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<p>Currently, the volume of malware grows faster each year and poses a thoughtful global security threat. The number of malware developed increases as computers became interconnected, at an alarming rate in the 1990s. This scenario resulted the increment of malware. It also caused many protections are built to fight the malware. Unfortunately, the current technology is no longer effective to handle more advanced malware. Malware authors have created them to become more difficult to be evaded from anti-virus detection. In the current research, Machine Learning (ML) algorithm techniques became more popular to the researchers to analyze malware detection. In this paper, researchers proposed a defense system which uses three ML algorithm techniques comparison and select them based on the high accuracy malware detection. The result indicates that Decision Tree algorithm is the best detection accuracy compares to others classifier with 99% and 0.021% False Positive Rate (FPR) on a relatively small dataset.</p>

Nowadays, computer network is very important because of the many advantages it has. However, it is also vulnerable to a lot of threats from attackers and the most common of such attack is the Distributed Denial of Service (DDoS) attack. This paper presents an overview of the existing detection and defense algorithms to mitigate four types of DDoS attacks and they are the UDP flood, TCP SYN flood, Ping of Death and Smurf attack. A detection and defense algorithm will be proposed in this paper and it will be evaluated using the existing Intrusion Detection and Prevention tool to determine whether it is the best algorithm to mitigate the DDoS attacks on a network environment. The proposed algorithm will be measured in terms of false positive rates and detection accuracy. Index Terms-DDoS, detection and defense algorithm, UDP flood, TCP SYN flood, ping of death and Smurf attack.