Malware Detection Method Based on Improved Harris Hawks Optimization Synchronization Optimization Feature Selection

doi:10.3969/j.issn.1671-1122.2021.12.002

Abstract

Abstract:

Aiming at the difficulty of feature selection and model parameter tuning in malware detection field, a malware detection method based on improved Harris Hawks Optimization (HHO) synchronous optimization feature selection is proposed. The adaptive elite reverse learning strategy, circle chaos energy factor and random dimensional quantum revolving door mutation strategy are introduced into HHO algorithm to enhance its global exploration and local development ability and improve the convergence accuracy and stability of the algorithm. Extreme Gradient Boosting (XGBoost) is an improved Harris Hawks optimization (IHHO) algorithm for simultaneous optimization of classification parameters and feature selection, in order to build a malware detection model based on network traffic characteristics. Finally, the improved algorithm is used to extract feature subset and optimize model parameters of CICInvesAndMal2019 dataset. The results show that IHHO can select higher quality feature subset and improve the classification ability of malware detection model.

Key words: Harris Hawks optimization algorithm, malware detection, flow characteristics, elite opposition based learning, sines and cosines strategy

CLC Number:

TP309

XU Guotian, LIU Mengmeng. Malware Detection Method Based on Improved Harris Hawks Optimization Synchronization Optimization Feature Selection[J]. Netinfo Security, 2021, 21(12): 9-18.

Figures/Tables 10

公式	维度	范围
$F1\left( x \right)=\underset{i=1}{\overset{n}{\mathop \sum }}\,{{x}_{i}}^{2}$	30	[-100,100]
$F2\left( x \right)=\underset{i=1}{\overset{n}{\mathop \sum }}\,\left\| {{x}_{i}} \right\|+\underset{i=1}{\overset{n}{\mathop \prod }}\,\left\| {{x}_{i}} \right\|$	30	[-10,10]
$F3\left( x \right)=\underset{i=1}{\overset{n}{\mathop \sum }}\,{{\left( \underset{j=i}{\overset{i}{\mathop \sum }}\,{{x}_{j}} \right)}^{2}}$	30	[-100,100]
$F4\left( x \right)=\text{ma}{{\text{x}}_{i}}\left\{ \left\| {{x}_{i}} \right\|,1\le i\le n \right\}$	30	[-100,100]
$F5\left( x \right)=\underset{i=1}{\overset{n-1}{\mathop \sum }}\,\left[ 100{{\left( {{x}_{i+1}}-{{x}_{i}}^{2} \right)}^{2}}+{{\left( {{x}_{i}}-1 \right)}^{2}} \right]$	30	[-1.28,1.28]
$F6\left( x \right)=\frac{1}{4000}\underset{i=1}{\overset{n}{\mathop \sum }}\,{{x}_{i}}^{2}-\underset{i=1}{\overset{n}{\mathop \prod }}\,\cos \left( \frac{{{x}_{i}}}{\sqrt{i}} \right)+1$	30	[-600,600]
$F7\left( x \right)=\underset{i=1}{\overset{d}{\mathop \sum }}\,\left\| {{x}_{i}}\text{sin}\left( {{x}_{i}} \right)+0.1{{x}_{i}} \right\|$	30	[-10,10]
$F8\left( x \right)=1-\text{cos}\left( 2\text{ }\!\!\pi\!\!\text{ }\sqrt{\underset{i=1}{\overset{d}{\mathop \sum }}\,{{x}_{i}}^{2}} \right)+0.1\sqrt{\underset{i=1}{\overset{d}{\mathop \sum }}\,{{x}_{i}}^{2}}$	30	[-100,100]

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测试函数	算法	IHHO	HHO	SSA	WOA	CEHHO
F1	Average	0.00E+00	3.02E-98	5.11E-46	1.51E-71	9.64E-111
F1	St.d	0.00E+00	1.58E-98	2.67E-46	6.92E-71	4.58E-111
F2	Average	1.71E-258	7.56E-50	6.73E-28	8.94E-45	1.24E-57
F2	St.d	0.00E+00	2.10E-49	5.51E-28	2.34E-45	8.37E-57
F3	Average	0.00E+00	1.28E-77	4.35E-34	4.45E+04	3.54E-95
F3	St.d	0.00E+00	7.02E-77	1.54E-34	1.53E+04	5.81E-95
F4	Average	2.96E-258	1.86E-47	8.19E-31	4.91E+01	4.96E-61
F4	St.d	0.00E+00	9.19E-47	3.36E-31	3.08E+01	8.34E-61
F5	Average	5.56E-06	8.94E-05	1.70E-03	2.99E-03	8.64E-05
F5	St.d	8.92E-07	9.91E-05	1.01E-03	3.20E-03	2.29E-05
F6	Average	0.00E+00	9.02E-05	0.00E+00	0.00E+00	0.00E+00
F6	St.d	0.00E+00	1.85E-05	0.00E+00	0.00E+00	0.00E+00
F7	Average	2.78E-256	1.53E-56	7.35E-20	1.83E-46	3.28E-42
F7	St.d	0.00E+00	5.65E-56	2.52E-21	8.89E-46	7.15E-42
F8	Average	0.00E+00	3.36E-49	5.92E-35	1.34E-42	6.06E-77
F8	St.d	0.00E+00	1.38E-48	3.11E-35	6.87E-41	2.59E-77

数据集名	样本数	特征数	类别数
wine	178	13	3
segment	2310	19	7
spectfheart	267	44	2
sonar	208	60	2
CIAM	2000	65	2

算法	wine		segment		spectfheart		sonar		CICInvesAndMal
算法	特征数	Acc	特征数	Acc	特征数	Acc	特征数	Acc	特征数	Acc
卡方检验	4	0.907	5	0.910	14	0.814	15	0.777	15	0.725
互信息法	4	0.907	5	0.916	14	0.839	15	0.841	15	0.786
RFRFE	4	0.907	5	0.919	14	0.814	15	0.809	15	0.795
XGBoostF	4	0.907	5	0.975	14	0.862	15	0.841	15	0.801
WOA	5.6	0.963	4.8	0.972	17.2	0.887	18.9	0.929	14.3	0.828
SSA	3.7	0.957	5.5	0.935	17.3	0.895	25.8	0.938	14.9	0.816
HHO	4.5	0.968	4.6	0.973	15.7	0.889	21.1	0.942	13.5	0.820
CEHHO	3.8	0.968	5.1	0.981	16.5	0.912	18.9	0.936	13.8	0.825
IHHO	3.7	0.972	4.6	0.978	12.4	0.905	15.6	0.968	13.2	0.831

算法	特征数	Acc	适应度	运行时间/ms
NB	9.5	0.628	0.348	9.4
RF	30.5	0.808	0.215	317
SVM	25.8	0.633	0.387	164
LightGBM	21.2	0.827	0.185	86.3
XGBoost	13.6	0.832	0.171	88.3