An Empirical Evaluation On Comparative Machine Learning Techniques For Detection of The Distributed Denial of Service (DDoS) Attacks

Abstract

<jats:p>The advent of the Internet that aided the efficient sharing of resources. Also, it has introduced adversaries whom are today restlessly in their continued efforts at an effective, non-detectable means to invade secure systems, either for fun or personal gains. They achieve these feats via the use of malware, which is both on the rise, wreaks havoc alongside causing loads of financial losses to users. With the upsurge to counter these escapades, users and businesses today seek means to detect these evolving behavior and pattern by these adversaries. It is also to worthy of note that adversaries have also evolved, changing their own structure to make signature detection somewhat unreliable and anomaly detection tedious to network administrators. Our study investigates the detection of the distributed denial of service (DDoS) attacks using machine learning techniques. Results shows that though evolutionary models have been successfully implemented in the detection DDoS, the search for optima is an inconclusive and continuous task. That no one method yields a better optima than hybrids. That with hybrids, users must adequately resolve the issues of data conflicts arising from the dataset to be used, conflict from the adapted statistical methods arising from data encoding, and conflicts in parameter selection to avoid model overtraining, over-fitting and over-parameterization.</jats:p>

Forging a deep learning neural network intrusion detection framework to curb the distributed denial of service attack

“…Distributed denial of service (DDoS) is usually a carefully crafted attack, initiated to target network resource(s). Targeted directly at compromised clients on a network [12,20,21], an adversary gains access of such compromised client, seizing up resources such CPU time, bandwidth, memory etc-to exploit a network's weakness. An adversary achieves this, by inserting malware that sought to overwhelm the network with requests [20][21][22][23].…”

“…Targeted directly at compromised clients on a network [12,20,21], an adversary gains access of such compromised client, seizing up resources such CPU time, bandwidth, memory etc-to exploit a network's weakness. An adversary achieves this, by inserting malware that sought to overwhelm the network with requests [20][21][22][23]. Since the DDoS is carefully crafted and well-coordinated, the magnitude depends on the botnet size-which corresponds also to the severity of the attack [19][20][21].…”

“…An adversary achieves this, by inserting malware that sought to overwhelm the network with requests [20][21][22][23]. Since the DDoS is carefully crafted and well-coordinated, the magnitude depends on the botnet size-which corresponds also to the severity of the attack [19][20][21]. A DDoS seeks to exhaust targeted resources, deny 'uncompromised' clients' access to services and ensure a compromised network on a larger-scale.…”

“…DDoS attacks are easy to fix by manually disconnecting affected clients from the network, when detected. Detection schemes seek to stop a detected attack as close and as fast as possible to its source [15,20,21,[24][25][26][27].…”

Empirical Evidence of Socially-Engineered Attack Menace Among Undergraduate Smartphone Users in Selected Universities in Nigeria

“…Spams are well-organized, carefully crafted and unsolicited messages sent to a network user without their consent, whose merchandise is unsolicited adverts aimed at making money. Its risen trend continues to pose concern to security experts the world over [7,8,9]. When spams are targeted at personal gains, it is simply fraud.…”