A Dependable Hybrid Machine Learning Model for Network Intrusion Detection

Network intrusion detection systems (NIDSs) play an important role in computer network security. There are several detection mechanisms where anomaly-based automated detection outperforms others significantly. Amid the sophistication and growing number of attacks, dealing with large amounts of data is a recognized issue in the development of anomaly-based NIDS. However, do current models meet the needs of today's networks in terms of required accuracy and dependability? In this research, we propose a new hybrid model that combines machine learning and deep learning to increase detection rates while securing dependability. Our proposed method ensures efficient pre-processing by combining SMOTE for data balancing and XGBoost for feature selection. We compared our developed method to various machine learning and deep learning algorithms to find a more efficient algorithm to implement in the pipeline. Furthermore, we chose the most effective model for network intrusion based on a set of benchmarked performance analysis criteria. Our method produces excellent results when tested on two datasets, KDDCUP'99 and CIC-MalMem-2022, with an accuracy of 99.99% and 100% for KDDCUP'99 and CIC-MalMem-2022, respectively, and no overfitting or Type-1 and Type-2 issues.Comment: Accepted in the Journal of Information Security and Applications (Scopus, Web of Science (SCIE) Journal, Quartile: Q1, Site Score: 7.6, Impact Factor: 4.96) on 7 December 202

Schedule design for liner shipping networks with port time windows

Containers are transported by global liner companies on regularly scheduled ship routes. A large variety of general cargos are containerized, such as manufactured products, food, and garment. Liner shipping services have fixed sequences of ports of call and fixed schedules, i.e., arrival and departure times at each port of call. Liner services are announced in advance to attract potential customers. Customers can arrange the delivery of their cargo based on the available date of the cargo at the origin port and the expected arrival date at the destination port. Therefore, container liner shipping is of significant importance to the global supply chain network. Different schedules mean different sailing times between ports, which dictate different sailing speeds. It is known in the shipping industry that the daily fuel consumption of ships increases approximately proportional to the sailing speed cubed. Therefore, schedule design affects the bunker fuel consumption and thereby air pollutant emission. Reducing the fuel consumption will also improve the sustainability of the global container transportation network. Container shipping lines provide weekly services for transporting containers, which means that the rotation time in terms of weeks for visiting all ports of call on a ship route is equal to the number of ships deployed. As a consequence, each port of call has a ship departure on the same day every week. When the speed of ships is higher, the rotation time is shorter, and hence fewer ships are required to maintain the weekly frequency. The objective of this thesis is to develop mathematical models and solution algorithms for designing the schedules of container liner shipping services. The aim is to minimize the sum of ship cost, fuel cost and inventory cost, while ensuring that ports are available to serve the ships on the planned days

Profile-based virtual machine management for more energy-efficient data centers

This research develops a resource management framework for improved energy efficiency in cloud data centers through energy-efficient virtual machine placement to physical machines as well as application assignment to virtual machines. The study investigates static virtual machine placement, dynamic virtual machine placement and application assignment using ant colony optimization to minimize the total energy consumption in data centers

Profile-based static virtual machine placement for energy-efficient data center

The energy consumption of a data center and hence the carbon footprint from it largely depends on the energy consumption by its active Physical Machines (PMs). Researchers have taken many attempts to minimize the data center energy consumption through the Virtual Machines (VMs) allocation into a minimal number of PMs of homogeneous types. However, the current VM placement strategies do not consider the fluctuations of resource requirements of a VM through its lifetime. To resolve the this issue, this paper introduces a novelty of profile-based VM assignment algorithm for minimizing the energy consumption in data center. Our algorithm considers the subsequent time intervals of data center based on profiling of VMs and PMs. An algorithm has been proposed and developed for finding near optimal solution for VMs placement with the objective of minimizing data center energy consumption. Our algorithm has been compared with a bin packing algorithm, First-Fit Decreasing (FFD), and experimental results have shown that our algorithm can reduce more energy consumption than the FFD algorithm and is scalable for larger test problems

Profile-based ant colony optimization for energy-efficient virtual machine placement

Cloud computing data centers contain a large number of physical machines (PMs) and virtual machine (VMs). This number can increase the energy consumption of the data centers especially when the VMs placed inappropriately on the PMs. This paper presents a new VM placement approach with the objective of minimizing the total energy consumption of a data center. VM placement problem is formulated as a combinatorial optimization problem. Since this problem has been proven to be an NP hard problem, Ant Colony Optimization (ACO) algorithm is adopted to solve the formulated problem. Information heuristic of ACO is used differently based on PM energy efficiency. Experimental results show that the proposed approach scales well on large data centers and significantly outperforms selected benchmark (ACOVMP) in terms of energy consumption

Profile-based static virtual machine placement for energy-efficient data center

The energy consumption of a data center and hence the carbon footprint from it largely depends on the energy consumption by its active Physical Machines (PMs). Researchers have taken many attempts to minimize the data center energy consumption through the Virtual Machines (VMs) allocation into a minimal number of PMs of homogeneous types. However, the current VM placement strategies do not consider the fluctuations of resource requirements of a VM through its lifetime. To resolve the this issue, this paper introduces a novelty of profile-based VM assignment algorithm for minimizing the energy consumption in data center. Our algorithm considers the subsequent time intervals of data center based on profiling of VMs and PMs. An algorithm has been proposed and developed for finding near optimal solution for VMs placement with the objective of minimizing data center energy consumption. Our algorithm has been compared with a bin packing algorithm, First-Fit Decreasing (FFD), and experimental results have shown that our algorithm can reduce more energy consumption than the FFD algorithm and is scalable for larger test problems

Profile-based ant colony optimization for energy-efficient virtual machine placement for energy-efficient data centers

Cloud computing data centers contain a large number of physical machines (PMs) and virtual machine (VMs). This number can increase the energy consumption of the data centers especially when the VMs placed inappropriately on the PMs. This paper presents a new VM placement approach with the objective of minimizing the total energy consumption of a data center. VM placement problem is formulated as a combinatorial optimization problem. Since this problem has been proven to be an NP hard problem, Ant Colony Optimization (ACO) algorithm is adopted to solve the formulated problem. Information heuristic of ACO is used differently based on PM energy efficiency. Experimental results show that the proposed approach scales well on large data centers and significantly outperforms selected benchmark (ACOVMP) in terms of energy consumption

Knowledge, attitudes and use of anabolic-androgenic steroids among male gym users: A community based survey in Riyadh, Saudi Arabia

Recreational use of anabolic-androgenic steroids (AAS) is a growing worldwide public health concern. However, studies assessing the level of awareness and knowledge of its effects on health are fairly limited, especially in developing countries, including Saudi Arabia. This community-based cross-sectional study was conducted to assess knowledge, attitudes and practices among male gym members toward AAS in Riyadh (Saudi Arabia) from March to October 2016. Twenty gyms were randomly selected from four different geographical regions (clusters) within Riyadh. In total, 482 participants responded to the self-administered anonymous questionnaire, which covered socio-demographic data, data assessing knowledge, attitude and behavior related to AAS use. The mean (±standard deviation) age of study participants was 27.2 (±6.9) years. Among these, 29.3% of participants reported having used AAS, while the majority (53.5%) reported hearing of AAS use, mostly through friends. Most study participants reported awareness of the effects of AAS on muscle mass, body weight and muscles strength (53.2%, 51.1% and 45.5%, respectively). In contrast, a higher proportion of study participants were unaware of the side-effects of AAS use. A high proportion of study participants (43.2%) reported that they had been offered AAS and 68.7% believed that AAS are easily accessible. Most of the gym users (90.1%) reported never having used any narcotics or psychoactive drugs. Regression analysis revealed that use of anabolic-androgenic steroids is significantly associated with “weight lifting practice” OR [95%CI] = 1.9[1.02 − 3.61], P = 0.044; “using supplementary vitamins, OR [95%CI] = 7.8[4.05 − 15.03], P < 0.0001, knowing anyone using anabolic-androgenic steroids’ OR [95%CI] = 7.5[3.78 − 14.10], P < 0.0001, and someone advised Gym users to take anabolic-androgenic steroids” OR [95%CI] = 2.26[1.23 − 4.14], P < 0.008. Our findings suggest that the level of awareness regarding the possible side-effects of AAS is fairly limited. Thus, efforts directed toward educating the public and limiting access to AAS as well as health policy reforms are crucial to reduce future negative implications of AAS use. Keywords: Androgenic anabolic steroids, Steroids, Drug abuse, Steroid abuse, Gym users, Addiction, Substance abuse, Saudi Arabi

An Ant Colony System for energy-efficient dynamic Virtual Machine Placement in data centers

Data centers are fundamental infrastructure for information technology and cloud services; however, their very high rates of energy consumption are a problem. The placement of Virtual Machines (VMs) to Physical Machines (PMs) in virtualized environments has a significant impact on the energy consumption of a data center. This is an NP-hard problem, for which an optimal solution is not practicable even for a small-scale data center. In this paper, we formulate placement of VMs to PMs in a data center as a constrained combinatorial optimization problem and make use of the information from PM and VM profiles to minimize the total energy consumption of all active PMs. An Ant Colony System (ACS) embedded with new heuristics is presented for an energy-efficient solution to the optimization problem. To demonstrate the effectiveness of the ACS, simulation experiments are conducted on small-, medium- and large-scale data centers. The results from our ACS are compared with two existing ACS methods as well as the widely used First-Fit-Decreasing (FFD) algorithm. Our ACS is shown to outperform the two existing ACS methods and FFD in energy performance for all small-, medium- and large-scale test problems. Our ACS also exhibits good scalability with the increase in the problem size