3,416 research outputs found
Statistical analysis driven optimized deep learning system for intrusion detection
Attackers have developed ever more sophisticated and intelligent ways to hack
information and communication technology systems. The extent of damage an
individual hacker can carry out upon infiltrating a system is well understood.
A potentially catastrophic scenario can be envisaged where a nation-state
intercepting encrypted financial data gets hacked. Thus, intelligent
cybersecurity systems have become inevitably important for improved protection
against malicious threats. However, as malware attacks continue to dramatically
increase in volume and complexity, it has become ever more challenging for
traditional analytic tools to detect and mitigate threat. Furthermore, a huge
amount of data produced by large networks has made the recognition task even
more complicated and challenging. In this work, we propose an innovative
statistical analysis driven optimized deep learning system for intrusion
detection. The proposed intrusion detection system (IDS) extracts optimized and
more correlated features using big data visualization and statistical analysis
methods (human-in-the-loop), followed by a deep autoencoder for potential
threat detection. Specifically, a pre-processing module eliminates the outliers
and converts categorical variables into one-hot-encoded vectors. The feature
extraction module discard features with null values and selects the most
significant features as input to the deep autoencoder model (trained in a
greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for
Cybersecurity is used as a benchmark to evaluate the feasibility and
effectiveness of the proposed architecture. Simulation results demonstrate the
potential of our proposed system and its outperformance as compared to existing
state-of-the-art methods and recently published novel approaches. Ongoing work
includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired
Cognitive Systems (BICS 2018
A cognitive based Intrusion detection system
Intrusion detection is one of the primary mechanisms to provide computer
networks with security. With an increase in attacks and growing dependence on
various fields such as medicine, commercial, and engineering to give services
over a network, securing networks have become a significant issue. The purpose
of Intrusion Detection Systems (IDS) is to make models which can recognize
regular communications from abnormal ones and take necessary actions. Among
different methods in this field, Artificial Neural Networks (ANNs) have been
widely used. However, ANN-based IDS, has two main disadvantages: 1- Low
detection precision. 2- Weak detection stability. To overcome these issues,
this paper proposes a new approach based on Deep Neural Network (DNN. The
general mechanism of our model is as follows: first, some of the data in
dataset is properly ranked, afterwards, dataset is normalized with Min-Max
normalizer to fit in the limited domain. Then dimensionality reduction is
applied to decrease the amount of both useless dimensions and computational
cost. After the preprocessing part, Mean-Shift clustering algorithm is the used
to create different subsets and reduce the complexity of dataset. Based on each
subset, two models are trained by Support Vector Machine (SVM) and deep
learning method. Between two models for each subset, the model with a higher
accuracy is chosen. This idea is inspired from philosophy of divide and
conquer. Hence, the DNN can learn each subset quickly and robustly. Finally, to
reduce the error from the previous step, an ANN model is trained to gain and
use the results in order to be able to predict the attacks. We can reach to
95.4 percent of accuracy. Possessing a simple structure and less number of
tunable parameters, the proposed model still has a grand generalization with a
high level of accuracy in compared to other methods such as SVM, Bayes network,
and STL.Comment: 18 pages, 6 figure
In-depth comparative evaluation of supervised machine learning approaches for detection of cybersecurity threats
This paper describes the process and results of analyzing CICIDS2017, a modern, labeled data set for testing intrusion detection systems. The data set is divided into several days, each pertaining to different attack classes (Dos, DDoS, infiltration, botnet, etc.). A pipeline has been created that includes nine supervised learning algorithms. The goal was binary classification of benign versus attack traffic. Cross-validated parameter optimization, using a voting mechanism that includes five classification metrics, was employed to select optimal parameters. These results were interpreted to discover whether certain parameter choices were dominant for most (or all) of the attack classes. Ultimately, every algorithm was retested with optimal parameters to obtain the final classification scores. During the review of these results, execution time, both on consumerand corporate-grade equipment, was taken into account as an additional requirement. The work detailed in this paper establishes a novel supervised machine learning performance baseline for CICIDS2017
Automatic Building of a Powerful IDS for The Cloud Based on Deep Neural Network by Using a Novel Combination of Simulated Annealing Algorithm and Improved Self- Adaptive Genetic Algorithm
Cloud computing (CC) is the fastest-growing data hosting and computational technology that stands today as a satisfactory answer to the problem of data storage and computing. Thereby, most organizations are now migratingtheir services into the cloud due to its appealing features and its tangible advantages. Nevertheless, providing privacy and security to protect cloud assets and resources still a very challenging issue. To address the aboveissues, we propose a smart approach to construct automatically an efficient and effective anomaly network IDS based on Deep Neural Network, by using a novel hybrid optimization framework “ISAGASAA”. ISAGASAA framework combines our new self-adaptive heuristic search algorithm called “Improved Self-Adaptive Genetic Algorithm” (ISAGA) and Simulated Annealing Algorithm (SAA). Our approach consists of using ISAGASAA with the aim of seeking the optimal or near optimal combination of most pertinent values of the parametersincluded in building of DNN based IDS or impacting its performance, which guarantee high detection rate, high accuracy and low false alarm rate. The experimental results turn out the capability of our IDS to uncover intrusionswith high detection accuracy and low false alarm rate, and demonstrate its superiority in comparison with stateof-the-art methods
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