The absence of standards and the diverse nature of the Internet of Things (IoT) have made security and privacy concerns more acute. Attacks such as distributed denial of service (DDoS) are becoming increasingly widespread in IoT, and the need for ways to stop them is growing. The use of newly formed Software-Defined Networking (SDN) significantly lowers the computational burden on IoT network nodes and makes it possible to perform more security measurements. This paper proposes an SDNbased, four-module DDoS attack detection and mitigation framework for IoT networks called FMDADM. The proposed FMDADM framework comprises four main modules and five-tier architecture. The first module implements an early detection process based on the average drop rate (ADR) principle using a 32packet window size. The second module uses a novel double-check mapping function (DCMF), that aids in earlier attack detection at the data plane level. The third module is an ML-based detection application comprising four phases: data preprocessing, feature extraction, training and testing, and classification. This module detects DDoS attacks using only seven features: two selected and five newly computed features. The last module introduces an attack mitigation process. We applied the proposed framework to three test cases: one single-node attack test case and two multi-node attack test cases, all with real IoT traffic generated and deployed in Mininet-IoT. The proposed FMDADM framework efficiently detects DDoS attacks at high and low rates, can discriminate between attack traffic and flash crowds, and protects both local and remote IoT nodes by preventing infection from propagating to the ISP level. The FMDADM outperformed most existing cutting-edge approaches across ten different evaluation criteria. According to the experimental results, FMDADM achieved the following accuracy,

Software defined networks (SDN) are a recently developed form for controlling network management by providing centralized control unit called the Controller. This master Controller is a great power point but at the same time it is unfortunately a failure point and a serious loophole if it is targeted and dropped by attacks. One of the most serious types of attacks is the inability to access the Controller, which is known as the distributed denial of service (DDoS) attack. This research shows how DDoS attack can deplete the resources of the Controller and proposes a lightweight mechanism, which works at the Controller and detects a DDoS attack in the early stages. The proposed mechanism can not only detect the attack, but also identify attack paths and initiate a mitigation process to provide some degree of protection to network devices immediately after the attack is detected. The proposed mechanism depends on a hybrid technique that merges between the average flow initiation rate, and the flow specification of the coming traffic to the network.

We present two protocols for information exchange between multiple identical senders and a single receiver. At each instant, every sender sends one bit, and the bits from all of senders are or-ed together into one bit before being received by the receiver. If a sender has a data message to send, it sends the message bits one by one: otherwise it sends zero bits. Clearly, if the sending of two messages by two senders overlap, then the resulting "collision" can result in a corrupted message, i.e., one that was not sent by either sender. The function of the protocol is to deliver those and only those messages that are not corrupted by collision. (In other words, the receiver acts as a discriminating seine that catches and delivers only uncorrupted messages; hence the title.) The two protocols presented here are based on Manchester codes and general balanced codes, respectively.