Biblio

Software-Defined Networking (SDN) introduces a centralized network control and management by separating the data plane from the control plane which facilitates traffic flow monitoring, security analysis and policy formulation. However, it is challenging to choose a proper degree of traffic flow handling granularity while proactively protecting forwarding devices from getting overloaded. In this paper, we propose a novel traffic flow matching control framework called Q-DATA that applies reinforcement learning in order to enhance the traffic flow monitoring performance in SDN based networks and prevent traffic forwarding performance degradation. We first describe and analyse an SDN-based traffic flow matching control system that applies a reinforcement learning approach based on Q-learning algorithm in order to maximize the traffic flow granularity. It also considers the forwarding performance status of the SDN switches derived from a Support Vector Machine based algorithm. Next, we outline the Q-DATA framework that incorporates the optimal traffic flow matching policy derived from the traffic flow matching control system to efficiently provide the most detailed traffic flow information that other mechanisms require. Our novel approach is realized as a REST SDN application and evaluated in an SDN environment. Through comprehensive experiments, the results show that-compared to the default behavior of common SDN controllers and to our previous DATA mechanism-the new Q-DATA framework yields a remarkable improvement in terms of traffic forwarding performance degradation protection of SDN switches while still providing the most detailed traffic flow information on demand.

Industry 4.0 or the fourth industrial revolution encapsulates future industry development trends to achieve more intelligent manufacturing processes, including reliance on Cyber Physical Systems (CPS). The increase in online access and control given by the incorporation of CPSs introduces a new challenge securing the operations of the CPS in that they are not supported by standard security protocols. This paper describes a process used to effectively protect the operations of an IIoT system by implementing security protocols on the CPS within the IIoT. A series of predefined boundary conditions of the safety critical parameters for which a heating and cooling CPS can safely operate within were established. If the CPS is commended to operate outside of these boundaries, it will disconnect from all external communication network and default to some pre-defined safe-operation mode until the system has been evaluated locally by an administrator and released from the safe-mode. This method was tested and validated by establishing a sample IIoT and CPS testbed setup which monitor and control the temperature of a target environment. An attack was initiated to force the target environment outside of the determined safety-critical parameters. The system responded by disabling all network ports and defaulted to the safe-operation mode established previously.

Web applications, as a conventional platform for sensitive data and important transactions, are of great significance to human society. But with its open source framework, the existing security vulnerabilities can easily be exploited by malicious users, especially when web developers fail to follow the secure practices. Here we present a distributed scanning system, FalconEye, with great precision and high performance, it will help prevent potential threats to Web applications. Besides, our system is also capable of covering basically all the web vulnerabilities registered in the Common Vulnerabilities and Exposures (CVE). The FalconEye system is consists of three modules, an input source module, a scanner module and a support platform module. The input module is used to improve the coverage of target server, and other modules make the system capable of generic vulnerabilities scanning. We then experimentally demonstrate this system in some of the most common vulnerabilities test environment. The results proved that the FalconEye system can be a strong contender among the various detection systems in existence today.

The uptake of virtualization and cloud technologies has pushed novel development and operation models for the software, bringing more agility and automation. Unfortunately, cyber-security paradigms have not evolved at the same pace and are not yet able to effectively tackle the progressive disappearing of a sharp security perimeter. In this paper, we describe a novel cyber-security architecture for cloud-based distributed applications and network services. We propose a security orchestrator that controls pervasive, lightweight, and programmable security hooks embedded in the virtual functions that compose the cloud application, pursuing better visibility and more automation in this domain. Our approach improves existing management practice for service orchestration, by decoupling the management of the business logic from that of security. We also describe the current implementation stage for a programmable monitoring, inspection, and enforcement framework, which represents the ground technology for the realization of the whole architecture.

With the rise of blockchain technology, peer-to-peer network system has once again caught people's attention. Peer-to-peer (P2P) is currently being implemented on various kind of decentralized systems such as InterPlanetary File System (IPFS). However, P2P file sharing network systems is not without its flaws. Data stored in the other nodes cannot be deleted by the owner and can only be deleted by other nodes themselves. Ensuring that personal data can be completely removed is an important issue to comply with the European Union's General Data Protection Regulation (GDPR) criteria. To improve P2Ps privacy and security, we propose a monitorable peer-to-peer file sharing mechanism that synchronizes with other nodes to perform file deletion and to generate the File Authentication Code (FAC) of each IPFS nodes in order to make sure the system synchronized correctly. The proposed mechanism can integrate with a consortium Blockchain to comply with GDPR.

Graphics Processing Units (GPUs) are getting popularly utilized for multi-purpose applications in order to enhance highly performed parallelism of computation. As memory virtualization methods in GPU nodes are not efficiently provided to deal with diverse memory usage patterns for these applications, the success of their execution depends on exclusive and limited use of physical memory in GPU environments. Therefore, it is important to predict a pattern change of GPU memory usage during runtime execution of an application. Data provenance extracted from application characteristics, GPU runtime environments, input, and execution patterns from runtime monitoring, is defined for supporting application management to set runtime configuration and predict an experimental result, and utilize resource with co-located applications. In this paper, we define data provenance of an application on GPUs and manage data by profiling the execution of CUDA scientific applications. Data provenance management helps to predict execution patterns of other similar experiments and plan efficient resource configuration.

In monolithic operating system (OS), any error of system software can be exploit to destroy the whole system. The situation becomes much more severe in cloud environment, when the kernel and the hypervisor share the same address space. The security of guest Virtual Machines (VMs), both sensitive data and vital code, can no longer be guaranteed, once the hypervisor is compromised. Therefore, it is essential to deploy some security approaches to secure VMs, regardless of the hypervisor is safe or not. Some approaches propose microhypervisor reducing attack surface, or a new software requiring a higher privilege level than hypervisor. In this paper, we propose a novel approach, named HyperPS, which separates the fundamental and crucial privilege into a new trusted environment in order to monitor hypervisor. A pivotal condition for HyperPS is that hypervisor must not be allowed to manipulate any security-sensitive system resources, such as page tables, system control registers, interaction between VM and hypervisor as well as VM memory mapping. Besides, HyperPS proposes a trusted environment which does not rely on any higher privilege than the hypervisor. We have implemented a prototype for KVM hypervisor on x86 platform with multiple VMs running Linux. KVM with HyperPS can be applied to current commercial cloud computing industry with portability. The security analysis shows that this approach can provide effective monitoring against attacks, and the performance evaluation confirms the efficiency of HyperPS.

Return-Oriented Programming (ROP) is a sophisticated exploitation technique that is able to drive target applications to perform arbitrary unintended operations by constructing a gadget chain reusing existing small code sequences (gadgets) collected across the entire code space. In this paper, we propose to address ROP attacks from a different angle-shrinking available code space at runtime. We present ROPStarvation , a generic and transparent ROP countermeasure that defend against all types of ROP attacks with almost zero run-time overhead. ROPStarvation does not aim to completely stop ROP attacks, instead it attempts to significantly increase the bar by decreasing the possibility of launching a successful ROP exploit in reality. Moreover, shrinking available code space at runtime is lightweight that makes ROPStarvation practical for being deployed with high performance requirement. Results show that ROPStarvation successfully reduces the code space of target applications by 85%. With the reduced code segments, ROPStarvation decreases the probability of building a valid ROP gadget chain by 100% and 83% respectively, with the assumptions that whether the adversary knows the vulnerable applications are protected by ROPStarvation . Evaluations on the SPEC CPU2006 benchmark show that ROPStarvation introduces nearly zero (0.2% on average) run-time performance overhead.

Mobile and web application security, particularly the areas of data privacy, has raised much concerns from the public in recent years. Most applications, or apps for short, are installed without disclosing full information to users and clearly stating what the application has access to, which often raises concern when users become aware of unnecessary information being collected. Unfortunately, most users have little to no technical expertise in regards to what permissions should be turned on and can only rely on their intuition and past experiences to make relatively uninformed decisions. To solve this problem, we developed DroidNet, which is a crowd-sourced Android recommendation tool and framework. DroidNet alleviates privacy concerns and presents users with high confidence permission control recommendations based on the decision from expert users who are using the same apps. This paper explains the general framework, principles, and model behind DroidNet while also providing an experimental setup design which shows the effectiveness and necessity for such a tool.

Cyber insurance is a viable method for cyber risk transfer. However, the cyber insurance faces critical challenges, the most important of which is lack of statistical data. In this paper, we proposed an incentive model considering monitoring signals for cybersecurity information haring based on the principal-agent theory. We studied the effect of monitoring signals on increasing the rationality of the incentive contract and reducing moral hazard in the process of cybersecurity information sharing, and analyzed factors influencing the effectiveness of the incentive contract. We show that by introducing monitoring signals, the insurer can collect more information about the effort level of the insured, and encourage the insured to share cybersecurity information based on the information sharing output and monitoring signals of the effort level, which can not only reduce the blindness of incentive to the insured in the process of cybersecurity information sharing, but also reduce moral hazard.

This paper considers one of the methods of efficient allocation of limited resources in special-purpose devices (sensors) to monitor complex network unit cybersecurity.

The design and implementation of exascale system is nowadays an important challenge. Such a system is expected to combine HPC with Big Data methods and technologies to allow the execution of scientific workloads which are not tractable at this present time. In this paper we focus on an event and anomaly detection framework which is crucial in giving a global overview of a exascale system (which in turn is necessary for the successful implementation and exploitation of the system). We propose an architecture for such a framework and show how it can be used to handle failures during job execution.

SCADA system is an essential component for automated control and monitoring in many of the Critical Infrastructures (CI). Cyber-attacks like Stuxnet, Aurora, Maroochy on SCADA systems give us clear insight about the damage a determined adversary can cause to any country's security, economy, and health-care systems. An in-depth analysis of these attacks can help in developing techniques to detect and prevent attacks. In this paper, we focus on the assessment of SCADA vulnerabilities from the widely used National Vulnerability Database (NVD) until May 2019. We analyzed the vulnerabilities based on severity, frequency, availability, integrity and confidentiality impact, and Common Weaknesses. The number of reported vulnerabilities are increasing yearly. Approximately 89% of the attacks are the network exploits severely impacting availability of these systems. About 19% of the weaknesses are due to buffer errors due to the use of insecure and legacy operating systems. We focus on finding the answer to four key questions that are required for developing new technologies for securing SCADA systems. We believe this is the first study of its kind which looks at correlating SCADA attacks with publicly available vulnerabilities. Our analysis can provide security researchers with useful insights into SCADA critical vulnerabilities and vulnerable components, which need attention. We also propose a domain-specific vulnerability scoring system for SCADA systems considering the interdependency of the various components.

Supervisory control and data acquisition (SCADA) systems are becoming increasingly susceptible to cyber-physical attacks on both physical and cyber layers of critical information infrastructure. Failure Mode and Effects Analysis (FMEA) have been widely used as a structured method to prioritize all possible vulnerable areas (failure modes) for design review of security of information systems. However, traditional RPN based FMEA has some inherent problems. Besides, there is a lacking of application of FMEA for security in SCADAs under vague and uncertain environment. Thus, the main purpose of this study was to propose a new evaluation model, which not only intends to recover above mentioned problems, but also intends to evaluate, prioritize and correct security risk of SCADA system's threat modes. A numerical case study was also conducted to demonstrate that the proposed new evaluation model is not only capable of addressing FMEA's inherent problems but also is best suited for a semi-quantitative high level analysis of a secure SCADA's failure modes in the early design phases.

The continuing decrease in feature size of integrated circuits, and the increase of the complexity and cost of design and fabrication has led to outsourcing the design and fabrication of integrated circuits to third parties across the globe, and in turn has introduced several security vulnerabilities. The adversaries in the supply chain can pirate integrated circuits, overproduce these circuits, perform reverse engineering, and/or insert hardware Trojans in these circuits. Developing countermeasures against such security threats is highly crucial. Accordingly, this paper first develops a learning-based trust verification framework to detect hardware Trojans. To tackle Trojan insertion, IP piracy and overproduction, logic locking schemes and in particular stripped functionality logic locking is discussed and its resiliency against the state-of-the-art attacks is investigated.

In the era of mass agriculture to keep up with the increasing demand for food production, advanced monitoring systems are required in order to handle several challenges such as perishable products, food waste, unpredictable supply variations and stringent food safety and sustainability requirements. The evolution of Internet of Things have provided means for collecting, processing, and communicating data associated with agricultural processes. This have opened several opportunities to sustain, improve productivity and reduce waste in every step in the food supply chain system. On the hand, this resulted in several new challenges, such as, the security of the data, recording and representation of data, providing real time control, reliability of the system, and dealing with big data. This paper proposes an architecture for security of big data in the agricultural supply chain management system. This can help in reducing food waste, increasing the reliability of the supply chain, and enhance the performance of the food supply chain system.

At the time of more and more devices being connected to the internet, personal and sensitive information is going around the network more than ever. Thus, security and privacy regarding IoT communications, devices, and data are a concern due to the diversity of the devices and protocols used. Since traditional security mechanisms cannot always be adequate due to the heterogeneity and resource limitations of IoT devices, we conclude that there are still several improvements to be made to the 2nd line of defense mechanisms like Intrusion Detection Systems. Using a collection of IP flows, we can monitor the network and identify properties of the data that goes in and out. Since network flows collection have a smaller footprint than packet capturing, it makes it a better choice towards the Internet of Things networks. This paper aims to study IP flow properties of certain network attacks, with the goal of identifying an attack signature only by observing those properties.

Despite the benefits offered by smart grids, energy producers, distributors and consumers are increasingly concerned about possible security and privacy threats. These threats typically manifest themselves at runtime as new usage scenarios arise and vulnerabilities are discovered. Adaptive security and privacy promise to address these threats by increasing awareness and automating prevention, detection and recovery from security and privacy requirements' failures at runtime by re-configuring system controls and perhaps even changing requirements. This paper discusses the need for adaptive security and privacy in smart grids by presenting some motivating scenarios. We then outline some research issues that arise in engineering adaptive security. We particularly scrutinize published reports by NIST on smart grid security and privacy as the basis for our discussions.

Security of VMs is now becoming a hot topic due to their outsourcing in cloud computing paradigm. All VMs present on the network are connected to each other, making exploited VMs danger to other VMs. and threats to organization. Rejuvenation of virtualization brought the emergence of hyper-visor based security services like VMI (Virtual machine introspection). As there is a greater chance for any intrusion detection system running on the same system, of being dis-abled by the malware or attacker. Monitoring of VMs using VMI, is one of the most researched and accepted technique, that is used to ensure computer systems security mostly in the paradigm of cloud computing. This thesis presents a work that is to integrate LibVMI with Volatility on a KVM, a Linux based hypervisor, to introspect memory of VMs. Both of these tools are used to monitor the state of live VMs. VMI capability of monitoring VMs is combined with the malware analysis and virtual honeypots to achieve the objective of this project. A testing environment is deployed, where a network of VMs is used to be introspected using Volatility plug-ins. Time execution of each plug-in executed on live VMs is calculated to observe the performance of Volatility plug-ins. All these VMs are deployed as Virtual Honeypots having honey-pots configured on them, which is used as a detection mechanism to trigger alerts when some malware attack the VMs. Using STIX (Structure Threat Information Expression), extracted IOCs are converted into the understandable, flexible, structured and shareable format.

Monitoring kernel object modification of virtual machine is widely used by virtual-machine-introspection-based security monitors to protect virtual machines in cloud computing, such as monitoring dentry objects to intercept file operations, etc. However, most of the current virtual machine monitors, such as KVM and Xen, only support page-level monitoring, because the Intel EPT technology can only monitor page privilege. If the out-of-virtual-machine security tools want to monitor some kernel objects, they need to intercept the operation of the whole memory page. Since there are some other objects stored in the monitored pages, the modification of them will also trigger the monitor. Therefore, page-level memory monitor usually introduces overhead to related kernel services of the target virtual machine. In this paper, we propose a low-overhead kernel object monitoring approach to reduce the overhead caused by page-level monitor. The core idea is to migrate the target kernel objects to a protected memory area and then to monitor the corresponding new memory pages. Since the new pages only contain the kernel objects to be monitored, other kernel objects will not trigger our monitor. Therefore, our monitor will not introduce runtime overhead to the related kernel service. The experimental results show that our system can monitor target kernel objects effectively only with very low overhead.

Cloud Computing as of large is evolving at a faster pace with an ever changing set of cloud services. The amenities in the cloud are all enabled with respect to the public cloud services in their own enormous domain aspects commercially, which tend to be more insecure. These cloud services should be thus protected and secured which is very vital to the cloud infrastructures. Therefore, in this research work, we have identified security features with a self-heal approach that could be rendered on the infrastructure as a service (IaaS) in a private cloud environment. We have investigated the attack model from the virtual machine snapshots and have analyzed based on the supervised machine learning techniques. The virtual machines memory snapshots API call sequences are considered as input for the supervised and unsupervised machine learning algorithms to classify the attacked and the un-attacked virtual machine memory snapshots. The obtained set of the attacked virtual machine memory snapshots are given as input to the self-heal algorithm which is enabled to retrieve back the functionality of the virtual machines. Our method of detecting the malware attains about 93% of accuracy with respect to the virtual machine snapshots.

For a long time, the most important approach for detecting malicious applications was the use of static, hash-based signatures. This approach provides a fast response time, has a low performance overhead and is very stable due to its simplicity. However, with the rapid growth in the number of malware, as well as their increased complexity in terms of polymorphism and evasion, the era of reactive security solutions started to fade in favor of new, proactive approaches such as behavior based detection. We propose a novel approach that uses an interpreter virtual machine to run proactive behavior heuristics from bytecode signatures, thus combining the advantages of behavior based detection with those of signatures. Based on our approximation, using this approach we succeeded to reduce by 85% the time required to update a behavior based detection solution to detect new threats, while continuing to benefit from the versatility of behavior heuristics.

Large-scale failures in communication networks due to natural disasters or malicious attacks can severely affect critical communications and threaten lives of people in the affected area. In the absence of a proper communication infrastructure, rescue operation becomes extremely difficult. Progressive and timely network recovery is, therefore, a key to minimizing losses and facilitating rescue missions. To this end, we focus on network recovery assuming partial and uncertain knowledge of the failure locations. We proposed a progressive multi-stage recovery approach that uses the incomplete knowledge of failure to find a feasible recovery schedule. Next, we focused on failure recovery of multiple interconnected networks. In particular, we focused on the interaction between a power grid and a communication network. Then, we focused on network monitoring techniques that can be used for diagnosing the performance of individual links for localizing soft failures (e.g. highly congested links) in a communication network. We studied the optimal selection of the monitoring paths to balance identifiability and probing cost. Finally, we addressed, a minimum disruptive routing framework in software defined networks. Extensive experimental and simulation results show that our proposed recovery approaches have a lower disruption cost compared to the state-of-the-art while we can configure our choice of trade-off between the identifiability, execution time, the repair/probing cost, congestion and the demand loss.

The safety management is an important and fundamental task in the construction and operation of pumped-storage power stations. However, because of the traditional technical framework, the relevant systems are separated from each other, leading to a lot of disadvantages in application and performance. In order to meet the requirements of smart pumped-storage power stations, an integrated safety management system (ISMS) based on ubiquitous internet of things in electricity is proposed in this paper. The ISMS is divided into five layers including data display layer, data manipulation layer, data processing layer, data transmission layer and data acquisition layer. It consists of six modules, i.e., central control module, cave access control and personnel location module, video and security monitoring module, emergency broadcasting and communication module, geological warning module, and fall protection module. All modules are integrated into a unified information platform.

Hardware Trojan Horses and active fault attacks are a threat to the safety and security of electronic systems. By such manipulations, an attacker can extract sensitive information or disturb the functionality of a device. Therefore, several protections against malicious inclusions have been devised in recent years. A prominent technique to detect abnormal behavior in the field is run-time verification. It relies on dedicated monitoring circuits and on verification rules generated from a set of temporal properties. An important question when dealing with such protections is the effectiveness of the protection against unknown attacks. In this paper, we present a methodology based on automatic generation of monitoring and formal verification techniques that can be used to validate and analyze the quality of a set of temporal properties when used as protection against generic attackers of variable strengths.