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I Have Some Network Skills and I Want to Build a Network Intrusion Detection System with Python can Someone Give me Some Resources and Documents to Start

          Sure, our resource team will provide the complete research support for the research scholars to enhance their network skills and build the network intrusion detection system using python. Let’s start this article with the list of algorithms that are deployed to implement the network intrusion detection system.

Algorithms to Detect Intrusions

  • Fully connected feed forward neural network
  • CNN with metaheuristics algorithm
  • Improved GAN
  • Attention based neural network
  • Hybrid CNN and LSTM

         Along with that, our technical professionals have highlighted some list of topics in network intrusion detection systems which are implemented by our development team.

Emerging Research Topics in IDS

  • False data injection attack detection model for cyber-attack resilient energy forecasting
  • Adaptive network security system for IoT enabled maritime transportation
  • Transfer learning approach to IDS on cloud IoT devices using optimized CNN
  • Novel detection approach of unknown cyber-attacks for intra vehicle networks using recurrence plots and neural networks
  • In vehicle CAN bus tampering attacks detection for connected and autonomous vehicles using an improved isolation forest method
  • Optimization of intrusion detection using likely point PSO and enhanced LSTM-RNN hybrid technique in communication networks
  • Cyber-attack correlation and mitigation for distribution systems via machine learning
  • Novel online network intrusion detection system for industrial IoT based on OI-SVDD and AS-ELM
  • Deep active learning intrusion detection and load balancing in software-defined vehicular networks
  • Dew cloud based hierarchical federated learning for intrusion detection in IoMT
  • TOW-IDS: Intrusion detection system based on three overlapped wavelets for automotive Ethernet
  • Online approach to covert attack detection and identification in power systems

         When we are addressing the resource to build intrusion detection system, we have provided the source code in python.

Python Based Resource for Network IDS

def model_perfomance(y_pred, y_prob, y_true):
tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
recall = tp / (tp+fn)
specificity = tn / (tn+fp)
gmean = np.sqrt(recall * specificity)
auc = roc_auc_score(y_true, y_prob)
f_score = f1_score(y_true, y_pred)
coverage = weighted_coverage(y_true, y_prob)
acc = accuracy_score(y_true, y_pred)
performance = [auc, coverage, f_score, gmean, recall, acc]
return np.array(performance)
rf = RandomForestClassifier(n_estimators=350, max_depth=6, random_state=2018)
xgb = XGBClassifier(n_estimators=350, learning_rate=0.25, max_depth=6, n_jobs=-1, random_state=2018)
svm = SVC(C=1.0, kernel='rbf', gamma='auto', probability=True, degree=3, random_state=2018)
lr = LogisticRegression(penalty='l2', C=1.0, solver='lbfgs', max_iter=1200, random_state=2019, n_jobs=-1)
def performance_contrast(seed, cost_fn=1.5, cost_fp=1.0, clf_one=xgb, clf_two=rf):
start = time.time()
P, U, U_label = generate_pudata(seed)
print('Seed:{:}, P Shape:{:}, U Shape:
{:}'.format(seed, P.shape, U.shape))
adoa = ADOA(P, U, clf_one)
y_pred, y_prob = adoa.predict()
adoa_performance = model_perfomance(y_pred, y_prob, U_label)
X_train = np.r_[P, U]
y_train = np.r_[np.ones(len(P)),
svm.fit(X_train, y_train, sample_weight=[cost_fn if i else cost_fp for i in y_train])
y_pred, y_prob = svm.predict(U),
svm.predict_proba(U)[:, -1] svm_performance = model_perfomance(y_pred, y_prob, U_label)
pos_weight = len(U) / len(X_train)
neg_weight = 1 - pos_weight lr.fit(X_train, y_train, sample_weight=[pos_weight if i else neg_weight for i in y_train])
y_pred, y_prob = lr.predict(U),
lr.predict_proba(U)[:, -1]
lr_performance = model_perfomance(y_pred, y_prob, U_label)
pul_csl= pul(P, U, cost_fn=cost_fn, cost_fp=cost_fp, clf_one=clf_one, clf_two=clf_two, over_sample=False)
y_pred, y_prob = pul_csl.predict()
pul_csl_performance = model_perfomance(y_pred, y_prob, U_label)
metrics = ['AUC', 'Coverage', 'F1_Score', 'G_Mean', 'Recall', 'ACC']
models = ['ADOA', 'Biased_SVM', 'Weighted_LR', 'PUL_CostSensitive']
list_ = [adoa_performance, svm_performance, lr_performance, pul_csl_performance]
performance = pd.DataFrame(list_, columns=metrics, index=models)
algorithms = [performance[i].idxmax() for i in metrics]
performance.loc['The Best Algorithm', :] = algorithms
decription = 'The evaluation of the algorithm has been completed.'
print(decription, 'Running_Time:
return performance
seeds = np.random.RandomState(2019).choice(range(1000), size=10, replace=False)
contrast = [performance_contrast(seed) for seed in seeds]
contrast_concat = np.concatenate([contrast[i] for i in range(len(contrast))])
data_names = np.array([['Dataset_' + str(i)]*5 for i in range(len(seeds))]).ravel()
models = ['ADOA', 'Biased_SVM',
'Weighted_LR', 'PUL_CostSensitive',
'The Best Algorithm'] * len(seeds) #
metrics = ['AUC', 'Coverage', 'F1_Score', 'G_Mean', 'Recall', 'ACC']
arrays = [data_names, models]
idx = pd.MultiIndex.from_arrays(arrays, names=('VerifyData', 'Algorithm'))
contrast_result = pd.DataFrame(contrast_concat, index=idx, columns=metrics)
best_algo = contrast_result.query("Algorithm == 'The Best Algorithm'")

Execution of Python Based IDS

       We have to open the Python IDE and the file has to be opened as mentioned in the following image.

Opening Python Source Code

        Following that, we have to browse the python source code file to proceed with the execution process.

Browsing Python Source Code File

          Finally, we have to execute the python IDE source code just by clicking the option namely, run module.

Execution of Python IDS

        If you guys have to know more about the implementation of intrusion detection system, then reach us and acquire more.