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We propose a novel way to analyze malware: focus closely on the malware's external (i. e., network) activity.
Practical analysis shows that such malware-providing ASs prevent themselves from being de-peered by hiding behind other ASs, which do not host the malware themselves but simply provide transit service for malware.
Proceedings ArticleDOI
09 Nov 2010
14 Citations
Our investigation revealed that anti-virus software fail to detect many malware files, and that traffic patterns to web honeypots are useful for detecting malware files on websites.
To address the sheer volume of malware and diversity of its behavior, we provide a method for automatically categorizing these profiles of malware into groups that reflect similar classes of behaviors and demonstrate how behavior-based clustering provides a more direct and effective way of classifying and analyzing Internet malware.
Accurate analyses of malware must be done by detecting them in initial stage in an automatic way to avoid severe damage in Internet of Thing devices.
Educating the internet users about malware attack, as well as the implementation and proper application of anti-malware tools, are critical steps in protecting the
Using this observation, we present a novel method for detection of malware using the correlation between the semantics of the malware and its API calls.
Malware analysis is important, since many malware at this day which is not detectable by antivirus.
Since malware infections inside an enterprise spread primarily via malware domain accesses, our approach can be used to detect and prevent malware infections.
Open accessJournal ArticleDOI
Sapna Malik, Kiran Khatter 
29 Citations
The system call tracing is an effective dynamic analysis technique for detecting malware as it can analyze the malware at the run time.

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How does the use of cloud computing affect the security posture of organizations?
5 answers
The use of cloud computing significantly impacts the security posture of organizations by introducing new challenges and opportunities. Cloud computing offers cost-effective and scalable processing, but it also raises concerns about data breaches, malware, and cyber threats as sensitive data is moved to cloud-based infrastructure managed by third parties. Organizations adopting cloud services must implement strong security measures, such as secure coding practices, vulnerability assessments, and penetration testing, to protect their applications and data throughout the software development lifecycle. Additionally, the integration of software-defined networking (SDN) in cloud environments can enhance network management flexibility and lower operating costs, but proper defensive architectures are crucial to mitigate distributed denial-of-service (DDoS) attacks. By understanding these challenges and leveraging security frameworks like NIST 800-53 and Cloud Security Alliance Cloud Controls Matrix, organizations can enhance their security posture and effectively manage risks in the cloud.
How to use large language models to achieve embodied intelligence?
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To achieve embodied intelligence using large language models (LLMs), researchers have proposed innovative frameworks. One such approach involves leveraging LLMs for grounded planning with physical scene constraints, enabling agents to generate executable plans aligned with visual perception models. Additionally, recent studies have explored the use of LLMs for multi-agent cooperation, showcasing their ability to surpass traditional planning methods and facilitate effective communication without extensive fine-tuning. These advancements highlight the potential of LLMs in enabling agents to plan, communicate, and cooperate efficiently in complex environments, ultimately paving the way for enhanced embodied artificial intelligence capabilities.
What are the predicted types of attacks that will target next-generation computer networks?
5 answers
Predicted types of attacks targeting next-generation computer networks include Distributed Denial of Service (DOS), Remote-to-Local (R2L), User-to-Root (U2R), Probe, and large assaults, as highlighted in the study focusing on enhancing packet connection transfers using machine learning algorithms. Additionally, the importance of localization security in integrated communication and localization systems is emphasized due to the increasing impact of localization systems in daily life and the reliance on location information for products and services, necessitating advanced security solutions against evolving threats. Furthermore, the use of Artificial Dust (AD) and Half-Duplex (HD) attacks in wireless communication networks is discussed, showcasing the vulnerability of secure networks to specific targeted users and the efficacy of the proposed attack mechanisms compared to conventional methods.
What is Feature engineering?
5 answers
Feature engineering is a crucial step in machine learning projects, involving the preparation of raw data for algorithmic analysis. It encompasses various processes like encoding variables, handling outliers and missing values, binning, and transforming variables. Feature engineering methods include creating, expanding, and selecting features to enhance data quality, ultimately improving model accuracy. In the context of malware detection, a novel feature engineering technique integrates layout information with structural entropy to enhance accuracy and F1-score in malware detection models. Automated Feature Engineering (AFE) automates the generation and selection of optimal feature sets for tasks, with recent advancements focusing on improving feature effectiveness and efficiency through reinforcement learning-based frameworks.
What are the most effective reconnaissance techniques used by advanced persistent threats?
4 answers
Advanced Persistent Threats (APTs) employ various reconnaissance techniques to gather information for targeted cyber attacks. These techniques include network scanning, image steganography, behavior obfuscation, and evasion tactics like packing. APTs focus on stealth and long-term infiltration, utilizing phases like reconnaissance, delivery, initial intrusion, command and control, lateral movement, and data exfiltration. To counter these threats, cyber deception systems are developed to deceive adversaries by simulating virtual topologies, delaying scanning techniques, and invalidating collected information. Additionally, a model is proposed to understand how adversaries acquire knowledge about target networks and expand their foothold, guiding the development of defensive capabilities like high-interaction honeypots to influence adversary behavior. Understanding these reconnaissance methods is crucial for enhancing defensive strategies against APTs.
Is detecting a wrong attack worse than not detecting any attack when there is an attack ?
5 answers
Detecting a wrong attack can be more detrimental than not detecting any attack when there is an actual attack present. While some focus on correcting classification errors caused by attacks, it is argued that detecting attacks is operationally more crucial. Various attack types, such as denial-of-service (DoS), replay, and false-data-injection (FDI) attacks, can be detected simultaneously using set-membership estimation methods. Additionally, the worst-case detection performance of physical layer authentication schemes against multiple-antenna impersonation attacks highlights the importance of accurate attack detection. Implementing effective countermeasures upon attack detection is crucial to mitigate the impact of malicious attacks. Therefore, prioritizing accurate attack detection over misclassification is essential for maintaining the security and integrity of cyber-physical systems and networks.
Is not detecting any attack worse than detecting a wrong attack when there is an attack ?
5 answers
Detecting any attack, even if incorrectly, is generally preferable to not detecting any attack at all. This is because failing to detect an attack leaves the system vulnerable to potential harm or compromise. Various methods have been proposed to enhance attack detection, such as using intelligent intrusion detection systems in IoT networks and smart cities. These systems leverage deep learning and machine learning techniques to identify and mitigate attacks, including DDoS attacks. Additionally, research has focused on developing algorithms to detect false data injection attacks, Byzantine attacks, and switching attacks in distributed control systems. Novel strategies like encoding-decoding and countermeasures have been introduced to improve attack detection rates in nonlinear cyber-physical systems. Overall, prioritizing the detection of attacks, even with the risk of false positives, is crucial for maintaining system security and integrity.
How did rosenzweig 1978 introduced enriched environmet into practice?
10 answers
The concept of an enriched environment (EE) as introduced by Rosenzweig in 1978 has significantly evolved and expanded in practice across various fields of research, particularly in understanding and enhancing brain function and recovery from neurological injuries. While Rosenzweig's foundational work laid the groundwork, subsequent studies have built upon and diversified the application of EE in experimental settings. EE involves modifications to the living conditions of laboratory animals to improve their biological conditions, promoting transcriptional and translational effects that enhance cellular plasticity and cognitive performance. This paradigm has been shown to ameliorate motor, sensory, and cognitive stimulation in animals, compared with those housed under standard conditions. The principle behind EE is to provide a combination of increased social interaction, physical activity, spatial complexity, and novelty, which collectively contribute to its beneficial effects on learning, memory, and recovery of function after brain injury. Research has demonstrated that EE can mitigate the effects of stress across generations by enhancing neurogenesis in the hippocampus, indicating its profound impact on cognitive development and resilience. In stroke recovery, EE, through multimodal stimulation including social interactions and sensorimotor stimulation, has shown promise in improving lost neurological function without affecting the extent of brain damage. This approach has also been explored in the context of behavioral epigenetics, where EE is used to correct aberrant epigenetic effects of stress, highlighting the intricate relationship between biological and biographical events in the understanding of stress. Moreover, EE's role extends beyond neurological and cognitive benefits. It has been found to have therapeutic potential in metabolic disorders, such as obesity, by restoring energy balance and improving metabolic alterations through enhanced central nervous system activity and reduced inflammation. These diverse applications underscore the multifaceted benefits of EE, rooted in the pioneering work of Rosenzweig, and its ongoing relevance in contemporary research across neurology, psychiatry, and beyond.
What is explainable ai?
10 answers
Explainable AI (XAI) refers to the subset of artificial intelligence technologies and methodologies that are designed to make the decision-making processes of AI models transparent and understandable to humans. The primary goal of XAI is to bridge the gap between the high-level accuracy of AI models and the need for human interpretability, ensuring that the internal mechanics behind AI decisions can be explained in a comprehensible manner. This is particularly important in fields where AI's decisions have significant implications, such as healthcare, finance, and autonomous driving, where understanding the rationale behind an AI's decision is crucial for trust and accountability. The interest in XAI has surged due to its critical role in various application domains, where the need for transparency and interpretability cannot be overstated. Unlike traditional "black box" AI models, where the decision-making process is opaque and not easily understood, XAI aims to produce models that not only deliver accurate results but also provide insights into their reasoning process. This involves developing AI systems that can explain their decisions in a manner that is interpretable or understandable to humans, addressing key challenges in implementing AI ethics in practice. Research in XAI encompasses techniques for creating interpretable models, such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), which aim to provide clarity on how AI models arrive at their decisions. Moreover, XAI includes efforts to develop machine learning classification models based on expressive Boolean formulas, offering a balance between interpretability and complexity, and potentially enabling applications in areas like credit scoring and medical diagnosis. Furthermore, XAI is not limited to enhancing the transparency of AI models but also extends to improving the trust and reliability of AI/ML solutions by providing evidence-based explanations for their predictions, thereby addressing the issue of trust in adopting AI solutions. By applying XAI techniques to various domains, such as Android malware detection, researchers aim to demystify the decision-making processes of machine learning models, thereby fostering a greater understanding and acceptance of AI technologies. In summary, Explainable AI represents a crucial advancement in making AI technologies more transparent, accountable, and trustworthy, by ensuring that the rationale behind AI decisions is accessible and understandable to end-users and stakeholders across various fields.
What are some potential applications of CN2 induction algorithm?
4 answers
The CN2 induction algorithm has various potential applications. It is utilized in rule induction, where formal rules are extracted from observations. CN2 can be adapted to handle missing values directly, enhancing performance compared to traditional data imputation methods. Moreover, CN2 has been employed in identifying cybersickness severity levels using EEG signals, providing a rules-based approach for accurate diagnosis and treatment. Additionally, CN2 has been extended to CN2e, which automatically encodes rules into C language, offering a fast and secure way to translate rules for execution. Furthermore, CN2 has been modified for subgroup discovery tasks, improving rule coverage, significance, and accuracy in various datasets. These diverse applications showcase the versatility and effectiveness of the CN2 induction algorithm in different domains.
Who planned the operation citadel?
5 answers
Operation Citadel was planned by the intelligence branch of the German army's General Staff, known as Foreign Armies East. This planning involved processing intelligence information from special services and preparing reports for the Supreme Command and Adolf Hitler during the conceptual development of the operation. The Citadel operation was a key military campaign in the summer of 1943. The design and construction of citadels, like the one in Pamplona, required meticulous planning and the delineation of numerous drawings by military engineers. Additionally, the Citadel malware, known for targeting financial information, was the subject of a joint operation by the FBI and the Microsoft Digital Crimes Unit to disrupt its command-and-control servers.