Applications in security and evasions in machine learning : a survey

In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications' perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers' knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks

Modeling Spammer Behavior: Artificial Neural Network vs. Naïve Bayesian Classifier

The exponential growth of spam emails in recent years is a fact of life. Internet subscribers world-wide are unwittingly paying an estimated €10 billion a year in connection costs just to receive “junk” emails, according to a study undertaken for the European Commission. Though there is no universal definition of spam, unwanted and unsolicited commercial email as a mass mailing to a large number of recipients is basically known as the junk email or spam to the internet community. Spams are considered to be potential threat to Internet Security. Spam's direct effects include the consumption of computer and network resources and the cost in human time and attention of dismissing unwanted messages. More importantly, these ever increasing spams are taking various forms and finding home not only in mailboxes but also in newsgroups, discussion forums etc without the consent of the recipients. Overflowing mailboxes are overwhelming users, causing newsgroups and discussion forums to be flooded with irrelevant or inappropriate messages. As a consequence, users are getting discouraged not to use them anymore though these systems can provide numerous benefits to them.Full Tex

A New Italian Cultural Heritage Data Set: Detecting Fake Reviews With BERT and ELECTRA Leveraging the Sentiment

Consiglio Nazionale delle Ricerche-CARI-CARE-ITALY’ within the CRUI CARE Agreemen