Sep 01, · Following are some PhD topics in Data Mining which can help you take this step a little ahead: A stride towards customer review extraction using frequent pattern mining algorithms and soft computing techniques. Medical data analysis with the aid of association rule mining and artificial intelligence. Intrusion detection and classification with * That the product provided is intended to Phd Thesis Intrusion Detection Data Mining be used for research or study purposes only. It is crime-free and secure cyberspace. Our service uses the latest security gains to protect your essay details, personal data, and financial operations from any internal and external dangers/10() Our online essay writing service delivers Master’s level writing by experts who have earned graduate degrees in your subject matter. All citations and Phd Thesis Intrusion Detection Data Mining writing are % original. Your thesis is delivered to you Phd Thesis Intrusion Detection Data Mining ready to submit for faculty review. You can stand behind our writing
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This present phd thesis intrusion detection data mining has placed cloud computing platforms under constant treats of cyber-attacks at all levels, with an ever-evolving treat landscape. It has been observed that the number of threats faced in cloud computing is rising exponentially mainly due to its widespread adoption, rapid expansion and a vast attack surface. One of the front-line tools employed in defense against cyber-attacks is the Intrusion Detection Systems IDSs. In recent times, an increasing number of researchers and cyber security practitioners alike have advocated the use of deception-based techniques in IDS and other cyber security defenses as against the use of traditional methods.
This paper presents phd thesis intrusion detection data mining extensive overview of the deception technology environment, phd thesis intrusion detection data mining, as well as a review of current trends and implementation models in deception-based Intrusion Detection Systems.
Issues mitigating the implementation of deception based cyber security defenses are also investigated. Cloud ComputingIntrusion Detection SystemCyber SecurityCyber DeceptionDeception Technology. The cloud computing treat landscape is ever evolving and security concerns persist and still remain a top priority in cloud computing today.
Such treats include insecure interfaces and APIs, system and application vulnerabilities, abuse of cloud services, network threats and Advanced Persistent Threats APTs. The main aim of most information security defenses is to deny and isolate all unauthorized access, execution or manipulations in a given information system, thereby creating a boundary which acts to isolate the information system from the outside world. Such controls for denying access may include a firewall, access controls and end-point protection such as anti-virus.
Other such controls may include the use of Network Address Translation NATVirtual Private Networks VPNencryption and steganography, which aid in isolating and hiding parts of our information systems. If the use of security controls to deny and isolate intruders fails, then the next steps would be to slow down the would-be attackers such as to slow down the response of system calls when anomalies are detectedprevent or in the least significantly reduce the likelihood that an intruder will gain sensitive data by:.
IDS are very versatile and can be deployed at numerous levels of our information system such as at the network level, application level or host level. While IDS affords great protection to information systems, traditional IDS models come with several inherent flaws and can easily be defeated by the share sophistication such as zero-day attacks and advanced persistent treats and attack volume of modern treats, phd thesis intrusion detection data mining.
Several techniques have been put forward to help strengthen the capability and resilience of IDS against modern attacks. One of such techniques is the use of deception technologies in IDS design.
In our context of study, intrusion could be described as any given set of actions which attempts to compromise the integrity, confidentiality or availability of any given system [1]. Intrusion detection Systems IDS are therefore systems involved in monitoring and analyzing events triggered by intrusion activities aimed at undermining the integrity, confidentiality or availability of the system.
The ultimate aim of the IDS is to ascertain intruders and aid in triggering counter-measures against such identified attacks. An IDS needs to be designed with multiple performance specifications [2] [3]. It should be able to collect data from the network related to suspected attack-like behaviors, store the data locally or on the network, analyze the data, and raise alerts and alarms [3].
The performance of an IDS in carrying out these tasks is characterized by its hardware capacity CPU, Memory, Storage, and Network bandwidthaccuracy of detection of attacks, coverage of attacks content, aspect, and form of attacksability to resist techniques of evading detection, speed of detection and reporting, overheads, and capacity to process the workloads assigned in a network [2].
The detection approach may be signature-based knowledge-basedanomaly-based behavioral detectionor a hybrid of both the techniques [4]. Figure 1 gives a detailed taxonomy of Intrusion Detection Systems IDS. The traditional IDS processes of signature or anomaly detection becomes even more cumbersome on cloud computing [5]. Anomaly-based IDS while showing great prospects in identifying new and evolving threats, are notorious in misidentifying legitimate traffic patterns as malicious, while possibly allowing malicious traffic as legitimate traffic.
In a similar vein, phd thesis intrusion detection data mining, while signature-based IDS are very effective in stopping all attacks documented in its signature database, they are grossly ineffective in identifying new evolving attacks and day-0 attacks.
Also, building and maintaining a meaningful, dynamic and relevant signature database remains a major challenge. Figure 2 gives a general architecture of Intrusion Detection Systems. In recent times, it has become quite common to find IDS being complimented by incorporating appropriate Machine Learning ML algorithms in their design [6].
Machine Learning has the ability to detect patterns of similarities between two data sets with definitive distance measures [7]. In NIDS, the patterns of attacks in the data flows passing through a network port can be detected by employing an appropriate Machine Learning Algorithm MLA [7] [8] [9] [10]. The accuracy and effectiveness of MLA depends upon the quality, relevance, and accuracy of the training data set used to train the MLA. Based on the quality, relevance, phd thesis intrusion detection data mining, and accuracy of learning, phd thesis intrusion detection data mining, MLAs can recognize highly complex data patterns in massive voluminous data flows.
In this quest, MLAs can be used to detect. Figure 1. Taxonomy of intrusion detection systems IDS. Figure 2. General architecture of traditional ID. Deception based techniques are a very powerful tool in the right hands [11]. Deception could be described as an untrue perception, which is induced intellectually by the actions or inactions of other entities. Deceptive mechanism has been used by mankind since the beginning of recorded history. In the field of computing, deception techniques have been advanced as a means of information security as far back as the s [12].
A generally accepted definition of deception-based computer security is given by [13] as the deliberate actions taken to mislead attackers, which is aimed to ultimately cause them to take or not take specific actions that will benefit computer-security defenses.
However, the use of deceptive techniques in computer-security was adopted and became more widespread in the s [14] [15]. Several authors have advanced different taxonomy for deception technologies see Figure 3. He further posits that both phases are actually interdependent and that a comprehensive deception technique must incorporate both phases implicitly or explicitly in its design. They go on to assert that dissimulation and simulation can be applied at three levels including:.
Figure 3. Taxonomy of deception techniques. Finally, [19] categorizes deception techniques into two broad groups, including Prevention techniques such as steganography and detection techniques such as honeypots. This paper explores the backgrounds of intrusion detection systems and deception-based technologies. It further considers the novel application of deception and decoy-based techniques in intrusion detection systems, the limitations of such IDS models and challenges to the use of deception technologies in cyber-security.
Cyber-attack footprints are ever evolving [20]. In their research work, [21] reviewed approaches to detecting DDoS attack in cloud computing, considering both the application-bug and infrastructure levels. In the same vein, [22] carried out a survey on current Intrusion Detection System techniques in cloud-based platforms.
He further presented a comprehensive comparative study on iCloud, Dropbox and Google Drive and how they go about securing their various cloud infrastructure. Similarly, [23] [24] [25] and [26] all carried out extensive surveys on IDS deployment in cloud-based environment. However, all these authors failed to address the use of deception-based techniques for intrusion detection in cloud-based platforms.
In recent times, the use of deception techniques in cyber security has expanded greatly, with numerous authors advocating the use of deception-based technologies in protecting information systems and to react against would be attackers [15] [27]. Likewise, [26] presents an extensive survey on intrusion detection, including methods used for obtain feature selection, computation of high dimensional data, and choice of learning algorithm.
In their paper, [32] gives an all-encompassing overview of deception-based technology including in-depth discussions on taxonomies, psychological concepts of deception, phd thesis intrusion detection data mining, implementation of deception based, legal and phd thesis intrusion detection data mining issues. Similarly, [33] presented a survey of technological trends in cyber deception research. They identified several gaps with presented techniques, extensively surveyed current research works in novel fields of deception-based techniques in cyber security defense.
Phd thesis intrusion detection data mining their paper, [34] gives a comprehensive classification and survey current application of deception techniques in cyber security including limitations of current solutions, deployment of deception in complex systems, novel techniques and experiments for evaluating effectiveness of deception-based techniques and current research directions.
The authors also stressed that approach to overall cyber security architecture must be comprehensive, thus proposing a security model referred to as the conceptual Hybrid Threats Model. The landscape of cloud computing is constantly evolving in an astronomical scale [36]phd thesis intrusion detection data mining, and so are the treats associated with the cloud platform. It is therefore imperative that security measures in cloud computing should not lag behind in innovation and efficacy.
Outlined below are current trends and innovations in deception-based Intrusion Detection Systems for cloud computing platforms:. In recent times, Honey pots have gained significant research attention in the field of cyber deception. The authors used a Gini index for classification and reduction of attack patterns, and trained a multi-class SVM to obtain better results than the KDD 99 existing database used as a trainer. The SSH sensor used was Kippo SSH Python script that emulates the POSIX file system with some customisations needed to keep the attackers engaged for long periods.
The phd thesis intrusion detection data mining data was collected on Apache Spark phd thesis intrusion detection data mining running Hadoop File System HDFS. A Naive Bayes classifier was used to categorise the traffic patterns as good and bad, and behaviour training module of the Raspberry P1 honey pot was used to generate and store alarms.
In their experiment, the top 20 attacker Ids tried 1. These were all real attackers indicating the seriousness of network attacks ongoing on the Internet. In a similar experiment, [39] used a Puppet Enterprise Server with four agents used as honey pot sensors and HonSSH for redirecting traffic to the honey pots.
Here, multiple MLAs need to be used on the cloud computing to arrive at a final comprehensive classification of attack patterns. As presented by [40]mobile honey pots can be used to collect distributed attack patterns throughout the cloud network that can dynamically roam on the cloud and position themselves intelligently on the propagation paths of ongoing attacks.
This research is similar to the dynamic Markov chain formation using intelligent dynamic honey pot agents presented by [41]. The data collected by dynamically distributed mobile honey pot agents need to be collaborated at the analysis engine to create new forms of attack classifiers prevailing on the cloud computing networks.
In another study involving mobile intelligent honey pots, [28] designed DNS honey tokens, web server honey tokens, and fake social network avatars to create network and application layer deception models such that attackers believe the victims as real social network users.
This is in order to more quickly identify and mitigate potential conflicts and risks in initial phases of software development that could compromise cyber security, ultimately reducing costs of ill-planned decisions. Dynamic networking techniques could be used in protecting hosts from internal and external attacks. Here, host address randomization was implemented by creating an interconnection of subnet switches and a central network.
This model was successfully built by taking advantage of improvements in software-defined networking, which are not available phd thesis intrusion detection data mining traditional physical infrastructure.
The use of decoy routing is a unique tool in deception based cyber security and has earlier been proposed by authors such [44], phd thesis intrusion detection data mining. Simply put, decoy routing is essentially designed to circumvent IP address-based network filtering by leveraging a decoy destination. A decoy router that supports a secret channel is implemented on the path between the decoy destination and the user.
Thus, the user is able to access filtered content through the hidden channel. This novel defense mechanism is based on the frequent migration of VMs follows a signaling game technique.
While the claims in this novel phd thesis intrusion detection data mining research looks plausible, the finding were not validated with data, nor was the proposed system analyzed with experimentations and real scenarios. Furthermore, phd thesis intrusion detection data mining, the signal gamming mode was not evaluated with numeric analysis in other to truly picture its workings and effectiveness.
It is also imperative that the bevaiour of the system for live and non-live migration defense of VMs be properly evaluated with the use of appropriate real-life case studies. Similarly, [30] presented a novel dynamic host mutation DHM architecture based on moving target defense MTD which actively deals with a variety of complex insider threats. A Flume module was designed and implemented, which helped to reduce and distribute real time data streams from numerous sources into the data analysis mode.
Apache Spark an implementation of MapReduce was used to further design the analysis mode. In order to detect abnormalities in network activities and alert the network administrators, the fuzzy c -means algorithm and k -means were used.
Network intrusion detection using deep learning techniques
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