A Survey on Feature Selection Techniques Based on Filtering Methods for Cyber Attack Detection
1.1. Cyber Attacks and Their Danger
1.2. Cyber Attack Detection
1.3. The Importance of Feature Selection for Attack Detection
- Reducing the cost of acquiring data.
- Reducing the cost of training classification models.
- Reducing model size.
- Making classification models easier to understand.
- Improving detection performance (maybe).
1.4. Our Motivation
2. Feature Selection Algorithms
2.1. Filter-Based Techniques
- They are independent of classifiers (classification algorithms) as they only use feature relevance that is evaluated according to inherent properties of the data itself.
- They are computationally efficient.
- They scale easily to datasets with many features (high-dimensional datasets).
- The process of feature selection is performed only once, and the result of feature selection can be used for different classifiers .
- They can be used in a supervised or unsupervised manner depending on the availability of labeled training data. This flexibility allows it to be used in a wide range of IDS applications .
|Algorithm 1: General filter-based technique|
|Input: ) //a training dataset with N features|
//a subset from which start the search
//a stopping criterion
Output: //an optimal subset
02 Initialize: ;
03 ; //evaluate
04 do begin
05 ; //generate a subset for evaluation
06 ; //evaluate the current subset S by M
07 if ( is better than ) then
10 end until ();
11 return ;
2.2. Wrapper Techniques
2.3. Embedded Techniques
2.4. Hybrid Techniques
2.5. Some Other Techniques
3. Search Algorithms in Filter-Based Feature Selection Methods
- High-dimensional datasets: when the number of features is very large, it can be computationally expensive to evaluate all possible feature subsets, but search algorithms can help to efficiently identify the most important features by only evaluating a subset of them.
- Overfitting: when a model utilizes too many features, it may fit the training data too well and yet perform poorly on new data, and search algorithms can help to find a minimal subset of features that can prevent overfitting.
- Improving model interpretability: search algorithms can help to identify a subset of features that are most informative, thus making the model more interpretable.
- Reducing computational complexity: when the number of features is very large, it can also be computationally expensive to train and evaluate a model, but using search algorithms to identify a subset of important features can reduce the computational complexity.
- Improving generalization: search algorithms can help to identify a subset of features that generalize well to new data.
3.1. Greedy Hill Climbing
|Algorithm 2: Greddy Hill Climbing|
|Input: ) //a training dataset with N features|
//a start state
Output: //an optimal state
02 Initialize: ;
03 Expand by making each possible local change
04 ; //get the child t of with the highest e(t)
05 if () then
08 goto 04;
10 return ;
3.2. Best First Search
|Algorithm 3: Best First Search|
|Input: ) //a training dataset with N features|
//a start state
Output: //an optimal state
02 Initialize: Set an OPEN list containing the start state;
03 Set a CLOSED list;
05 ; //get the state from OPEN with the highest evaluation
06 if () then
08 For each child t of S that is not in the OPEN or COSED list, evaluate and add to OPEN;
09 if ( has changed in the last set of expansions) then
10 goto 05;
12 return ;
3.3. Genetic Algorithms
|Algorithm 4: Genetic Algorithm|
|Input: ) //a training dataset with N features|
Output: x // for which e(x) is highest.
02 Initialize: Objective function
03 Encode the solutions into chromosomes (strings);
04 Define fitness F (eg, for maximization);
05 Generate the initial population P;
06 Initialize the probabilities of crossover () and mutation ();
07 while (t < Max number of generations)
08 Generate new population P′ by crossover and mutation;
09 Crossover with a crossover probability ;
10 Mutate with a mutation probability ;
11 if () then //Accept the new solution if their fitness increase
14 goto 08;
15 t = t+1;
16 end while
17 return ;
4. Relevance Measures for the Filter-Based Feature Selection Methods
4.1. Pearson Correlation
4.3. Information Gain (IG)
4.4. Mutual Information (MI)
4.5. Minimum Redundancy Maximum Relevance Feature Selection (MRMR)
4.6. Fast Correlation Based Filter (FCBF)
4.8. Multivariate Mutual Information (MMI)
4.9. Mutal Information Feature Selection (MIFS)
4.10. Multivariate Mutual Information-Based Feature Selection (MMIFS)
4.11. Correlation Based Feature Selection (CFS)
4.12. Efficient Correlation-Based Feature Selection (ECOFS)
5. Experiments and Result
5.1. Common Datasets for Studies on Network Anomaly Detection
5.1.1. KDDcup’99 and NSL-KDD
- The training set and test set of the NSL-KDD dataset do not contain redundant records, making the detection more accurate.
- The number of records in training and testing is set reasonably, which makes it cheap to run experiments on the full set without randomly selecting a small subset. Therefore, the evaluation results of different research efforts will be consistent and comparable.
5.2. A Comparative Study on the Performance of Filter-Based Feature Selection Techniques
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|KDDcup’99 Dataset||#Samples||#Features||#Classes||Multi Classification|
|Tran data (10%)||494,021||41||5(22)||Normal, DoS, Probe, U2R, R2L|
|Test data||311,029||41||5(39)||Normal, DoS, Probe, U2R, R2L|
|NSL-KDD Dataset||#Samples||#Features||#Classes||Multi Classification|
|Tran data||125,973||41||5(23)||Normal, DoS,|
Probe, U2R, R2L
|Test data||22,544||41||5(38)||Normal, DoS, Probe, U2R, R2L|
|Attack Category||Attacks in KDDcup’99 Training Set||Additional Attacks in KDDcup’99 Test Set|
|DoS||back, neptune, smurf, teardrop, land, pod||apache2, mailbomb, processtable, udpstorm|
|Probe||satan, portsweep, ipsweep, nmap||mscn, saint|
|R2L||warezmaster, warezclient, ftp_write, guess_password, imap, multihop, phf, spy||sendmail, named, snmpgetattack, snmpguess, xlock, xsnoop, worm|
|U2R||rootkit, buffer_overflow, loadmodule, perl||httptunnel, ps, sqlattack, xterm|
|Attacks in Dataset||Attack Type (37)|
|DoS||back, land, neptune, pod, smurf, teardrop, mailbomb, processtable, udpstorm, apache2, worm|
|Probe||satan, ipsweep, nmap, portsweep, mscan, saint|
|R2L||guess_password, ftp_write, imap, phf, multihop, warezmaster, xlock, xsnoop, snmpguess, snmpgetattack, httptunnel, sendmail, named|
|U2R||buffer_overflow, loadmodule, rootkit, perl, sqlattack, xterm, ps|
|Tuesday||BForce, SFTP and SSH|
|Wednesday||DoS and Hearbleed Attacks|
Hulk and GoldenEye
|Thursday||Web and Infifiltration Attacks|
Web BForce, XSS and Sql Inject.
Infifiltration Dropbox Download
and Cool disk
|Friday||DDoS LOIT, Botnet ARES,|
PortScans (sS, sT, sF, sX, sN, sP, sV, sU, sO, sA, sW, sR, sL and B)
|Author/Year||FS Method||No. of Features||Detection Method||Dataset||Performance|
|Li et al.|
|IG + Chi2||6||Maximum Entropy Model|
|Nguyen et al.|
|KDDcup’99||ACC(%): 99.41 (C4.5)|
|Eid et al.|
|Wahba et al.|
|CFS + IG (Adaboost)||13||NB||NSL-KDD||ACC(%): 99.3|
|Shahbaz et al.|
|Ullah et al.|
|ACC(%): 99.70 (ISCX)|
|Kushwaha et al.|
|MI||5||Support vector machine (SVM)||KDDcup’99||ACC(%): 99.91|
|Moham madi et al.|
|MI||/||least square version of SVM (LSSVM)||KDDcup’99,|
99.11 (Kyoto 2006+)
|Wang et al.|
(MIFS + Symmetric Uncertainty)
|ACC(%): 99.85 (KDDcup’99)|
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Lyu, Y.; Feng, Y.; Sakurai, K. A Survey on Feature Selection Techniques Based on Filtering Methods for Cyber Attack Detection. Information 2023, 14, 191. https://doi.org/10.3390/info14030191
Lyu Y, Feng Y, Sakurai K. A Survey on Feature Selection Techniques Based on Filtering Methods for Cyber Attack Detection. Information. 2023; 14(3):191. https://doi.org/10.3390/info14030191Chicago/Turabian Style
Lyu, Yang, Yaokai Feng, and Kouichi Sakurai. 2023. "A Survey on Feature Selection Techniques Based on Filtering Methods for Cyber Attack Detection" Information 14, no. 3: 191. https://doi.org/10.3390/info14030191