice for cover media. Embedding efficiency, payload and robustness against attackers are the main key point to successfully design a steganography algorithm. Current steganography algorithm are lacking behind by preprocessing stage, which includes alteration procedure for both cover media and secret message. We address this problem by proposing a novel method to encode the secret image inside the cover video. Here we use Karhunen-Loeve Transform (KL transform), since it provides the image compression and then after we use our proposed algorithm to embed the secret image on that compact zone. The experimental results show higher data embedding capacity and decode the secret image effectively.

V. Mishra, K. Choudhary, S. Maheshwari.  2015.  "Video Streaming Using Dual-Channel Dual-Path Routing to Prevent Packet Copy Attack". 2015 IEEE International Conference on Computational Intelligence Communication Technology. :645-650.

The video streaming between the sender and the receiver involves multiple unsecured hops where the video data can be illegally copied if the nodes run malicious forwarding logic. This paper introduces a novel method to stream video data through dual channels using dual data paths. The frames' pixels are also scrambled. The video frames are divided into two frame streams. At the receiver side video is re-constructed and played for a limited time period. As soon as small chunk of merged video is played, it is deleted from video buffer. The approach has been tried to formalize and initial simulation has been done over MATLAB. Preliminary results are optimistic and a refined approach may lead to a formal designing of network layer routing protocol with corrections in transport layer.

Ma, Xiaojuan, Cao, Nan.  2017.  Video-Based Evanescent, Anonymous, Asynchronous Social Interaction: Motivation and Adaption to Medium. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. :770–782.

Danmaku is an emerging socio-digital media paradigm that puts anonymous, asynchronous user-generated scrolling comments on videos. (How) can danmaku afford the illusion and realization of social interactions, if at all possible given its interactional incoherence nature? To answer this question, we collect Chinese danmaku users' reflection on their motivations to use this social service and explore the actual practices that meet the needs. According to a preliminary danmaku usage survey, users consider it as an information seeking and emotion venting channel. Through archival analysis of real-world data, we find that danmaku commentaries are relatively short, video-centric, saturated with emotions, and similar in syntactic and semantic features. Users have developed a set of mechanisms adapted to the medium, to leverage such text-based messages to foster interpersonal and hyperpersonal communication for sharing of facts, thoughts, and feelings.

Ouyang, Deqiang, Shao, Jie, Zhang, Yonghui, Yang, Yang, Shen, Heng Tao.  2018.  Video-Based Person Re-Identification via Self-Paced Learning and Deep Reinforcement Learning Framework. Proceedings of the 26th ACM International Conference on Multimedia. :1562–1570.

Person re-identification is an important task in video surveillance, focusing on finding the same person across different cameras. However, most existing methods of video-based person re-identification still have some limitations (e.g., the lack of effective deep learning framework, the robustness of the model, and the same treatment for all video frames) which make them unable to achieve better recognition performance. In this paper, we propose a novel self-paced learning algorithm for video-based person re-identification, which could gradually learn from simple to complex samples for a mature and stable model. Self-paced learning is employed to enhance video-based person re-identification based on deep neural network, so that deep neural network and self-paced learning are unified into one frame. Then, based on the trained self-paced learning, we propose to employ deep reinforcement learning to discard misleading and confounding frames and find the most representative frames from video pairs. With the advantage of deep reinforcement learning, our method can learn strategies to select the optimal frame groups. Experiments show that the proposed framework outperforms the existing methods on the iLIDS-VID, PRID-2011 and MARS datasets.

Naik, N., Shang, C., Shen, Q., Jenkins, P..  2018.  Vigilant Dynamic Honeypot Assisted by Dynamic Fuzzy Rule Interpolation. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). :1731–1738.

Dynamic Fuzzy Rule Interpolation (D-FRI) offers a dynamic rule base for fuzzy systems which is especially useful for systems with changing requirements and limited prior knowledge. This suggests a possible application of D-FRI in the area of network security due to the volatility of the traffic. A honeypot is a valuable tool in the field of network security for baiting attackers and collecting their information. However, typically designed with fewer resources they are not considered as a primary security tool for use in network security. Consequently, such honeypots can be vulnerable to many security attacks. One such attack is a spoofing attack which can cause severe damage to the honeypot, making it inefficient. This paper presents a vigilant dynamic honeypot based on the D-FRI approach for use in predicting and alerting of spoofing attacks on the honeypot. First, it proposes a technique for spoofing attack identification based on the analysis of simulated attack data. Then, the paper employs the identification technique to develop a D-FRI based vigilant dynamic honeypot, allowing the honeypot to predict and alert that a spoofing attack is taking place in the absence of matching rules. The resulting system is capable of learning and maintaining a dynamic rule base for more accurate identification of potential spoofing attacks with respect to the changing traffic conditions of the network.

Mirzamohammadi, Saeed, Amiri Sani, Ardalan.  2016.  Viola: Trustworthy Sensor Notifications for Enhanced Privacy on Mobile Systems. Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services. :263–276.

Modern mobile systems such as smartphones, tablets, and wearables contain a plethora of sensors such as camera, microphone, GPS, and accelerometer. Moreover, being mobile, these systems are with the user all the time, e.g., in user's purse or pocket. Therefore, mobile sensors can capture extremely sensitive and private information about the user including daily conversations, photos, videos, and visited locations. Such a powerful sensing capability raises important privacy concerns. To address these concerns, we believe that mobile systems must be equipped with trustworthy sensor notifications, which use indicators such as LED to inform the user unconditionally when the sensors are on. We present Viola, our design and implementation of trustworthy sensor notifications, in which we leverage two novel solutions. First, we deploy a runtime monitor in low-level system software, e.g., in the operating system kernel or in the hypervisor. The monitor intercepts writes to the registers of sensors and indicators, evaluates them against checks on sensor notification invariants, and rejects those that fail the checks. Second, we use formal verification methods to prove the functional correctness of the compilation of our invariant checks from a high-level language. We demonstrate the effectiveness of Viola on different mobile systems, such as Nexus 5, Galaxy Nexus, and ODROID XU4, and for various sensors and indicators, such as camera, microphone, LED, and vibrator. We demonstrate that Viola incurs almost no overhead to the sensor's performance and incurs only small power consumption overhead.

Ko, Hajoon, Jin, Jiong, Keoh, Sye Loong.  2017.  ViotSOC: Controlling Access to Dynamically Virtualized IoT Services Using Service Object Capability. Proceedings of the 3rd ACM Workshop on Cyber-Physical System Security. :69–80.

Virtualization of Internet of Things(IoT) is a concept of dynamically building customized high-level IoT services which rely on the real time data streams from low-level physical IoT sensors. Security in IoT virtualization is challenging, because with the growing number of available (building block) services, the number of personalizable virtual services grows exponentially. This paper proposes Service Object Capability(SOC) ticket system, a decentralized access control mechanism between servers and clients to efficiently authenticate and authorize each other without using public key cryptography. SOC supports decentralized partial delegation of capabilities specified in each server/client ticket. Unlike PKI certificates, SOC's authentication time and handshake packet overhead stays constant regardless of each capability's delegation hop distance from the root delegator. The paper compares SOC's security benefifits with Kerberos and the experimental results show SOC's authentication incurs significantly less time packet overhead compared against those from other mechanisms based on RSA-PKI and ECC-PKI algorithms. SOC is as secure as, and more efficient and suitable for IoT environments, than existing PKIs and Kerberos.

Jangra, Ajay, Singh, Niharika, Lakhina, Upasana.  2016.  VIP: Verification and Identification Protective Data Handling Layer Implementation to Achieve MVCC in Cloud Computing. Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies. :124:1–124:6.

Over transactional database systems MultiVersion concurrency control is maintained for secure, fast and efficient access to the shared data file implementation scenario. An effective coordination is supposed to be set up between owners and users also the developers & system operators, to maintain inter-cloud & intra-cloud communication Most of the services & application offered in cloud world are real-time, which entails optimized compatibility service environment between master and slave clusters. In the paper, offered methodology supports replication and triggering methods intended for data consistency and dynamicity. Where intercommunication between different clusters is processed through middleware besides slave intra-communication is handled by verification & identification protection. The proposed approach incorporates resistive flow to handle high impact systems that identifies and verifies multiple processes. Results show that the new scheme reduces the overheads from different master and slave servers as they are co-located in clusters which allow increased horizontal and vertical scalability of resources.

Liu, Renju, Srivastava, Mani.  2018.  VirtSense: Virtualize Sensing Through ARM TrustZone on Internet-of-Things. Proceedings of the 3rd Workshop on System Software for Trusted Execution. :2–7.
Internet-of-Things (IoTs) are becoming more and more popular in our life. IoT devices are generally designed for sensing or actuation purposes. However, the current sensing system on IoT devices lacks the understanding of sensing needs, which diminishes the sensing flexibility, isolation, and security when multiple sensing applications need to use sensor resources. In this work, we propose VirtSense, an ARM TrustZone based virtual sensing system, to provide each sensing application a virtual sensor instance, which further enables a safe, flexible and isolated sensing environment on the IoT devices. Our preliminary results show that VirtSense: 1) can provide virtual sensor instance for each sensing application so that the sensing needs of each application will be satisfied without affecting others; 2) is able to enforce access control policy even under an untrusted environment.
Murillo, Andrés Felipe, Cómbita, Luis Francisco, Gonzalez, Andrea Calderón, Rueda, Sandra, Cardenas, Alvaro A., Quijano, Nicanor.  2018.  A Virtual Environment for Industrial Control Systems: A Nonlinear Use-Case in Attack Detection, Identification, and Response. Proceedings of the 4th Annual Industrial Control System Security Workshop. :25–32.

The integration of modern information technologies with industrial control systems has created an enormous interest in the security of industrial control, however, given the cost, variety, and industry practices, it is hard for researchers to test and deploy security solutions in real-world systems. Industrial control testbeds can be used as tools to test security solutions before they are deployed, and in this paper we extend our previous work to develop open-source virtual industrial control testbeds where computing and networking components are emulated and virtualized, and the physical system is simulated through differential equations. In particular, we implement a nonlinear control system emulating a three-water tank with the associated sensors, PLCs, and actuators that communicate through an emulated network. In addition, we design unknown input observers (UIO) to not only detect that an attack is occurring, but also to identify the source of the malicious false data injections and mitigate its impact. Our system is available through Github to the academic community.

Bose, A. J., Aarabi, P..  2019.  Virtual Fakes: DeepFakes for Virtual Reality. 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP). :1—1.
The proliferation of data and computational resources has led into many advancements in computer vision for facial data including easily replacing a face in one video with another one, the so called DeepFake. In this paper, we apply techniques to generate DeepFakes for virtual reality applications. We empirically validate our method by generating, for the first time, Deep Fake videos in virtual reality.
Palmer, D., Fazzari, S., Wartenberg, S..  2017.  A virtual laboratory approach for risk assessment of aerospace electronics trust techniques. 2017 IEEE Aerospace Conference. :1–8.

This paper describes a novel aerospace electronic component risk assessment methodology and supporting virtual laboratory structure designed to augment existing supply chain management practices and aid in Microelectronics Trust Assurance. This toolkit and methodology applies structure to the unclear and evolving risk assessment problem, allowing quantification of key risks affecting both advanced and obsolete systems that rely on semiconductor technologies. The impacts of logistics & supply chain risk, technology & counterfeit risk, and faulty component risk on trusted and non-trusted procurement options are quantified. The benefits of component testing on part reliability are assessed and incorporated into counterfeit mitigation calculations. This toolkit and methodology seek to assist acquisition staff by providing actionable decision data regarding the increasing threat of counterfeit components by assessing the risks faced by systems, identifying mitigation strategies to reduce this risk, and resolving these risks through the optimal test and procurement path based on the component criticality risk tolerance of the program.

Liu, D. Y. W., Leung, A. C. Y., Au, M. H., Luo, X., Chiu, P. H. P., Im, S. W. T., Lam, W. W. M..  2019.  Virtual Laboratory: Facilitating Teaching and Learning in Cybersecurity for Students with Diverse Disciplines. 2019 IEEE International Conference on Engineering, Technology and Education (TALE). :1—6.

Cybersecurity education is a pressing need, when computer systems and mobile devices are ubiquitous and so are the associated threats. However, in the teaching and learning process of cybersecurity, it is challenging when the students are from diverse disciplines with various academic backgrounds. In this project, a number of virtual laboratories are developed to facilitate the teaching and learning process in a cybersecurity course. The aim of the laboratories is to strengthen students’ understanding of cybersecurity topics, and to provide students hands-on experience of encountering various security threats. The results of this project indicate that virtual laboratories do facilitate the teaching and learning process in cybersecurity for diverse discipline students. Also, we observed that there is an underestimation of the difficulty of studying cybersecurity by the students due to the general image of cybersecurity in public, which had a negative impact on the student’s interest in studying cybersecurity.

Lin, Hsin-Peng, Shih, Yuan-Yao, Pang, Ai-Chun, Lou, Yuan-Yao.  2016.  A Virtual Local-hub Solution with Function Module Sharing for Wearable Devices. Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. :278–286.

Wearable devices, which are small electronic devices worn on a human body, are equipped with low level of processing and storage capacities and offer some types of integrated functionalities. Recently, wearable device is becoming increasingly popular, various kinds of wearable device are launched in the market; however, wearable devices require a powerful local-hub, most are smartphone, to replenish processing and storage capacities for advanced functionalities. Sometime it may be inconvenient to carry the local-hub (smartphone); thus, many wearable devices are equipped with Wi-Fi interface, enabling them to exchange data with local-hub though the Internet when the local-hub is not nearby. However, this results in long response time and restricted functionalities. In this paper, we present a virtual local-hub solution, which utilizes network equipment nearby (e.g., Wi-Fi APs) as the local-hub. Since migrating all applications serving the wearable devices respectively takes too much networking and storage resources, the proposed solution deploys function modules to multiple network nodes and enables remote function module sharing among different users and applications. To reduce the impact of the solution on the network bandwidth, we propose a heuristic algorithm for function module allocation with the objective of minimizing total bandwidth consumption. We conduct series of experiments, and the results show that the proposed solution can reduce the bandwidth consumption by up to half and still serve all requests given a large number of service requests.

Li, Zhixin, Liu, Lei, Kong, Degang.  2019.  Virtual Machine Failure Prediction Method Based on AdaBoost-Hidden Markov Model. 2019 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS). :700–703.

The failure prediction method of virtual machines (VM) guarantees reliability to cloud platforms. However, the uncertainty of VM security state will affect the reliability and task processing capabilities of the entire cloud platform. In this study, a failure prediction method of VM based on AdaBoost-Hidden Markov Model was proposed to improve the reliability of VMs and overall performance of cloud platforms. This method analyzed the deep relationship between the observation state and the hidden state of the VM through the hidden Markov model, proved the influence of the AdaBoost algorithm on the hidden Markov model (HMM), and realized the prediction of the VM failure state. Results show that the proposed method adapts to the complex dynamic cloud platform environment, can effectively predict the failure state of VMs, and improve the predictive ability of VM security state.

Laurén, Samuel, Leppänen, Ville.  2018.  Virtual Machine Introspection Based Cloud Monitoring Platform. Proceedings of the 19th International Conference on Computer Systems and Technologies. :104–109.

Virtual Machine Introspection (VMI) is an emerging family of techniques for extracting data from virtual machines without the use of active monitoring probes within the target machines themselves. In VMI based systems, the data is collected at the hypervisor-level by analyzing the state of virtual machines. This has the benefit of making collection harder to detect and block by malware as there is nothing in the machine indicating that monitoring is taking place. In this paper we present Nitro Web, a web-based monitoring system for virtual machines that uses virtual machine introspection for data collection. The platform is capable of detecting and visualizing system call activity taking place within virtual machines in real-time. The secondary purpose of this paper is to offer an introduction to Nitro virtual machine introspection framework that we have been involved in developing. In this paper, we reflect on how Nitro Framework can be used for building applications making use of VMI data.

Sentanoe, Stewart, Taubmann, Benjamin, Reiser, Hans P..  2017.  Virtual Machine Introspection Based SSH Honeypot. Proceedings of the 4th Workshop on Security in Highly Connected IT Systems. :13–18.

A honeypot provides information about the new attack and exploitation methods and allows analyzing the adversary's activities during or after exploitation. One way of an adversary to communicate with a server is via secure shell (SSH). SSH provides secure login, file transfer, X11 forwarding, and TCP/IP connections over untrusted networks. SSH is a preferred target for attacks, as it is frequently used with password-based authentication, and weak passwords are easily exploited using brute-force attacks. In this paper, we introduce a Virtual Machine Introspection based SSH honeypot. We discuss the design of the system and how to extract valuable information such as the credential used by the attacker and the entered commands. Our experiments show that the system is able to detect the adversary's activities during and after exploitation, and it has advantages compared to currently used SSH honeypot approaches.

Li, Xiao-Ke, Gu, Chun-Hua, Yang, Ze-Ping, Chang, Yao-Hui.  2015.  Virtual machine placement strategy based on discrete firefly algorithm in cloud environments. 2015 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). :61–66.

Because of poor performance of heuristic algorithms on virtual machine placement problem in cloud environments, a multi-objective constraint optimization model of virtual machine placement is presented, which taking energy consumption and resource wastage as the objective. We solve the model based on the proposed discrete firefly algorithm. It takes firefly's location as the placement result, brightness as the objective value. Its movement strategy makes darker fireflies move to brighter fireflies in solution space. The continuous position after movement is discretized by the proposed discrete strategy. In order to speed up the search for solution, the local search mechanism for the optimal solution is introduced. The experimental results in OpenStack cloud platform show that the proposed algorithm makes less energy consumption and resource wastage compared with other algorithms.