Visible to the public Biblio

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Fargo, Farah, Franza, Olivier, Tunc, Cihan, Hariri, Salim.  2020.  VM Introspection-based Allowlisting for IaaS. 2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS). :1—4.
Cloud computing has become the main backend of the IT infrastructure as it provides ubiquitous and on-demand computing to serve to a wide range of users including end-users and high-performance demanding agencies. The users can allocate and free resources allocated for their Virtual Machines (VMs) as needed. However, with the rapid growth of interest in cloud computing systems, several issues have arisen especially in the domain of cybersecurity. It is a known fact that not only the malicious users can freely allocate VMs, but also they can infect victims' VMs to run their own tools that include cryptocurrency mining, ransomware, or cyberattacks against others. Even though there exist intrusion detection systems (IDS), running an IDS on every VM can be a costly process and it would require fine configuration that only a small subset of the cloud users are knowledgeable about. Therefore, to overcome this challenge, in this paper we present a VM introspection based allowlisting method to be deployed and managed directly by the cloud providers to check if there are any malicious software running on the VMs with minimum user intervention. Our middleware monitors the processes and if it detects unknown events, it will notify the users and/or can take action as needed.
Sato, Masaya, Taniguchi, Hideo, Nakamura, Ryosuke.  2020.  Virtual Machine Monitor-based Hiding Method for Access to Debug Registers. 2020 Eighth International Symposium on Computing and Networking (CANDAR). :209—214.
To secure a guest operating system running on a virtual machine (VM), a monitoring method using hardware breakpoints by a virtual machine monitor is required. However, debug registers are visible to guest operating systems; thus, malicious programs on a guest operating system can detect or disable the monitoring method. This paper presents a method to hide access to debug registers from programs running on a VM. Our proposed method detects programs' access to debug registers and disguises the access as having succeeded. The register's actual value is not visible or modifiable to programs, so the monitoring method is hidden. This paper presents the basic design and evaluation results of our method.
Dovgalyuk, Pavel, Vasiliev, Ivan, Fursova, Natalia, Dmitriev, Denis, Abakumov, Mikhail, Makarov, Vladimir.  2020.  Non-intrusive Virtual Machine Analysis and Reverse Debugging with SWAT. 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS). :196—203.
This paper presents SWAT - System-Wide Analysis Toolkit. It is based on open source emulation and debugging projects and implements the approaches for non-intrusive system-wide analysis and debugging: lightweight OS-agnostic virtual machine introspection, full system execution replay, non-intrusive debugging with WinDbg, and full system reverse debugging. These features are based on novel non-intrusive introspection and reverse debugging methods. They are useful for stealth debugging and analysis of the platforms with custom kernels. SWAT includes multi-platform emulator QEMU with additional instrumentation and debugging features, GUI for convenient QEMU setup and execution, QEMU plugin for non-intrusive introspection, and modified version of GDB. Our toolkit may be useful for the developers of the virtual platforms, emulators, and firmwares/drivers/operating systems. Virtual machine intospection approach does not require loading any guest agents and source code of the OS. Therefore it may be applied to ROM-based guest systems and enables using of record/replay of the system execution. This paper includes the description of SWAT components, analysis methods, and some SWAT use cases.
Flores, Hugo, Tran, Vincent, Tang, Bin.  2020.  PAM PAL: Policy-Aware Virtual Machine Migration and Placement in Dynamic Cloud Data Centers. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :2549—2558.
We focus on policy-aware data centers (PADCs), wherein virtual machine (VM) traffic traverses a sequence of middleboxes (MBs) for security and performance purposes, and propose two new VM placement and migration problems. We first study PAL: policy-aware virtual machine placement. Given a PADC with a data center policy that communicating VM pairs must satisfy, the goal of PAL is to place the VMs into the PADC to minimize their total communication cost. Due to dynamic traffic loads in PADCs, however, above VM placement may no longer be optimal after some time. We thus study PAM: policy-aware virtual machine migration. Given an existing VM placement in the PADC and dynamic traffic rates among communicating VMs, PAM migrates VMs in order to minimize the total cost of migration and communication of the VM pairs. We design optimal, approximation, and heuristic policyaware VM placement and migration algorithms. Our experiments show that i) VM migration is an effective technique, reducing total communication cost of VM pairs by 25%, ii) our PAL algorithms outperform state-of-the-art VM placement algorithm that is oblivious to data center policies by 40-50%, and iii) our PAM algorithms outperform the only existing policy-aware VM migration scheme by 30%.
Long, Vu Duc, Duong, Ta Nguyen Binh.  2020.  Group Instance: Flexible Co-Location Resistant Virtual Machine Placement in IaaS Clouds. 2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). :64—69.
This paper proposes and analyzes a new virtual machine (VM) placement technique called Group Instance to deal with co-location attacks in public Infrastructure-as-a-Service (IaaS) clouds. Specifically, Group Instance organizes cloud users into groups with pre-determined sizes set by the cloud provider. Our empirical results obtained via experiments with real-world data sets containing million of VM requests have demonstrated the effectiveness of the new technique. In particular, the advantages of Group Instance are three-fold: 1) it is simple and highly configurable to suit the financial and security needs of cloud providers, 2) it produces better or at least similar performance compared to more complicated, state-of-the-art algorithms in terms of resource utilization and co-location security, and 3) it does not require any modifications to the underlying infrastructures of existing public cloud services.
Long, Saiqin, Li, Zhetao, Xing, Yun, Tian, Shujuan, Li, Dongsheng, Yu, Rong.  2020.  A Reinforcement Learning-Based Virtual Machine Placement Strategy in Cloud Data Centers. :223—230.
{With the widespread use of cloud computing, energy consumption of cloud data centers is increasing which mainly comes from IT equipment and cooling equipment. This paper argues that once the number of virtual machines on the physical machines reaches a certain level, resource competition occurs, resulting in a performance loss of the virtual machines. Unlike most papers, we do not impose placement constraints on virtual machines by giving a CPU cap to achieve the purpose of energy savings in cloud data centers. Instead, we use the measure of performance loss to weigh. We propose a reinforcement learning-based virtual machine placement strategy(RLVMP) for energy savings in cloud data centers. The strategy considers the weight of virtual machine performance loss and energy consumption, which is finally solved with the greedy strategy. Simulation experiments show that our strategy has a certain improvement in energy savings compared with the other algorithms.
Chaturvedi, Amit Kumar, Kumar, Punit, Sharma, Kalpana.  2020.  Proposing Innovative Intruder Detection System for Host Machines in Cloud Computing. 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART). :292—296.
There is very significant role of Virtualization in cloud computing. The physical hardware in the cloud computing reside with the host machine and the virtualization software runs on it. The virtualization allows virtual machines to exist. The host machine shares its physical components such as memory, storage, and processor ultimately to handle the needs of the virtual machines. If an attacker effectively compromises one VM, it could outbreak others on the same host on the network over long periods of time. This is an gradually more popular method for cross-virtual-machine attacks, since traffic between VMs cannot be examined by standard IDS/IPS software programs. As we know that the cloud environment is distributed in nature and hence more susceptible to various types of intrusion attacks which include installing malicious software and generating backdoors. In a cloud environment, where organizations have hosted important and critical data, the security of underlying technologies becomes critical. To alleviate the hazard to cloud environments, Intrusion Detection Systems (IDS) are a cover of defense. In this paper, we are proposing an innovative model for Intrusion Detection System for securing Host machines in cloud infrastructure. This proposed IDS has two important features: (1) signature based and (2) prompt alert system.
SANE, Bernard Ousmane, BA, Mandicou, FALL, Doudou, KASHIHARA, Shigeru, TAENAKA, Yuzo, NIANG, Ibrahima, Kadobayashi, Youki.  2020.  Solving the Interdependency Problem: A Secure Virtual Machine Allocation Method Relying on the Attacker’s Efficiency and Coverage. 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). :440—449.
Cloud computing dominates the information communication and technology landscape despite the presence of lingering security issues such as the interdependency problem. The latter is a co-residence conundrum where the attacker successfully compromises his target virtual machine by first exploiting the weakest (in terms of security) virtual machine that is hosted in the same server. To tackle this issue, we propose a novel virtual machine allocation policy that is based on the attacker's efficiency and coverage. By default, our allocation policy considers all legitimate users as attackers and then proceeds to host the users' virtual machines to the server where their efficiency and/or coverage are the smallest. Our simulation results show that our proposal performs better than the existing allocation policies that were proposed to tackle the same issue, by reducing the attacker's possibilities to zero and by using between 30 - 48% less hosts.
Mengli, Zhou, Fucai, Chen, Wenyan, Liu, Hao, Liang.  2020.  Negative Feedback Dynamic Scheduling Algorithm based on Mimic Defense in Cloud Environment. 2020 IEEE 6th International Conference on Computer and Communications (ICCC). :2265–2270.
The virtualization technology in cloud environment brings some data and privacy security issues to users. Aiming at the problems of virtual machines singleness, homogeneity and static state in cloud environment, a negative feedback dynamic scheduling algorithm is proposed. This algorithm is based on mimic defense and creates multiple virtual machines to complete user request services together through negative feedback control mechanism which can achieve real-time monitor of the running state of virtual machines. When virtual machines state is found to be inconsistent, this algorithm will dynamically change its execution environment, resulting in the attacker's information collection and vulnerability exploitation process being disrupting. Experiments show that the algorithm can better solve security threats caused by the singleness, homogeneity and static state of virtual machines in the cloud, and improve security and reliability of cloud users.
Chalkiadakis, Nikolaos, Deyannis, Dimitris, Karnikis, Dimitris, Vasiliadis, Giorgos, Ioannidis, Sotiris.  2020.  The Million Dollar Handshake: Secure and Attested Communications in the Cloud. 2020 IEEE 13th International Conference on Cloud Computing (CLOUD). :63—70.

The number of applications and services that are hosted on cloud platforms is constantly increasing. Nowadays, more and more applications are hosted as services on cloud platforms, co-existing with other services in a mutually untrusted environment. Facilities such as virtual machines, containers and encrypted communication channels aim to offer isolation between the various applications and protect sensitive user data. However, such techniques are not always able to provide a secure execution environment for sensitive applications nor they offer guarantees that data are not monitored by an honest but curious provider once they reach the cloud infrastructure. The recent advancements of trusted execution environments within commodity processors, such as Intel SGX, provide a secure reverse sandbox, where code and data are isolated even from the underlying operating system. Moreover, Intel SGX provides a remote attestation mechanism, allowing the communicating parties to verify their identity as well as prove that code is executed on hardware-assisted software enclaves. Many approaches try to ensure code and data integrity, as well as enforce channel encryption schemes such as TLS, however, these techniques are not enough to achieve complete isolation and secure communications without hardware assistance or are not efficient in terms of performance. In this work, we design and implement a practical attestation system that allows the service provider to offer a seamless attestation service between the hosted applications and the end clients. Furthermore, we implement a novel caching system that is capable to eliminate the latencies introduced by the remote attestation process. Our approach allows the parties to attest one another before each communication attempt, with improved performance when compared to a standard TLS handshake.

Yamato, K., Kourai, K., Saadawi, T..  2020.  Transparent IDS Offloading for Split-Memory Virtual Machines. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). :833—838.
To enable virtual machines (VMs) with a large amount of memory to be flexibly migrated, split migration has been proposed. It divides a large-memory VM into small pieces and transfers them to multiple hosts. After the migration, the VM runs across those hosts and exchanges memory data between hosts using remote paging. For such a split-memory VM, however, it becomes difficult to securely run intrusion detection systems (IDS) outside the VM using a technique called IDS offloading. This paper proposes VMemTrans to support transparent IDS offloading for split-memory VMs. In VMemTrans, offloaded IDS can monitor a split-memory VM as if that memory were not distributed. To achieve this, VMemTrans enables IDS running in one host to transparently access VM's remote memory. To consider a trade-off, it provides two methods for obtaining memory data from remote hosts: self paging and proxy paging. We have implemented VMemTrans in KVM and compared the execution performance between the two methods.
Usman, S., Winarno, I., Sudarsono, A..  2020.  Implementation of SDN-based IDS to protect Virtualization Server against HTTP DoS attacks. 2020 International Electronics Symposium (IES). :195—198.
Virtualization and Software-defined Networking (SDN) are emerging technologies that play a major role in cloud computing. Cloud computing provides efficient utilization, high performance, and resource availability on demand. However, virtualization environments are vulnerable to various types of intrusion attacks that involve installing malicious software and denial of services (DoS) attacks. Utilizing SDN technology, makes the idea of SDN-based security applications attractive in the fight against DoS attacks. Network intrusion detection system (IDS) which is used to perform network traffic analysis as a detection system implemented on SDN networks to protect virtualization servers from HTTP DoS attacks. The experimental results show that SDN-based IDS is able to detect and mitigate HTTP DoS attacks effectively.
Fadhilah, D., Marzuki, M. I..  2020.  Performance Analysis of IDS Snort and IDS Suricata with Many-Core Processor in Virtual Machines Against Dos/DDoS Attacks. 2020 2nd International Conference on Broadband Communications, Wireless Sensors and Powering (BCWSP). :157—162.
The rapid development of technology makes it possible for a physical machine to be converted into a virtual machine, which can operate multiple operating systems that are running simultaneously and connected to the internet. DoS/DDoS attacks are cyber-attacks that can threaten the telecommunications sector because these attacks cause services to be disrupted and be difficult to access. There are several software tools for monitoring abnormal activities on the network, such as IDS Snort and IDS Suricata. From previous studies, IDS Suricata is superior to IDS Snort version 2 because IDS Suricata already supports multi-threading, while IDS Snort version 2 still only supports single-threading. This paper aims to conduct tests on IDS Snort version 3.0 which already supports multi-threading and IDS Suricata. This research was carried out on a virtual machine with 1 core, 2 core, and 4 core processor settings for CPU, memory, and capture packet attacks on IDS Snort version 3.0 and IDS Suricata. The attack scenario is divided into 2 parts: DoS attack scenario using 1 physical computer, and DDoS attack scenario using 5 physical computers. Based on overall testing, the results are: In general, IDS Snort version 3.0 is better than IDS Suricata. This is based on the results when using a maximum of 4 core processor, in which IDS Snort version 3.0 CPU usage is stable at 55% - 58%, a maximum memory of 3,000 MB, can detect DoS attacks with 27,034,751 packets, and DDoS attacks with 36,919,395 packets. Meanwhile, different results were obtained by IDS Suricata, in which CPU usage is better compared to IDS Snort version 3.0 with only 10% - 40% usage, and a maximum memory of 1,800 MB. However, the capabilities of detecting DoS attacks are smaller with 3,671,305 packets, and DDoS attacks with a total of 7,619,317 packets on a TCP Flood attack test.
Moustafa, N., Keshky, M., Debiez, E., Janicke, H..  2020.  Federated TONİoT Windows Datasets for Evaluating AI-Based Security Applications. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :848—855.

Existing cyber security solutions have been basically developed using knowledge-based models that often cannot trigger new cyber-attack families. With the boom of Artificial Intelligence (AI), especially Deep Learning (DL) algorithms, those security solutions have been plugged-in with AI models to discover, trace, mitigate or respond to incidents of new security events. The algorithms demand a large number of heterogeneous data sources to train and validate new security systems. This paper presents the description of new datasets, the so-called ToNİoT, which involve federated data sources collected from Telemetry datasets of IoT services, Operating system datasets of Windows and Linux, and datasets of Network traffic. The paper introduces the testbed and description of TONİoT datasets for Windows operating systems. The testbed was implemented in three layers: edge, fog and cloud. The edge layer involves IoT and network devices, the fog layer contains virtual machines and gateways, and the cloud layer involves cloud services, such as data analytics, linked to the other two layers. These layers were dynamically managed using the platforms of software-Defined Network (SDN) and Network-Function Virtualization (NFV) using the VMware NSX and vCloud NFV platform. The Windows datasets were collected from audit traces of memories, processors, networks, processes and hard disks. The datasets would be used to evaluate various AI-based cyber security solutions, including intrusion detection, threat intelligence and hunting, privacy preservation and digital forensics. This is because the datasets have a wide range of recent normal and attack features and observations, as well as authentic ground truth events. The datasets can be publicly accessed from this link [1].

Kishimoto, K., Taniguchi, Y., Iguchi, N..  2020.  A Practical Exercise System Using Virtual Machines for Learning Cross-Site Scripting Countermeasures. 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). :1—2.

Cross-site scripting (XSS) is an often-occurring major attack that developers should consider when developing web applications. We develop a system that can provide practical exercises for learning how to create web applications that are secure against XSS. Our system utilizes free software and virtual machines, allowing low-cost, safe, and practical exercises. By using two virtual machines as the web server and the attacker host, the learner can conduct exercises demonstrating both XSS countermeasures and XSS attacks. In our system, learners use a web browser to learn and perform exercises related to XSS. Experimental evaluations confirm that the proposed system can support learning of XSS countermeasures.

Devi, B. T., Shitharth, S., Jabbar, M. A..  2020.  An Appraisal over Intrusion Detection Systems in Cloud Computing Security Attacks. 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). :722—727.

Cloud computing provides so many groundbreaking advantages over native computing servers like to improve capacity and decrease costs, but meanwhile, it carries many security issues also. In this paper, we find the feasible security attacks made about cloud computing, including Wrapping, Browser Malware-Injection and Flooding attacks, and also problems caused by accountability checking. We have also analyzed the honey pot attack and its procedural intrusion way into the system. This paper on overall deals with the most common security breaches in cloud computing and finally honey pot, in particular, to analyze its intrusion way. Our major scope is to do overall security, analyze in the cloud and then to take up with a particular attack to deal with granular level. Honey pot is the one such attack that is taken into account and its intrusion policies are analyzed. The specific honey pot algorithm is in the queue as the extension of this project in the future.

Zeid, R. B., Moubarak, J., Bassil, C..  2020.  Investigating The Darknet. 2020 International Wireless Communications and Mobile Computing (IWCMC). :727—732.

Cybercrime is growing dramatically in the technological world nowadays. World Wide Web criminals exploit the personal information of internet users and use them to their advantage. Unethical users leverage the dark web to buy and sell illegal products or services and sometimes they manage to gain access to classified government information. A number of illegal activities that can be found in the dark web include selling or buying hacking tools, stolen data, digital fraud, terrorists activities, drugs, weapons, and more. The aim of this project is to collect evidence of any malicious activity in the dark web by using computer security mechanisms as traps called honeypots.

Huang, H., Zhou, S., Lin, J., Zhang, K., Guo, S..  2020.  Bridge the Trustworthiness Gap amongst Multiple Domains: A Practical Blockchain-based Approach. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1–6.
In isolated network domains, global trustworthiness (e.g., consistent network view) is critical to the multiple-domain business partners who aim to perform the trusted corporations depending on each isolated network view. However, to achieve such global trustworthiness across distributed network domains is a challenge. This is because when multiple-domain partners are required to exchange their local domain views with each other, it is difficult to ensure the data trustworthiness among them. In addition, the isolated domain view in each partner is prone to be destroyed by malicious falsification attacks. To this end, we propose a blockchain-based approach that can ensure the trustworthiness among multiple-party domains. In this paper, we mainly present the design and implementation of the proposed trustworthiness-protection system. A cloud-based prototype and a local testbed are developed based on Ethereum. Finally, experimental results demonstrate the effectiveness of the proposed prototype and testbed.
Singh, M., Butakov, S., Jaafar, F..  2018.  Analyzing Overhead from Security and Administrative Functions in Virtual Environment. 2018 International Conference on Platform Technology and Service (PlatCon). :1—6.
The paper provides an analysis of the performance of an administrative component that helps the hypervisor to manage the resources of guest operating systems under fluctuation workload. The additional administrative component provides an extra layer of security to the guest operating systems and system as a whole. In this study, an administrative component was implemented by using Xen-hypervisor based para-virtualization technique and assigned some additional roles and responsibilities that reduce hypervisor workload. The study measured the resource utilizations of an administrative component when excessive input/output load passes passing through the system. Performance was measured in terms of bandwidth and CPU utilisation Based on the analysis of administrative component performance recommendations have been provided with the goal to improve system availability. Recommendations included detection of the performance saturation point that indicates the necessity to start load balancing procedures for the administrative component in the virtualized environment.
Deng, Y., Lu, D., Chung, C., Huang, D., Zeng, Z..  2018.  Personalized Learning in a Virtual Hands-on Lab Platform for Computer Science Education. 2018 IEEE Frontiers in Education Conference (FIE). :1—8.

This Innovate Practice full paper presents a cloud-based personalized learning lab platform. Personalized learning is gaining popularity in online computer science education due to its characteristics of pacing the learning progress and adapting the instructional approach to each individual learner from a diverse background. Among various instructional methods in computer science education, hands-on labs have unique requirements of understanding learner's behavior and assessing learner's performance for personalization. However, it is rarely addressed in existing research. In this paper, we propose a personalized learning platform called ThoTh Lab specifically designed for computer science hands-on labs in a cloud environment. ThoTh Lab can identify the learning style from student activities and adapt learning material accordingly. With the awareness of student learning styles, instructors are able to use techniques more suitable for the specific student, and hence, improve the speed and quality of the learning process. With that in mind, ThoTh Lab also provides student performance prediction, which allows the instructors to change the learning progress and take other measurements to help the students timely. For example, instructors may provide more detailed instructions to help slow starters, while assigning more challenging labs to those quick learners in the same class. To evaluate ThoTh Lab, we conducted an experiment and collected data from an upper-division cybersecurity class for undergraduate students at Arizona State University in the US. The results show that ThoTh Lab can identify learning style with reasonable accuracy. By leveraging the personalized lab platform for a senior level cybersecurity course, our lab-use study also shows that the presented solution improves students engagement with better understanding of lab assignments, spending more effort on hands-on projects, and thus greatly enhancing learning outcomes.

Li, Huhua, Zhan, Dongyang, Liu, Tianrui, Ye, Lin.  2019.  Using Deep-Learning-Based Memory Analysis for Malware Detection in Cloud. 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems Workshops (MASSW). :1–6.
Malware is one of the biggest threats in cloud computing. Malware running inside virtual machines or containers could steal critical information or continue to attack other cloud nodes. To detect malware in cloud, especially zero-day malware, signature-and machine-learning-based approaches are proposed to analyze the execution binary. However, malicious binary files may not permanently be stored in the file system of virtual machine or container, periodically scanner may not find the target files. Dynamic analysis approach usually introduce run-time overhead to virtual machines, which is not widely used in cloud. To solve these problems, we propose a memory analysis approach to detect malware, employing the deep learning technology. The system analyzes the memory image periodically during malware execution, which will not introduce run-time overhead. We first extract the memory snapshot from running virtual machines or containers. Then, the snapshot is converted to a grayscale image. Finally, we employ CNN to detect malware. In the learning phase, malicious and benign software are trained. In the testing phase, we test our system with real-world malwares.
Sethi, Kamalakanta, Kumar, Rahul, Sethi, Lingaraj, Bera, Padmalochan, Patra, Prashanta Kumar.  2019.  A Novel Machine Learning Based Malware Detection and Classification Framework. 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1–4.
As time progresses, new and complex malware types are being generated which causes a serious threat to computer systems. Due to this drastic increase in the number of malware samples, the signature-based malware detection techniques cannot provide accurate results. Different studies have demonstrated the proficiency of machine learning for the detection and classification of malware files. Further, the accuracy of these machine learning models can be improved by using feature selection algorithms to select the most essential features and reducing the size of the dataset which leads to lesser computations. In this paper, we have developed a machine learning based malware analysis framework for efficient and accurate malware detection and classification. We used Cuckoo sandbox for dynamic analysis which executes malware in an isolated environment and generates an analysis report based on the system activities during execution. Further, we propose a feature extraction and selection module which extracts features from the report and selects the most important features for ensuring high accuracy at minimum computation cost. Then, we employ different machine learning algorithms for accurate detection and fine-grained classification. Experimental results show that we got high detection and classification accuracy in comparison to the state-of-the-art approaches.
Chen, Cheng-Yu, Hsiao, Shun-Wen.  2019.  IoT Malware Dynamic Analysis Profiling System and Family Behavior Analysis. 2019 IEEE International Conference on Big Data (Big Data). :6013–6015.
Not only the number of deployed IoT devices increases but also that of IoT malware increases. We eager to understand the threat made by IoT malware but we lack tools to observe, analyze and detect them. We design and implement an automatic, virtual machine-based profiling system to collect valuable IoT malware behavior, such as API call invocation, system call execution, etc. In addition to conventional profiling methods (e.g., strace and packet capture), the proposed profiling system adapts virtual machine introspection based API hooking technique to intercept API call invocation by malware, so that our introspection would not be detected by IoT malware. We then propose a method to convert the multiple sequential data (API calls) to a family behavior graph for further analysis.
Zhang, Xiao, Wang, Yanqiu, Wang, Qing, Zhao, Xiaonan.  2019.  A New Approach to Double I/O Performance for Ceph Distributed File System in Cloud Computing. 2019 2nd International Conference on Data Intelligence and Security (ICDIS). :68—75.
Block storage resources are essential in an Infrastructure-as-a-Service(IaaS) cloud computing system. It is used for storing virtual machines' images. It offers persistent storage service even the virtual machine is off. Distribute storage systems are used to provide block storage services in IaaS, such as Amazon EBS, Cinder, Ceph, Sheepdog. Ceph is widely used as the backend block storage service of OpenStack platform. It converts block devices into objects with the same size and saves them on the local file system. The performance of block devices provided by Ceph is only 30% of hard disks in many cases. One of the key issues that affect the performance of Ceph is the three replicas for fault tolerance. But our research finds that replicas are not the real reason slow down the performance. In this paper, we present a new approach to accelerate the IO operations. The experiment results show that by using our storage engine, Ceph can offer faster IO performance than the hard disk in most cases. Our new storage engine provides more than three times up than the original one.
Duncan, Adrian, Creese, Sadie, Goldsmith, Michael.  2019.  A Combined Attack-Tree and Kill-Chain Approach to Designing Attack-Detection Strategies for Malicious Insiders in Cloud Computing. 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1—9.

Attacks on cloud-computing services are becoming more prevalent with recent victims including Tesla, Aviva Insurance and SIM-card manufacturer Gemalto[1]. The risk posed to organisations from malicious insiders is becoming more widely known about and consequently many are now investing in hardware, software and new processes to try to detect these attacks. As for all types of attack vector, there will always be those which are not known about and those which are known about but remain exceptionally difficult to detect - particularly in a timely manner. We believe that insider attacks are of particular concern in a cloud-computing environment, and that cloud-service providers should enhance their ability to detect them by means of indirect detection. We propose a combined attack-tree and kill-chain based method for identifying multiple indirect detection measures. Specifically, the use of attack trees enables us to encapsulate all detection opportunities for insider attacks in cloud-service environments. Overlaying the attack tree on top of a kill chain in turn facilitates indirect detection opportunities higher-up the tree as well as allowing the provider to determine how far an attack has progressed once suspicious activity is detected. We demonstrate the method through consideration of a specific type of insider attack - that of attempting to capture virtual machines in transit within a cloud cluster via use of a network tap, however, the process discussed here applies equally to all cloud paradigms.