Results (18)
Search Parameters:
Keyword: AnomalyEnhancing the Network Anomaly Detection using CNN-Bidirectional LSTM Hybrid Model and Sampling Strategies for Imbalanced Network Traffic Data
The cybercriminal utilized the skills and freely available tools to breach the networks of internet-connected devices by exploiting confidentiality, integrity, and availability. Network anomaly detection is crucial for ensuring the security of information resources. Detecting abnormal network behavior poses challenges because of the extensive data, imbalanced attack class nature, and the abundance of features in…
Read MoreOptimizing the Performance of Network Anomaly Detection Using Bidirectional Long Short-Term Memory (Bi-LSTM) and Over-sampling for Imbalance Network Traffic Data
Cybercriminal exploits integrity, confidentiality, and availability of information resources. Cyberattacks are typically invisible to the naked eye, even though they target a wide range of our digital assets, such as internet-connected smart devices, computers, and networking devices. Implementing network anomaly detection proves to be an effective method for identifying these malicious activities. The traditional anomaly…
Read MoreDevelopment of an Intelligent Road Anomaly Detection System for Autonomous Vehicles
Globally, road transportation has become one of the most reliable means of moving goods and services from one place to the other. It has contributed immensely to the standard of living and modern civilization. However, this means of transportation is characterised by some issues which are poised to be harmful to the human population if…
Read MoreImproved Detection of Advanced Persistent Threats Using an Anomaly Detection Ensemble Approach
Rated a high-risk cyber-attack type, Advanced Persistent Threat (APT) has become a cause for concern to cyber security experts. Detecting the presence of APT in order to mitigate this attack has been a major challenge as successful attacks to large organizations still abound. Our approach combines static rule anomaly detection through pattern recognition and machine…
Read MoreDecision Making System for Improving Firewall Rule Anomaly Based on Evidence and Behavior
Firewalls are controlled by rules which often incur anomalies. The anomalies are considered serious problems that administrators do not desire to happen over their firewalls because they cause more vulnerabilities and decrease the overall performance of the firewall. Resolving anomaly rules that have already occurred on the firewall is difficult and mainly depends on the…
Read MoreNonlinear \(\ell_{2,p}\)-norm based PCA for Anomaly Network Detection
Intrusion detection systems are well known for their ability to detect internal and external intrusions, it usually recognizes intrusions through learning the normal behaviour of users or the normal traffic of activities in the network. So, if any suspicious activity or behaviour is detected, it informs the users of the network. Nonetheless, intrusion detection system…
Read MoreImproved Nonlinear Fuzzy Robust PCA for Anomaly-based Intrusion Detection
Among the most popular tools in security field is the anomaly based Intrusion Detection System (IDS), it detects intrusions by learning to classify the normal activities of the network. Thus if any abnormal activity or behaviour is recognized it raises an alarm to inform the users of a given network. Nevertheless, IDS is generally susceptible…
Read MoreA Systematic Literature Review of Vessel Anomaly Behavior Detection Methods Based on Automatic Identification System (AIS) and another Sensor Fusion
The high flow of vessel traffic affects the difficulty of monitoring vessel in the middle of the sea because of limited human visibility, occurrence of vessel accidents at the sea and other illegal activities that illustrate abnormal vessel behavior such as oil bunkering, piracy, illegal fishing and other crimes that will continue and will certainly…
Read MoreEKMC: Ensemble of kNN using MetaCost for Efficient Anomaly Detection
Anomaly detection aims at identification of suspicious items, observations or events by differing from most of the data. Intrusion Detection, Fault Detection, and Fraud Detection are some of the various applications of Anomaly Detection. The Machine learning classifier algorithms used in these applications would greatly affect the overall efficiency. This work is an extension of…
Read MoreValidity and efficiency of conformal anomaly detection on big distributed data
Conformal Prediction is a recently developed framework for reliable confident predictions. In this work we discuss its possible application to big data coming from different, possibly heterogeneous data sources. On example of anomaly detection problem, we study the question of saving validity of Conformal Prediction in this case. We show that the straight forward averaging…
Read MoreDetection Method and Mitigation of Server-Spoofing Attacks on SOME/IP at the Service Discovery Phase
Service-oriented architecture has attracted attention in automotive development. The Automotive Open System Architecture (AUTOSAR) specifies Scalable Service-Oriented Middleware over IP (SOME/IP) as a key middleware for service-oriented communication in-vehicles. However, SOME/IP-based networks are vulnerable to server spoofing during the service discovery phase, enabling attackers to cause man-in-the-middle attacks by impersonating legitimate services. This paper proposes…
Read MoreHybrid Intrusion Detection Using the AEN Graph Model
The Activity and Event Network (AEN) is a new dynamic knowledge graph that models different network entities and the relationships between them. The graph is generated by processing various network security logs, such as network packets, system logs, and intrusion detection alerts, which allows the graph to capture security-relevant activity and events in the network.…
Read MoreMethod of Analysis and Classification of Acoustic Emission Signals to Identify Pre-Seismic Anomalies
A new method of analysis and classification of rock acoustic emission signals is proposed. It is based on symbol description of signals and involves the following processing. First, signal segments containing pulses are detected. Second, noise of the detected pulses is reduced by the wavelet filtration method. Fourth-order symlets and adaptive threshold scheme based on…
Read MoreStudy of the Effect of Abnormalities in the External Ear Inducing Hearing Problems
Tinnitus is a phenomenon for which the patient hears sound in the absence of any external sound source. To this day, there is no cure for this phantom sound perception. But it can be masked temporarily to help relief the patient’s pain. In order to allow this, a better understanding of the phenomenon is needed.…
Read MoreAnalysis of Local Rainfall Characteristics as a Mitigation Strategy for Hydrometeorology Disaster in Rain-fed Reservoirs Area
The Gembong reservoir in Pati Regency, Java, Indonesia is a rain-fed reservoir, which experiences a depletion of it carrying capacity. The characteristic of local rainfall is one of the important factors in assessing the potential of hydrometeorology disasters in its area. Sedimentation in watersheds and reservoirs has covered water sources, so local rainfall determines the…
Read MoreNetwork Intrusion Detection System using Apache Storm
Network security implements various strategies for the identification and prevention of security breaches. Network intrusion detection is a critical component of network management for security, quality of service and other purposes. These systems allow early detection of network intrusion and malicious activities; so that the Network Security infrastructure can react to mitigate these threats. Various…
Read MoreSemantic-less Breach Detection of Polymorphic Malware in Federated Cloud
Cloud computing is one of the largest emerging utility services that is expected to grow enormously over the next decade. Many organizations are moving into hybrid cloud/hosted computing models. Single cloud service provider introduces cost and environmental challenges. Also, multi-cloud solution implemented by the Cloud tenant is suboptimal as it requires expensive adaptation costs. Cloud…
Read MoreDynamic detection of abnormalities in video analysis of crowd behavior with DBSCAN and neural networks
Visual analysis of human behavior is a broad field within computer vision. In this field of work, we are interested in dynamic methods in the analysis of crowd behavior which consist in detecting the abnormal entities in a group in a dense scene. These scenes are characterized by the presence of a great number of…
Read More
