Software development is a dynamic process and is characterized by change. Software projects often begin with unclear, ambiguous, and incomplete requirements which give rise to intrinsic volatility. Constant change in requirements is one of the main causes of software defects and a major issue faced by the software industry. This paper describes the findings of our research-based study that investigates the impact of both the pre-release and post-release requirements changes on overall defects by defining measures, collecting data against those measures and analyzing the collected data through statistical techniques. Our findings, based on industry data from 4 software projects consisting of 30 releases, all in ecommerce domain, indicate that there is a significant relationship between pre/post release change requests initiated by the client and software defects. In addition, our data analysis indicates that changes in the design of the system at the later stages of software development i.e., during coding, testing and after release have a significant impact on the high severity defects that affect the major functionality of the system. Also, we found that insufficient time spent on the design phase and inadequate communication with the client could be some of the causes of requirements changes and consequently software defects.

At the advent of advanced wireless technology and contemporary computing paradigms, Distributed Denial of Service (DDoS) attacks on Web-based services have not only increased exponentially in number, but also in the degree of sophistication; hence the need for detecting these attacks within the ocean of communication packets is extremely important. DDoS attacks were initially projected toward the network and transport layers. Over the years, attackers have shifted their offensive strategies toward the application layer. The application layer attacks are potentially more detrimental and stealthier because of the attack traffic and the benign traffic flows being indistinguishable. The distributed nature of these attacks is difficult to combat as they may affect tangible computing resources apart from network bandwidth consumption. In addition, smart devices connected to the Internet can be infected and used as botnets to launch DDoS attacks. In this paper, we propose a novel deep neural network-based detection mechanism that uses feed-forward back-propagation for accurately discovering multiple application layer DDoS attacks. The proposed neural network architecture can identify and use the most relevant high level features of packet flows with an accuracy of 98% on the state-of-the-art dataset containing various forms of DDoS attacks.

One of the most challenging tasks for clinicians is detecting symptoms of cardiovascular disease as earlier as possible. Many individuals worldwide die each year from cardiovascular disease. Since heart disease is a major concern, it must be dealt with timely. Multiple variables affecting health, such as excessive blood pressure, elevated cholesterol, an irregular pulse rate, and many more, make it challenging to diagnose cardiac disease. Thus, artificial intelligence can be useful in identifying and treating diseases early on. This paper proposes an ensemble-based approach that uses machine learning (ML) and deep learning (DL) models to predict a person’s likelihood of developing cardiovascular disease. We employ six classification algorithms to predict cardiovascular disease. Models are trained using a publicly available dataset of cardiovascular disease cases. We use random forest (RF) to extract important cardiovascular disease features. The experiment results demonstrate that the ML ensemble model achieves the best disease prediction accuracy of 88.70%.

Due to the widespread use of the internet and smart devices, various attacks like intrusion, zero-day, Malware, and security breaches are a constant threat to any organization's network infrastructure. Thus, a Network Intrusion Detection System (NIDS) is required to detect attacks in network traffic. This paper proposes a new hybrid method for intrusion detection and attack categorization. The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization. In the first step, the dataset is preprocessed through the data transformation technique and min-max method. Secondly, the random forest recursive feature elimination method is applied to identify optimal features that positively impact the model's performance. Next, we use various Support Vector Machine (SVM) types to detect intrusion and the Adaptive Neuro-Fuzzy System (ANFIS) to categorize probe, U2R, R2U, and DDOS attacks. The validation of the proposed method is calculated through Fine Gaussian SVM (FGSVM), which is 99.3% for the binary class. Mean Square Error (MSE) is reported as 0.084964 for training data, 0.0855203 for testing, and 0.084964 to validate multiclass categorization.

increase productivity 3 by 65% (Jones, 2000). Lamsweerde (2000) conducted a survey of more than 8,000 projects from 350 U.S. companies and revealed that one third of the projects were never completed and one half succeeded only partially; that is, with partial functionalities, major cost overruns, and significant delays. When asked about the causes of such failures, executive managers identified poor requirements as the major source of problems (about half of the responses)-more specifically, lack of user involvement (13%), requirements incompleteness (12%), changing requirements (11%), unrealistic expectations (6%), and unclear objectives (5%).