A machine learning based intrusion detection scheme for data fusion in mobile clouds involving heterogeneous client networks

Dey, Saurabh; Ye, Qiang; Sampalli, Srinivas

doi:10.1016/j.inffus.2019.01.002

Cited by 81 publications

(44 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The results are promising, framework is able to recognize diverse threats with a high accuracy. Other solution described in [21] involves a multilayered intrusion detection system with both feature-based and profile-based traffic filtration mechanisms. Classification and clustering techniques are being employed.…”

Section: Related Workmentioning

confidence: 99%

Adaptive context-aware energy optimization for services on mobile devices with use of machine learning considering security aspects

Nawrocki

Śnieżyński

Kołodziej

et al. 2020

2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)

View full text Add to dashboard Cite

In this paper we present an original adaptive task scheduling system, which optimizes the energy consumption of mobile devices using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the device and the cloud, which is especially important in mobile systems. Decisions are made taking the context into account (e.g. network connection type, location, potential time and cost of executing the application or service). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online, on a mobile device. Information about the context, task description, the decision made and its results such as power consumption are stored and constitute training data for a supervised learning algorithm, which updates the knowledge used to determine the optimal location for the execution of a given type of task. To verify the solution proposed, appropriate software has been developed and a series of experiments have been conducted. Results show that due to the experience gathered and the learning process performed, the decision module has consequently become more efficient in assigning the task to either the mobile device or cloud resources. In face of presented improvements, the security issues inherent within the context of mobile application and cloud computing are further discussed. As threats associated with mobile data offloading are a serious concern, often preventing the utilization of cloud services, we propose a more security focused approach for our solution, preferably without hindering the performance.

show abstract

Section: Related Workmentioning

confidence: 99%

Adaptive context-aware energy optimization for services on mobile devices with use of machine learning considering security aspects

Nawrocki

Śnieżyński

Kołodziej

et al. 2020

2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)

View full text Add to dashboard Cite

show abstract

“…ML techniques are playing a vital role in numerous applications of the cyber security for early detection and prediction of different attacks such as spam classification [29][30][31][32], fraud detection [33][34][35][36], malware detection [37][38][39][40], phishing [41][42][43], darkweb or deepweb sites [44,45], and intrusion detection [46][47][48][49]. ML techniques can address the scarcity available of required personnel with expertise in these niche cybercrime detection technologies.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

et al. 2020

View full text Add to dashboard Cite

Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.

show abstract

“…Similarly, a new intelligent IDS was presented in [14] applying ensemble and unsupervised machine learning techniques specifically to combat the security challenges in software-defined 5G networks. Saurabh Dey et al in [15] proposed a multi-layered IDS for mobile clouds involving heterogeneous client networks. In this approach, they have applied machine learning methods such as DBScan and K-means to observe the incoming traffic pattern and detect potential attacks.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive analysis and recommendation of feature evaluation measures for intrusion detection

Binbusayyis

Vaiyapuri

2020

Heliyon

View full text Add to dashboard Cite

The revolutionary advances in network technologies have spearheaded the design of advanced cyberattacks to surpass traditional security defense with dreadful consequences. Recently, Intrusion Detection System (IDS) is considered as a pivotal element in network security infrastructures to achieve solid line of protection against cyberattacks. The prime challenges presented to IDS are curse of high dimensionality and class imbalance that tends to increase the detection time and degrade the efficiency of IDS. As a result, feature selection plays an important role in enabling to identify the most significant features for intrusion detection. Although, several feature evaluation measures are being proposed for feature selection in literature, there is no consensus on which measures are best for intrusion detection. Therein, this work aims at recommending the most appropriate feature evaluation measure for building an efficient IDS. In this direction, four filter-based feature evaluation measures that stem from different theories such as Consistency, Correlation, Information and Distance are investigated for their potential implications in enhancing the detection ability of IDS model for different classes of attacks. Along with this, the influence of the selected features on classification accuracy of an IDS model is analyzed using four different categories of classifiers namely, K-nearest neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM) and Deep Belief Network (DBN). Finally, a two-step statistical significance test is conducted on the experimental results to determine which feature evaluation measure contributes statistically significant difference in IDS performance. All the experimental comparisons are performed on two benchmark intrusion detection datasets, NSL-KDD and UNSW-NB15. In these experiments, consistency measure has best influenced the IDS model in improving the detection ability with regard to detection rate (DR), false alarm rate (FAR), kappa statistics (KS) and identifying the most significant features for intrusion detection. Also, from the analysis results, it is revealed that RF is the ideal classifier to be used in conjunction with any of these four feature evaluation measures to achieve better detection accuracy than others. From the statistical results, we recommend the use of consistency measure for designing an efficient IDS in terms of DR and FAR.

show abstract

A machine learning based intrusion detection scheme for data fusion in mobile clouds involving heterogeneous client networks

Cited by 81 publications

References 8 publications

Adaptive context-aware energy optimization for services on mobile devices with use of machine learning considering security aspects

Adaptive context-aware energy optimization for services on mobile devices with use of machine learning considering security aspects

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

Comprehensive analysis and recommendation of feature evaluation measures for intrusion detection

Contact Info

Product

Resources

About