Towards an Adversary-Aware ML-Based Detector of Spam on Twitter Hashtags

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ML-Based Detector of Spam on Twitter Hashtags</title> <meta name="description" content="Towards an Adversary-Aware ML-Based Detector of Spam on Twitter Hashtags"> <meta name="keywords" content="Twitter spam detection, adversarial examples, evasion attack, adversarial concept drift, account hijacking, trending hashtag"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img 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publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Towards an Adversary-Aware ML-Based Detector of Spam on Twitter Hashtags</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Niddal%20Imam">Niddal Imam</a>, <a href="https://publications.waset.org/abstracts/search?q=Vassilios%20G.%20Vassilakis"> Vassilios G. Vassilakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> After analysing messages posted by health-related spam campaigns in Twitter Arabic hashtags, we found that these campaigns use unique hijacked accounts (we call them adversarial hijacked accounts) as adversarial examples to fool deployed ML-based spam detectors. Existing ML-based models build a behaviour profile for each user to detect hijacked accounts. This approach is not applicable for detecting spam in Twitter hashtags since they are computationally expensive. Hence, we propose an adversary-aware ML-based detector, which includes a newly designed feature (avg posts) to improve the detection of spam tweets posted by the adversarial hijacked accounts at a tweet-level in trending hashtags. The proposed detector was designed considering three key points: robustness, adaptability, and interpretability. The new feature leverages the account鈥檚 temporal patterns (i.e., account age and number of posts). It is faster to compute compared to features discussed in the literature and improves the accuracy of detecting the identified hijacked accounts by 73%. <iframe src="https://publications.waset.org/abstracts/157771.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Twitter%20spam%20detection" title="Twitter spam detection">Twitter spam detection</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20examples" title=" adversarial examples"> adversarial examples</a>, <a href="https://publications.waset.org/abstracts/search?q=evasion%20attack" title=" evasion attack"> evasion attack</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20concept%20drift" title=" adversarial concept drift"> adversarial concept drift</a>, <a href="https://publications.waset.org/abstracts/search?q=account%20hijacking" title=" account hijacking"> account hijacking</a>, <a href="https://publications.waset.org/abstracts/search?q=trending%20hashtag" title=" trending hashtag"> trending hashtag</a> </p> <a href="https://publications.waset.org/abstracts/157771/towards-an-adversary-aware-ml-based-detector-of-spam-on-twitter-hashtags" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157771.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">78</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" 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Towards an Adversary-Aware ML-Based Detector of Spam on Twitter Hashtags