Data Intrusion Detection with basic Python coding and prevention of other intrusive manifestation by the use of intrusion application

Abstract: The growing smartphone technology and emerging mobile cloud technology are the latest wireless technology. Mobile cloud computing has many of the advantages that look forward to the future and it's also simple for hackers to take full control of many other users Privacy of Data. While data security is expected to be secured, the main drawback for users when the computer is connected to the internet it's not that difficult for an intruder to engage in a data theft on the required target. So, for providing better security the combination of Hybrid Intrusion Detection System (HyInt) and Honeypot networks is thus implemented into Mobile Cloud Environment with the significant purpose of mitigating unidentified and known attacks in order to provide security. Execution of the research work provides a pure perspective of the security and quality products of the algorithm that was not included in the previous research work. As part of the research work, intensive statistical analysis was performed to prove the consistency of the proposed algorithm. The implementation and evaluation outcome offers clear potential for any further research work in the cloud-based Intrusion Detection System. The implemented algorithm can be used for high-security cloud environment that is developed for army and banking purposes to monitor the network's activities effectively.

Abstract: In recent years, with the growing popularity of cloud computing, security in cloud has become an important issue. As "Prevention is better than cure", detecting and blocking an attack is better than responding to an attack after a system has been compromised. This paper proposes architecture capable of detecting intrusions in a distributed cloud computing environment, and safeguarding it from possible security breaches. It deploys a separate instance of IDS for each user and uses a single controller to manage the instances. IDS in this architecture can use signature based as well as learning based method.

Abstract: Intrusion detection has become a critical component of network administration due to the vast number of attacks persistently threaten our computers. Traditional intrusion detection systems are limited and do not provide a complete solution for the problem. They search for potential malicious activities on network traffics; they sometimes succeed to find true security attacks and anomalies. However, in many cases, they fail to detect malicious behaviours (false negative) or they fire alarms when nothing wrong in the network (false positive). In addition, they require exhaustive manual processing and human expert interference. Applying Data Mining (DM) techniques on network traffic data is a promising solution that helps develop better intrusion detection systems. Moreover, Network Behaviour Analysis (NBA) is also an effective approach for intrusion detection. In this paper, we discuss DM and NBA approaches for network intrusion detection and suggest that a combination of both approaches has the potential to detect intrusions in networks more effectively.

Abstract: Intrusion Detection Systems (IDS) are used in computer networks to safeguard the integrity and confidentiality of sensitive data. In recent years, network traffic has become sizeable enough to be considered under the big data domain. Current machine learning based techniques used in IDS are largely defined on eager learning paradigms which lose performance efficiency by trying to generalize training data before receiving queries thereby incurring overheads for trivial computations. This paper, proposes the use of lazy learning methodologies to improve overall performance of IDS. A novel heuristic weight based indexing technique has been used to overcome the drawback of high search complexity inherent in lazy learning. IBk and LWL, two popular lazy learning algorithms have been compared and applied on the NSL-KDD dataset for simulating a real-world like scenario and comparing their relative performances with hw-IBk. The results of this paper clearly indicate lazy algorithms as a viable solution for real-world network intrusion detection.