|Type of paper:||Research paper|
The need for rapid reconfiguration remains to be one of the biggest challenges for companies like Google and Facebook. Through the use of white-labelled hardware and in-house technical expertise, these companies have managed to deal with the challenges of massive scale regarding the deployment of infrastructure. These challenges may include scalability, agility, and cost of implementation procedures. Monolithic mainframe systems have managed to adopt the computer infrastructure, thus enabling the running of the majority of applications to various commodities such as x86-based hardware. Furthermore, the management and control of scale-out virtualized resources are also made possible by the integrated control panel.
Some of the characteristics of software-defined storage include commodity hardware, scale-out architecture, resource pooling, abstraction, automation, and programmability. Commodity hardware ensures that all the intelligence in software-defined storage is contained within the layer of the software. On the other hand, scale-out architecture is the hardware in which the system allows rather than prevent fluid, flexible and elastic configuration of storage resources. Furthermore, resource pooling allows the available storage resources to be pooled in the form of entity which is unified logically. In turn, the resources are managed on a central basis. Abstraction allows the virtualization and presentation of physical storage resources to the control panel. Extensive automation is provided by the storage layer thus delivering a policy-based storage provision. Also, programmability consists of inbuilt automation that offers visibility and control of fine-grained underlying resources.
Policy-Driven Services Levels: Software-Defined Storage API
Fig: A Storage Services Pool.
The SDS API usually defines service levels. In this process, the metadata matches the required services. A storage services pool uses specific storage that contains different data services new or used and in turn, applies them to certain ranges of the required services. The level of implementing these kinds of services such as volume, file, object and container is determined by the required granularity. Furthermore, the pools are aggregated by these resources. Service level requirements are met through the application of various data services. In turn, this process assures that new resources are capable of being added to the required pools. However, the attempted failed resources are detached from the pool until repaired.
The network design is gradually evolving at a rapid pace due to its functionality-based capability. As a result, the behavior of network devices can be able to programmatically attain flexibility and also become more dynamic and cost-efficient regarding the operational intricacy. Software-defined networks attempt to deal with the emerging trends in the computer industry. They tend to examine the definition and review of a uses case system. The use case system is considered as an engineering term which shows how a user can be able to accomplish a certain or particular goal. Recent advances in software-based networking have enabled the simplification of operations, improvement of agility and also supporting other advances related to network programmability.
The network architecture design has widely been influenced by the emergence of software-defined networking. For instance, recent advances such as cloud computing, data centers and big data science have enabled developers to use them as guides especially during the customization of networks. This concept has also assisted developers into rethinking classical approaches, thus, breaking the traditional approach in which the switch decided what kind of actions were to be done. Nowadays, the network, being a notable service, can manage other intact networks through the means of software. This process is usually done by writing certain programs which alter other network activities to correspond with the required applications and environments.
Two-tiered architecture, low-level operation and event reordering are constantly dealt with to control the program and the packet processing. Switches are installed on the packet processing while on the other hand, the program is equipped with a controller. These two components rely on intricate dependencies which make reasoning difficult. For example, when installing or removing a rule, it will act as a barrier for future network events to receive any program. Thus, the reasoning of a programmer often relates to the kind of behavior the controller program has. Also, the rules on switches and their interaction via asynchronous messages also reason about the particular behavior of the program.
OpenFlow force programmers are among the platforms that use a low-level API to express intentions based on high-level procedures. By allowing such programmers to act as platforms, the reasoning of SDN programs becomes unnecessarily hard. On the other hand, hardware switches are responsible for employing some techniques which enhance the optimal maximization of performance. These include techniques such as reordering controller messages. As a result, SDN programs can be highly non-deterministic thus further complicating reasoning. Also, SDN technology also enables network operators to identify which kinds of network services are reliable without any alteration between the required specifications with network interfaces.
Detecting Of Ransomware Using Software-Defined Networking
Cyber-Extortion malware is a sophisticated signature-based detection that employs encryption techniques as well as social engineering. Modern malware such as ransomware was developed to extort money from people and specific companies. As a result, mission-critical systems have widely been affected due to the kind of impact it has generated towards businesses. Usually, money is paid in the form of bitcoins as it guarantees the attacker anonymity and it's easily untraceable. When companies fail to respond, the data can be destroyed.
The ransomware relies on multiple OS platforms thus guaranteeing the security of known pay-outs. Furthermore, it is considered as popular by attackers as it retains anonymity. Most attacks are carried out in major tech companies and targeted people in the business. Various platforms such as Linux, Mac OS and Android tend to be equipped with ransomware. Other malware including CryptoLocker, CryptoWall, TeslaCrypt and Locky have also been developed and are constantly updated. Modern ransomware works in two ways; firstly, various attack vectors such as phishing or spam infect the user machine. Secondly, the stored data or system is encrypted depending on the type of ransomware.
Storage drives such as Dropbox can be encrypted using ransomware. In turn, multiple systems on the network often are compromised due to such an infection. Due to the recent developments and constant update of this malware, long lasting countermeasures have become increasingly difficult. As a result, the large number of devices connected to the internet is constantly being circulated. Signature-based detection is one of the most common methods of malware detection. Antivirus scanners are constantly kept up to date especially with the use of encryption techniques and social engineering. In turn, this process enables the defence in the firewall and email spam filters to be easily evaded. This kind of detection explains the difficulty of entering a malware into the system or network.
Certain techniques of encryption can cover various methods of detection such as extension identification like locky. Having a tested, reliable backup is one of the best ways to tackle malware. The reason for this is because it avoids the damages that come as a result of the malware. This kind of method guarantees safe creation and maintenance of backups. However, it can be quite expensive and time-consuming. Current implementations have been deployed to detect malware such as User Behavior Analytics. This method mostly works on the baseline of normal activities. Some of the examples of User Based Analytics include Varonics and DatAdvantage.
One of the main disadvantages of User Based Analytics is when any other legitimate activity cannot be able to perform under normal behavior. Thus, when abnormal activity is identified, the administrator usually receives the alert. Honeypot techniques have been deployed to detect malware. For instance, Software-defined networking can be added to the honeypot to improve the effectiveness of the detection. Furthermore, the methods that are applied are frequently checked, and loopholes are constantly fixed to prevent attackers from exploiting them. Also, necessary and sensitive files receive constant backup to a secured and tested location. As a result, the restoration of the work is quickly assessed in case of a threat to the critical system.
Different storage devices based on different various natural phenomena have been invented to allow more convenient storage of data. In turn, within the computer system, each has its unique role to play. These storage devices include application storage, cloud storage, object-based storage, software-defined storage and solid-state storage. The central processing unit, being the most important part of a computer, is responsible for a data operation, performing calculations and controlling the various components. It usually contains memory where fixed operations are performed as well as showing the result of the output. More versatile computers are also able to reconfigure new programs with new in-memory instructions. The operating instructions and data usually differ from different machines due to a sudden change of behavior during reconfiguration of such devices. Also, memory types that are organized in a storage hierarchy act as a trade-off that exists between the level of performance and cost.
Bolles, Benton R., et al. "Method and system for implementing a virtual storage pool in a virtual environment." U.S. Patent No. 8,370,833. 5 Feb. 2013.
Cabaj, Krzysztof, and Wojciech Mazurczyk. "Using software-defined networking for ransomware mitigation: the case of cryptowall." IEEE Network 30.6 (2016): 14-20.
Ellul, Peter Anthony Ronald, and Stephen James Brown. "Data storage devices." U.S. Patent No. 8,670,242. 11 Mar. 2014.
Malina, James N., and Allen Samuels. "Method and apparatus for a network connected storage system." U.S. Patent Application No. 15/484,493.
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