Investigating an Anomaly-based Intrusion Detection via Tree-based Adaptive Boosting Ensemble

Paul Avweresuo Onoma 1,*, Joy Agboi 2,, Victor Ochuko Geteloma 3,, Asuobite ThankGod Max-Egba 4,,

Andrew Okonji Eboka 5,, Arnold Adimabua Ojugo 6,, Christopher Chukwufunaya Odiakaoase 7,,

Eferhire Valentine Ugbotu 8,, Tabitha Chukwudi Aghaunor 9,, and Amaka Patience Binitie 10,

1,3,4,6 Department of Computer Science, Federal University of Petroleum Resources Effurun, Nigeria

2 Department of Computer Science, Delta State University Abraka, Nigeria

5,10 Department of Computer Science, Federal College of Education (Technical) Asaba, Nigeria

7,8 Department of Computer, Dennis Osadebay University Asaba, Nigeria

9 Department of Data Intelligence and Technology, Robert Morris University, Pittsburg, Pennsylvania, USA

10 Department of Data Science, University of Salford, United Kingdom

Email: 1 kenbridge14@gmail.com, 2 agboijoy0@gmail.com, 3 geteloma.victor@fupre.edu.ng,

4 max-egbaasuobite@fupre.edu.ng, 5 andrew.eboka@fcetasaba.edu.ng, 6 ojugo.arnold@fupre.edu.ng, 7 osegalaxy@gmail.com, 8 eferhire.ugbotu@gmail.com, 9 tabitha.aghaunot@gmail.com, 10 amaka.binitie@fcetasaba.edu.ng 

*Corresponding Author

Abstract—The eased accessibility, mobility, and portability of smartphones have caused the consequent rise in the proliferation of users' vulnerability to a variety of phishing attacks. Some users are more vulnerable due to factors like personality behavioral traits, media presence, and other factors. Our study seeks to reveal cues utilized by successful attacks by identifying web content as genuine and malicious data. We explore a sentiment-based extreme gradient boost learner with data collected over social platforms, scraped using the Python Google Scrapper. Our results show AdaBoost yields a prediction accuracy of 0.9989 to correctly classify 2148 cases with incorrectly classified 25 cases. The result shows the tree-based AdaBoost ensemble can effectively identify phishing cues and efficiently classify phishing lures against unsuspecting users from access to malicious content.

Keywords—Anomaly Detection; Intrusion; Machine Learning; Boosting Ensemble; Tree-based Algorithms

1. Introduction

Phishing utilizes multiple means like man-in-the-middle chat, forged links, and spoofed emails, amongst other means, to convince a user to divulge confidential data [1][2]. A major variant of phishing is spear phishing, which targets user email with links that seek to cleverly nudge an unsuspecting user to access malicious contents [3][4]; Thus, compromises a targeted user or device via malware download [5]. Phishing redirect users by exploiting vulnerabilities such as malware that is installed on the network infrastructure by an adversary [6][7]. Users are often redirected to fraudulent, spoofed websites without their knowledge cum consent. The socially-engineered attack consists of 3 elements [8][9]: (a) a lure feature from an adversary targets the unsuspecting user as a message that originates from a legitimate user on the network and is strengthened to exploit the unsuspecting victim’s fear, curiosity, and empathy [10], (b) a hook is an attachment cum compromised link component of the message [11], and (c) a catch feature of the malicious content is the exploit facet of how the adversary obtains the unsuspecting user's private data [12][13].

While this may seem quite simple [14] – the method is constantly evolving by adversaries to evade detection. Also, its continued proliferation has equipped adversaries to vary their attacks to varying degrees of diversity at sporadic rates that improve their rate of success [15]. Its features include: (a) the message makes unrealistic demands via intimidation [16], (b) the message stresses a catch intent [17], (c) the message is rippled with misspellings and poor grammar [18], (d) mismatch in URL that redirects to a spoofed site [19], and (e) message asks for user sensitive data [20], etc. Phishing utilizes 2 features that aid its understanding capability to identify malicious data [21][22]: (a) believability increases possibility of a user to believe the contents of a website and not identify the malicious cues [23], and (b) insidiousness ascertains the potency of each cue, and its rate of success in remaining undetectable [24]. Studies have even tackled phishing attacks on IoTs [25][26].

To minimize phishing, machine learning (ML) has been trained to effectively recognize phishing cues and lures as patterns. They learn to identify these cues as predictor features using anomaly detection from the behavior norms as outlier data, or detected as unusual activity signatures in valid transactions that indicate a fraudulent profile [27]. Some MLs used are: Random Forest [28], Deep Learning [29][30], Logistic Regression [31], SVM [32], etc. Tree methods are common, and from a decision tree [33] – a tree generates a series of if-else rules, a majority vote for prediction [34][35]. Using a recursive tree to partition predictor space, each tree groups predictor(s) into a distribution of dependent variable 𝑦 and predictor class x as homogeneous [36]. It constructs individually-trained trees to aggregate results into a stronger classifier that outperforms any single tree [37]; achieved via bagging [38][39] and boost [40]–[42] modes.

In boosting – tree(s) are constructed to achieve accuracy and performance by sequentially training its weak learners to correct its weaknesses [43][44] and in turn, yield a stronger classifier [45][46]. Common boost models are the adaptive boost [47], gradient boost [48], boosted logistic [49], and stochastic gradient boost [50][51]. In bagging, each decision tree is constructed and summed via bootstrap mode to independently train each tree so that it samples data via majority vote [52]. The Random Forest extends the bagging mode with an extra layer of randomness that alters how its RF trees are constructed so that, unlike the decision trees that have each node split amongst its predictor variables – the RF tree splits its nodes amongst randomly chosen predictors [53]; Thus, exploits its recursive nature to unveil intricate interactions in the dataset as predictor feat [54][55]. In all, tree-based ensembles have successfully proven to be better than other schemes [56] in traffic flow [57], churn prediction [58], and purchase intention [59]. They are known to reduce variance [60] and bias [61] inherent in a dataset. While some models easily get stuck at local minima [62], the weighted fusion of trees produced by ensemble methods [63][64] – often minimizes the inherent risk of choosing the wrong local minimum [65]. Table 1 lists ML contributions to phishing schemes so far:

1. Related Literature Contributions

Knowledge gaps inherent from previous works [71] as:

1. Lack of Datasets: Data access to the right quality, and the right format of domain dataset is a requisite for ML training and performance generalization – due to the limited availability of the dataset, which in turn, yields high error rates [72].
2. Imbalanced Datasets: A major issue with ML tasks is the imbalanced nature of its many datasets, which is also true with the case of phishing (as the minor class) where the data labels in the phishing (minor) class lag behind in its distribution frequency [40],[73][74]. Thus, a model must harness the robust flexibility in a tree-ensemble tailored to mitigating the challenges of data-imbalance [75][76].
3. Cross-Channel Detection: The new model must be able to handle and deal with the increased amount of transaction channels [77][78] vis-à-vis integrating various transaction data for enhanced generalization. Thus, cross-detection is now an imperative design and a focal area for businesses and researches [79][80] as traditional phishing detection modes are limited in adapting the emergent fraud patterns as well as keeping up with novel tactics.

2. Materials and Methods

Our proposed method uses the Adaptive boosting scheme as in Fig. 1.

1. Sentiment Analysis Process with Decision Support AdaBoost heuristics

1. Step 1: Dataset – were gathered via Google Play Scraper Library with a total of 8.693 records collected. Scrapped records include emails, compromised images-links-texts, posts, personal user data, likes/shares, etc – as reported and agreed by [81][82].
2. Step 2: Preprocessing cleans the dataset ensuring data integrity by removing redundant data, as well as data quality by removing missing data [83]. It restructures our dataset from its unstructured-to-normalized format [84] via the actions of word-stem, tokenization, and removal of stopwords [85] explained below as thus [22],[86]:

• Case Fold converts all words onto tokens – and is achieved to avoid 2-or-more tokens to report the same meaning even when treated differently by the ML due to its written form (as in lowercase and uppercase) [87].
• Stopword removes from the document – tokens that are common such as period, punctuation symbols, etc. This reduces the dataset dimension by resolving conjunctions, prepositions, and pronouns; And grouped as stopwords.
• Tokenization splits a sentence into smaller units cum token elements so that each yields implicit meaning that when analyzed, grants insights into their placement order in the text corpus. Thus, they form input as normalized texts for tokens, stopwords, and special characters [88][89], some of which are removed by this unit or phase whose output yields input for the next.
• Normalization extends/expands tokens to yield implicit meaning in their generic form. Thus, abridged words and slang/tokens are reverted to preserve their basic format. It expands tokens to their complete nature. For example, the term  ‘uwc’ is expanded to ‘you are welcomed’; while, ‘notin’ is expanded to ‘nothing’ [90]. Dictionaries used were as in the works [91].
• Word Stem removes all word and token pre-fixes cum affixes – and converts each word to its root text [92].

3. Step 3: Feature Extract – identifies and determines the token to be used as input predictors for text classification via ranked selection of all underlying features grouping them in order of importance. This in turn, yields reduced dimensions for the adopted model inputs, yields fastened model construction, reduces the training time dynamics to yield a model devoid of overfit, decreases the required computational complexity, improves generalization of the model, and enhances model accuracy [93][94]. We use Term Frequency Inverse Document Frequency to uncover relative probability scores for predictors; while ML schemes do not effectively recognize words as input – we use TD-IDF to convert each token into their numeric equivalence [95][96] by finding the occurrence in the frequency of each token in a document. A greater TF-value implies more frequency of such a token across the document [97]; while the IDF is the weighted sum of each token on the document so that the more a token appears, it yields a smaller IDF-value as expressed in (1), and in (2) [98] respectively:

4. Step 4: Machine Learning – We utilize the tree-based AdaBoost to effectively identify tokens into sentiments such as neutral, negative, and positive words-polarity according to the scrapped data. To train/test the dataset used [99] – our AdaBoost leverages the Gradient Boost scalability as it combines several weak learners to yield an optimal fit, that extends its objective function via minimized loss factor. This helps it to control its decision trees’ complexity – and combines predictive processing so that each base learner contributes to ensure Adaboost is a stronger regressor [100] with train data  and its corresponding labels  – as in (3).

For enhanced generalization – AdaBoost localizes its loss function () as in its goal/objective function, which is fused with regularization term (). The loss factor ensures the tree is not over-trained or overfitted. Its regularization function suits fit model complexity so that with each tree tuned, the AdaBoost yields enhanced generalization with higher accuracy for a simplified ensemble that is devoid of over-parameterization, over-fit, and improved generalization as in (4) [101][102].

3. Result Findings and Discussion

1. Data Pre-Processing

As in Fig. 2 – positive sentiment words used to identify cues/lures [103] depicted as compromised links, images, etc.

2. Training Phase

We perform TF-IDF vectorization to help the ensemble convert retrieved texts into vectors via Python's ScikitLearn TfidfVectorizer function. With hyper-predictors, we train AdaBoost using the training dataset on a trial-and-error approach to yield its best-values generalization for learn_rate = 0.25, estimators_n = 250, and depth_max = 5 as in Table 2.

2. Construction of Adaboost with Best Values

2. Frequency Chart of Positive Sentiment Words

1. AdaBoost Learner Evaluation

Our word sentiments were evaluated in Table 3 [104]:

3. AdaBoost Classifier Evaluation

Table 3 notes that cues and lures for negative sentiments were detected with harmonic mean (F1-score) of 0.9829 with an accuracy of 0.9792; while the cues and lures for positive sentiments were detected with an accuracy of 0.9981, and agrees with [105]. Such disparities in accuracy are expected due to false-and-true positives cum negatives [106][107]. Testing yields an accuracy of 0.9923 to detect cues/lures for both the positive and negative sentiments [108]–[110].

2. Discussion of Findings

Fig. 3 confusion matrix clearly shows the proposed AdaBoost ensemble efficiently and correctly classified 2148 cases; 20 incorrectly-classified with an accuracy of 0.9989 as compared to [111]. Each user interface as trust decision box that allows a user to trust (accept) or not trust (reject) via content-specific decisions. Our use of the semantic-text normalization [112][113] yielded improved performance as compared with [114].

3. Confusion Matrix for the Memetic Ensemble

Text normalization [115] did not degrade performance and agrees with [116]. Rather, it focuses on critical feats to successfully construct a model [117] to detect spoofed sites with reduced errors that will secure user resources and provide an enhanced user experience [118][119].

Table 4 shows sample cues in detecting malicious content such as emails, photo likes, posts, shares, etc as in agreement with [120][121].

4. Website Content for Sample Lures and Cues

Table 5 lists sophistication cues that make content harder to identify. The sample list is described below [101],[122] [123]:

S03: Contextual and Personal Data: user details known 🡪 Content-Personal_information-IsObfuscated

S04: Good Attachment: Generic boxes used 🡪 Context-Image-Professional

This study seeks to grasp how users make cum explore trusted decisions, identify cues and lures deficiencies in their decision/trust level, and adapt awareness campaigns cum training capabilities that will help prevent user susceptibility cum victimization, vis-à-vis associate such deficiencies with organizations where such users work [124]. The result yields mixed content for real-time user interactions with email, social media, and web-browser for which our participants’ responses to malicious phishing content cues as insider tactics to keep unsuspecting victims engaged online and interested to yield increased online presence [125]. Also, our participants were provided a rich interaction platform with hover capabilities over attachments that explored and felt like natural browser-like schemes and behaviour [126][127].

3. Comparison

Improved generalization for our proposed ensemble as used in phishing demonstrates the flexibility, robustness, and adaptability of the tree-based AdaBoost ensemble as in [128] [129], which we benchmark across studies that explored the same. This is as seen in Table 5 [130][131].

5. Benchmark / Comparative Testing of Method

While some domain-chosen datasets are much easier to identify; Others, in turn, have proven to be more painstaking and tedious such as medical classification and image identification tasks. This requires that the explored ensemble yields a design generalization and performance evaluation that is strongly correlated to its error rates within the captured dataset. Thus, explored heuristics often measure sensitivity and specificity as crucial predictors that directly relate to all clinical outcomes.

4. Conclusion

To protect users over social media platforms – designers have often explored the use of safeguards while utilizing ads to educate and campaign users against phishing. Where a case is reported, such is investigated, and where concluded that such is a potential phisher – the adversary is blacklisted. Thus, users are held accountable to report a case; while the platform is held responsible to investigate/blacklist potential adversaries vis-à-vis creating campaign awareness adverts that dissuades phishing attacks. Thus, platforms must have the capability to control and prevent attacks with actions or measures meted out to potential blacklisted users. This will help them stay ahead to limit such incidents. Furthermore, the consequent constant rise in technology and its widespread use to yield tech-rich business strategies with the proliferation of smartphones has consequently resulted in improved user productivity, greater efficiency, and business profitability.

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