Advanced Evasion Techniques Research Articles

Enhancing Cloud Security Integrating Deep Learning & Data Mining: A Comprehensive Review

Cloud computing plays a pivotal role in data storage and online services, offering benefits such as easy access, on-demand storage, scalability, and cost-efficiency. Its rapidly evolving technologies can enhance the security of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) against cyber threats. Given the increasing prevalence of sophisticated malware, traditional detection methods are often inadequate due to advanced evasion techniques. This article provides a comprehensive review of cloud-based malware detection technologies and explores the use of cloud computing in safeguarding IoT and critical infrastructure. It evaluates the pros and cons of cloud environments for malware detection and introduces a deep learning and data extraction methodology for detecting cloud-based malware. Additionally, it discusses recent research on malware propagation issues and compares various detection approaches, identifying their strengths and weaknesses. The study's findings offer valuable insights for addressing current challenges in malware research.

A Review Paper on Integration of Deep Learning & Data Mining Strategies

Cloud computing plays a crucial role in data storage and online services, offering advantages such as easy access, on-demand storage, scalability, and cost efficiency. Its rapidly advancing technologies can improve the security of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) against cyber threats. With the rise of sophisticated malware, traditional detection methods are often insufficient due to advanced evasion techniques. This article presents a comprehensive review of cloud-based malware detection technologies and examines the use of cloud computing to protect IoT and critical infrastructure. It assesses the benefits and drawbacks of cloud environments for malware detection and introduces a deep learning and data extraction methodology for identifying cloud-based malware. Additionally, it discusses recent research on malware propagation issues and compares various detection methods, highlighting their strengths and weaknesses. The study's findings provide valuable insights for addressing current challenges in malware research.

A Framework for Detecting Botnet Command and Control Communication over an Encrypted Channel

Botnet employs advanced evasion techniques to avoid detection. One of the Botnet evasion techniques is by hiding their command and control communication over an encrypted channel like SSL and TLS. This paper provides a Botnet Analysis and Detection System (BADS) framework for detecting Botnet. The BADS framework has been used as a guideline to devise the methodology, and we divided this methodology into six phases: i. data collection, customization, and conversion, ii. feature extraction and feature selection, iii. Botnet prediction and classification, iv. Botnet detection, v. attack notification, and vi. testing and evaluation. We tend to use the machine learning algorithm for Botnet prediction and classification. We also found several challenges in implementing this work. This research aims to detect Botnet over an encrypted channel with high accuracy, fast detection time, and provides autonomous management to the network manager.

Malware Dynamic Analysis Evasion Techniques

The cyber world is plagued with ever-evolving malware that readily infiltrate all defense mechanisms, operate viciously unbeknownst to the user, and surreptitiously exfiltrate sensitive data. Understanding the inner workings of such malware provides a leverage to effectively combat them. This understanding is pursued often through dynamic analysis which is conducted manually or automatically. Malware authors accordingly, have devised and advanced evasion techniques to thwart or evade these analyses. In this article, we present a comprehensive survey on malware dynamic analysis evasion techniques. In addition, we propose a detailed classification of these techniques and further demonstrate how their efficacy holds against different types of detection and analysis approaches. Our observations attest that evasive behavior is mostly concerned with detecting and evading sandboxes. The primary tactic of such malware we argue is fingerprinting followed by new trends for reverse Turing test tactic which aims at detecting human interaction. Furthermore, we will posit that the current defensive strategies, beginning with reactive methods to endeavors for more transparent analysis systems, are readily foiled by zero-day fingerprinting techniques or other evasion tactics such as stalling. Accordingly, we would recommend the pursuit of more generic defensive strategies with an emphasis on path exploration techniques that has the potential to thwart all the evasive tactics.

Malware traffic classification using principal component analysis and artificial neural network for extreme surveillance

Code-driven systems have extent to more than half of the world’s populations in ambient data and connectivity, offering formerly unimagined opportunities and unexpected threats. Evolutions in Artificial Intelligence (AI) are seen increasing day by day especially in industrial builds. The unconventional technique of AI in cyber-attacks seems to be quite daunting. The idea of a machine growing its own knowledge through self-learning becomes sophisticated to attack things is a fretful problem to the cyber world. Most of the time, these AI enabled cyber-attacks are performed using advanced malwares which incorporates advanced evasion techniques to evade security perimeters. Traditional cyber security methods fail to cope with these attacks. In order to address these issues, robust traffic classification system using Principal Component Analysis (PCA) and Artificial Neural Network (ANN) is proposed for providing extreme surveillance. Further, these proposed method aims to expose various AI based cyber-attacks with their present-day impact, and their fortune in the future. Simulation is carried out using a self-developed autonomous agent which learns by itself. Experimental results confirm that the proposed schemes are efficient to classify the attack traffic with 99% of accuracy when compared to the state of the art methods.

Hybrid Analysis Technique to detect Advanced Persistent Threats

Advanced persistent threats (APT) are major threats in the field of system and network security. They are extremely stealthy and use advanced evasion techniques like packing and behaviour obfuscation to hide their malicious behaviour and evade the detection methods. Existing behavior-based detection technique fails to detect the APTs due to its high persistence mechanism and sophisticated code nature. Hence, a novel hybrid analysis technique using Behavior based Sandboxing approach is proposed. The proposed technique consists of four phases namely, Static, Dynamic, Memory and System state analysis. Initially, static analysis is performed on the sample which involves packer detection and signature verification. If the sample is found stealthy and remains undetected, then it is executed inside a sandbox environment to analyze its behavior. Further, memory analysis is performed to extract memory artefacts of the current system state. Finally, system state analysis is performed by correlating clean system state and infected system state to determine whether the system is compromised

Security Challenges and Threats for the CISO's Agenda

Today's CISO has the difficult task of balancing the security books and accommodating the economic needs of the business in complete alignment with the mission of maintaining the operational lights in secure working order. Each year, in addition to the business-as-usual security issues, CISOs must deal with many new and rapidly evolving threats and vulnerabilities. Add to this the challenge of advanced and persistent threats (APT) and the potential for cloaked surprise attacks delivered by advanced evasion techniques (AET), and the mission starts to look like a very steep one. The easiest solution would be to obtain a fully operational crystal ball that reveals every adversity well before it arrives at the front door. However, as no such tool yet exists, this article provides a forward look at the insecurities CISOs are likely to face over the next year.

Advanced evasion techniques

Much has been made in recent months of so-called Advanced Evasion Techniques (AETs) that could be used by hackers and cyber-criminals to bypass IT security defences. Do the claims stand up to technical scrutiny? Steve Gold investigates and discovers that AETs may not be as new as some experts claim. The basic techniques were outlined 12 years ago but the technology wasn't available then to exploit them. Now that we have sufficiently powerful computers to mount such attacks, they are easily thwarted by the proper implementation of intrusion detection and prevention systems – provided these are properly configured. The main danger is that budget constraints might undermine attempts at protection.

Network-level polymorphic shellcode detection using emulation

Significant progress has been made in recent years towards preventing code injection attacks at the network level. However, as state-of-the-art attack detection technology becomes more prevalent, attackers are likely to evolve, employing techniques such as polymorphism and metamorphism to defeat these defenses. A major outstanding question in security research and engineering is thus whether we can proactively develop the tools needed to contain advanced polymorphic and metamorphic attacks. While recent results have been promising, most of the existing proposals can be defeated using only minor enhancements to the attack vector. In fact, some publicly-available polymorphic shellcode engines are currently one step ahead of the most advanced publicly-documented network-level detectors. In this paper, we present a heuristic detection method that scans network traffic streams for the presence of previously unknown polymorphic shellcode. In contrast to previous work, our approach relies on a NIDS- embedded CPU emulator that executes every potential instruction sequence in the inspected traffic, aiming to identify the execution behavior of polymorphic shellcode. Our analysis demonstrates that the proposed approach is more robust to obfuscation techniques like self-modifications compared to previous proposals, but also highlights advanced evasion techniques that need to be more closely examined towards a satisfactory solution to the polymorphic shellcode detection problem.