Drive-by download refers to attacks that automatically download malwares to user's computer without his knowledge or consent. This type of attack is accomplished by exploiting web browsers and plugins vulnerabilities. The damage may include data leakage leading to financial loss. Traditional antivirus and intrusion detection systems are not efficient against such attacks. Researchers proposed plenty of detection approaches mostly passive blacklisting. However, a few proposed dynamic classification techniques, which suffer from clear shortcomings. In this paper, we propose a novel approach to detect drive-by download infected web pages based on extracted features from their source code. We test 23 different machine learning classifiers using data set of 5435 webpages and based on the detection accuracy we selected the top five to build our detection model. The approach is expected to serve as a base for implementing and developing anti drive-by download programs. We develop a graphical user interface program to allow the end user to examine the URL before visiting the website. The Bagged Trees classifier exhibited the highest accuracy of 90.1% and reported 96.24% true positive and 26.07% false positive rate.
Drive-by download refers to attacks that automatically download malwares to user's computer without his knowledge or consent. This type of attack is accomplished by exploiting web browsers and plugins vulnerabilities. The damage may include data leakage leading to financial loss. Traditional antivirus and intrusion detection systems are not efficient against such attacks. Researchers proposed plenty of detection approaches mostly passive blacklisting. However, a few proposed dynamic classification techniques, which suffer from clear shortcomings. In this paper, we propose a novel approach to detect drive-by download infected web pages based on extracted features from their source code. We test 23 different machine learning classifiers using data set of 5435 webpages and based on the detection accuracy we selected the top five to build our detection model. The approach is expected to serve as a base for implementing and developing anti drive-by download programs. We develop a graphical user interface program to allow the end user to examine the URL before visiting the website. The Bagged Trees classifier exhibited the highest accuracy of 90.1% and reported 96.24% true positive and 26.07% false positive rate.
Internet of Things (IoT) products became recently an essential part of any home in conjunction with the great advancements in internet speeds and services. The invention of IoT based devices became an easy task that could be performed through the widely available IoT development boards. Raspberry Pi is considered one of the advanced development boards that have high hardware capabilities with a reasonable price. Unfortunately, the security aspect of such products is overlooked by the developers, revealing a huge amount of threats that result in invading the privacy and the security of the users. In this research, we directed our study to SSH due to its extensive adoption by the developers. It was found that due to the nature of the Raspberry Pi and development boards, the Raspberry Pi generates predictable and weak keys which make it easy to be utilized by MiTM attack. In this paper, Man in The Middle (MiTM) attack was conducted to examine the security of different variations provided by the SSH service, and various hardening approaches were proposed to resolve the issue of SSH weak implementation and weak keys.
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